This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method.
Enhanced target tracking based on mean shift algorithm for satellite imageryeSAT Journals
Abstract Target tracking in high resolution satellite images is challenging task for computer vision field. In this paper we have proposed a mean shift algorithm based enhanced target tracking system for high resolution satellite imagery. In proposed tracking algorithm, Target modeling is done using spectral features of target object i.e. Mean & Energy density function. Feature Vector space with minimum Euclidean Distance is used for predicting next possible position of target object in consecutive frames. Proposed tracking algorithm has been tested using two high resolution databases i.e. Harbor & Airport region database acquired by WorldView-2 satellite at different times. Recall, Precision & F1 score etc. performance parameters are also calculated for showing the tracking ability of the proposed method in real-time applications and are compared with the results of Regional Operator Design based tracking algorithm proposed in [1]. The results show that our proposed method gives relatively better performance than the other tracking algorithms used in satellite imagery. Keywords- Target tracking, Mean shift algorithm, Energy density function, Feature Vector Space, Frame
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...csandit
3-dimensional object modelling of real world objects in steady state by means of multiple point
cloud (pcl) depth scans taken by using sensing camera and application of smoothing algorithm
are suggested in this study. Polygon structure, which is constituted by coordinates of point
cloud (x,y,z) corresponding to the position of 3D model in space and obtained by nodal points
and connection of these points by means of triangulation, is utilized for the demonstration of 3D
models. Gaussian smoothing and developed methods are applied to the mesh consisting of
merge of these polygons, and a new mesh simplification and augmentation algorithm are
suggested for the over the 3D modelling. Mesh consisting of merge of polygons can be
demonstrated in a more packed, smooth and fluent way. In this study is shown that applied the
triangulation and smoothing method for 3D modelling, perform to a fast and robust mesh
structures compared to existing methods therewithal no remeshing is necessary for refinement
and reduction.
Enhanced target tracking based on mean shift algorithm for satellite imageryeSAT Journals
Abstract Target tracking in high resolution satellite images is challenging task for computer vision field. In this paper we have proposed a mean shift algorithm based enhanced target tracking system for high resolution satellite imagery. In proposed tracking algorithm, Target modeling is done using spectral features of target object i.e. Mean & Energy density function. Feature Vector space with minimum Euclidean Distance is used for predicting next possible position of target object in consecutive frames. Proposed tracking algorithm has been tested using two high resolution databases i.e. Harbor & Airport region database acquired by WorldView-2 satellite at different times. Recall, Precision & F1 score etc. performance parameters are also calculated for showing the tracking ability of the proposed method in real-time applications and are compared with the results of Regional Operator Design based tracking algorithm proposed in [1]. The results show that our proposed method gives relatively better performance than the other tracking algorithms used in satellite imagery. Keywords- Target tracking, Mean shift algorithm, Energy density function, Feature Vector Space, Frame
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...csandit
3-dimensional object modelling of real world objects in steady state by means of multiple point
cloud (pcl) depth scans taken by using sensing camera and application of smoothing algorithm
are suggested in this study. Polygon structure, which is constituted by coordinates of point
cloud (x,y,z) corresponding to the position of 3D model in space and obtained by nodal points
and connection of these points by means of triangulation, is utilized for the demonstration of 3D
models. Gaussian smoothing and developed methods are applied to the mesh consisting of
merge of these polygons, and a new mesh simplification and augmentation algorithm are
suggested for the over the 3D modelling. Mesh consisting of merge of polygons can be
demonstrated in a more packed, smooth and fluent way. In this study is shown that applied the
triangulation and smoothing method for 3D modelling, perform to a fast and robust mesh
structures compared to existing methods therewithal no remeshing is necessary for refinement
and reduction.
Image confusion and diffusion based on multi-chaotic system and mix-columnjournalBEEI
In this paper, a new image encryption algorithm based on chaotic cryptography was proposed. The proposed scheme was based on multiple stages of confusion and diffusion. The diffusion process was implemented twice, first, by permuting the pixels of the plain image by using an Arnold cat map and, the second time by permuting the plain image pixels via the proposed permutation algorithm. The confusion process was performed many times, by performing the XOR operation between the two resulted from permuted images, subtracted a random value from all pixels of the image, as well as by implementing the mix column on the resulted image, and by used the Lorenz key to obtain the encrypted image. The security analysis tests that used to exam the results of this encryption method were information entropy, key space analysis, correlation, histogram analysis UACI, and NPCR have shown that the suggested algorithm has been resistant against different types of attacks.
Fast Full Search for Block Matching Algorithmsijsrd.com
This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). Contemporary motion estimation architectures are either too rigid for multiple BMAs or the flexibility in them is implemented at the cost of reduced performance. In block-based motion estimation, a block-matching algorithm (BMA) searches for the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Integration of poses to enhance the shape of the object tracking from a singl...eSAT Journals
Abstract In computer vision, tracking human pose has received a growing attention in recent years. The existing methods used multi-view videos and camera calibrations to enhance the shape of the object in 3D view. In this paper, tracking and partial reconstruction of the shape of the object from a single view video is identified. The goal of the proposed integrated method is to detect the movement of a person more accurately in 2D view. The integrated method is a combination of Silhouette based pose estimation and Scene flow based pose estimation. The silhouette based pose estimation is used to enhance the shape of the object for 3D reconstruction and scene flow based pose estimation is used to capture the size as well as the stability of the object. By integrating these two poses, the accurate shape of the object has been calculated from a single view video. Keywords: Pose Estimation, optical Flow, Silhouette, Object Reconstruction, 3D Objects
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
An enhanced difference pair mapping steganography method to improve embedding...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Median based parallel steering kernel regression for image reconstructioncsandit
Image reconstruction is a process of obtaining the original image from corrupted data.
Applications of image reconstruction include Computer Tomography, radar imaging, weather
forecasting etc. Recently steering kernel regression method has been applied for image
reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is
computationally intensive. Secondly, output of the algorithm suffers form spurious edges
(especially in case of denoising). We propose a modified version of Steering Kernel Regression
called as Median Based Parallel Steering Kernel Regression Technique. In the proposed
algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The
second problem is addressed by a gradient based suppression in which median filter is used.
Our algorithm gives better output than that of the Steering Kernel Regression. The results are
compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of
21x using GPUs and shown speedup of 6x using multi-cores.
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTIONcsandit
Image reconstruction is a process of obtaining the original image from corrupted data.Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges(especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used.Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
An enhanced fireworks algorithm to generate prime key for multiple users in f...journalBEEI
This work presents a new method to enhance the performance of fireworks algorithm to generate a prime key for multiple users. A threshold technique in image segmentation is used as one of the major steps. It is used processing the digital image. Some useful algorithms and methods for dividing and sharing an image, including measuring, recognizing, and recognizing, are common. In this research, we proposed a hybrid technique of fireworks and camel herd algorithms (HFCA), where Fireworks are based on 3-dimension (3D) logistic chaotic maps. Both, the Otsu method and the convolution technique are used in the pre-processing image for further analysis. The Otsu is employed to segment the image and find the threshold for each image, and convolution is used to extract the features of the used images. The sample of the images consists of two images of fingerprints taken from the Biometric System Lab (University of Bologna). The performance of the anticipated method is evaluated by using FVC2004 dataset. The results of the work enhanced algorithm, so quick response code (QRcode) is used to generate a stream key by using random text or number, which is a class of symmetric-key algorithm that operates on individual bits or bytes.
cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs : NOTESSubhajit Sahu
Highlighted notes on cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs (compressed).
While doing research work under Prof. Kishore Kothapalli.
These people wrote the first data structure for maintaining dynamic graphs with NVIDIA CUDA GPUs. Unlike STINGER which uses a block linked-list and GT-STINGER which uses Array of Structures (AoS), here they instead used Structure of Arrays (SoA) which is not only more suitable to GPU memory access, but also allows smaller allocations modes (memory is a premium in GPU).
They use a custom memory manager which speeds up memory management (instead of using system memory manager). The data structure used is the standard adjacency list (CSR) and pointers are updated when edge list has to grow. It could handle
upto 10 million updates per second (large batch sizes), and ran a static graph algorithm (triangle counting) 1-10% slower. This shows that cuSTINGER can also be used with static graph algorithms. Dynamic graph algorithms should run much faster.
Expert system of single magnetic lens using JESS in Focused Ion Beamijcsa
This work shows expert system of symmetrical single magnetic lens used in focused ion beam optical system. Java expert system shell(JESS) programming is proposed to build the intelligent agent "MOPTION"for getting an optimum magnetic flux density , and calculate the ion optical trajectory. The combination of such rule based engine and SIMION 8.1 has configured the reconstruction process and compiled the data retrieved by the proposed expert system agent to implement the pole-pieces reconstruction for lens design. The pole pieces reconstruction has been resulted in 3D graph , and under the infinite magnification conditions of the optical path, aberration (spherical / chromatic and total) disks diameters have been obtained and got the values (0.03,0.13 and 0.133) micron (μm) respectively.
cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs (NOTES)Subhajit Sahu
Highlighted notes on cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs.
While doing research work under Prof. Kishore Kothapalli.
These people wrote the first data structure for maintaining dynamic graphs with NVIDIA CUDA GPUs. Unlike STINGER which uses a block linked-list and GT-STINGER which uses Array of Structures (AoS), here they instead used Structure of Arrays (SoA) which is not only more suitable to GPU memory access, but also allows smaller allocations modes (memory is a premium in GPU).
They use a custom memory manager which speeds up memory management (instead of using system memory manager). The data structure used is the standard adjacency list (CSR) and pointers are updated when edge list has to grow. It could handle
upto 10 million updates per second (large batch sizes), and ran a static graph algorithm (triangle counting) 1-10% slower. This shows that cuSTINGER can also be used with static graph algorithms. Dynamic graph algorithms should run much faster.
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Stereo matching algorithm using census transform and segment tree for depth e...TELKOMNIKA JOURNAL
This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
Image confusion and diffusion based on multi-chaotic system and mix-columnjournalBEEI
In this paper, a new image encryption algorithm based on chaotic cryptography was proposed. The proposed scheme was based on multiple stages of confusion and diffusion. The diffusion process was implemented twice, first, by permuting the pixels of the plain image by using an Arnold cat map and, the second time by permuting the plain image pixels via the proposed permutation algorithm. The confusion process was performed many times, by performing the XOR operation between the two resulted from permuted images, subtracted a random value from all pixels of the image, as well as by implementing the mix column on the resulted image, and by used the Lorenz key to obtain the encrypted image. The security analysis tests that used to exam the results of this encryption method were information entropy, key space analysis, correlation, histogram analysis UACI, and NPCR have shown that the suggested algorithm has been resistant against different types of attacks.
Fast Full Search for Block Matching Algorithmsijsrd.com
This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). Contemporary motion estimation architectures are either too rigid for multiple BMAs or the flexibility in them is implemented at the cost of reduced performance. In block-based motion estimation, a block-matching algorithm (BMA) searches for the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Integration of poses to enhance the shape of the object tracking from a singl...eSAT Journals
Abstract In computer vision, tracking human pose has received a growing attention in recent years. The existing methods used multi-view videos and camera calibrations to enhance the shape of the object in 3D view. In this paper, tracking and partial reconstruction of the shape of the object from a single view video is identified. The goal of the proposed integrated method is to detect the movement of a person more accurately in 2D view. The integrated method is a combination of Silhouette based pose estimation and Scene flow based pose estimation. The silhouette based pose estimation is used to enhance the shape of the object for 3D reconstruction and scene flow based pose estimation is used to capture the size as well as the stability of the object. By integrating these two poses, the accurate shape of the object has been calculated from a single view video. Keywords: Pose Estimation, optical Flow, Silhouette, Object Reconstruction, 3D Objects
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
An enhanced difference pair mapping steganography method to improve embedding...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Median based parallel steering kernel regression for image reconstructioncsandit
Image reconstruction is a process of obtaining the original image from corrupted data.
Applications of image reconstruction include Computer Tomography, radar imaging, weather
forecasting etc. Recently steering kernel regression method has been applied for image
reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is
computationally intensive. Secondly, output of the algorithm suffers form spurious edges
(especially in case of denoising). We propose a modified version of Steering Kernel Regression
called as Median Based Parallel Steering Kernel Regression Technique. In the proposed
algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The
second problem is addressed by a gradient based suppression in which median filter is used.
Our algorithm gives better output than that of the Steering Kernel Regression. The results are
compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of
21x using GPUs and shown speedup of 6x using multi-cores.
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTIONcsandit
Image reconstruction is a process of obtaining the original image from corrupted data.Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges(especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used.Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
An enhanced fireworks algorithm to generate prime key for multiple users in f...journalBEEI
This work presents a new method to enhance the performance of fireworks algorithm to generate a prime key for multiple users. A threshold technique in image segmentation is used as one of the major steps. It is used processing the digital image. Some useful algorithms and methods for dividing and sharing an image, including measuring, recognizing, and recognizing, are common. In this research, we proposed a hybrid technique of fireworks and camel herd algorithms (HFCA), where Fireworks are based on 3-dimension (3D) logistic chaotic maps. Both, the Otsu method and the convolution technique are used in the pre-processing image for further analysis. The Otsu is employed to segment the image and find the threshold for each image, and convolution is used to extract the features of the used images. The sample of the images consists of two images of fingerprints taken from the Biometric System Lab (University of Bologna). The performance of the anticipated method is evaluated by using FVC2004 dataset. The results of the work enhanced algorithm, so quick response code (QRcode) is used to generate a stream key by using random text or number, which is a class of symmetric-key algorithm that operates on individual bits or bytes.
cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs : NOTESSubhajit Sahu
Highlighted notes on cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs (compressed).
While doing research work under Prof. Kishore Kothapalli.
These people wrote the first data structure for maintaining dynamic graphs with NVIDIA CUDA GPUs. Unlike STINGER which uses a block linked-list and GT-STINGER which uses Array of Structures (AoS), here they instead used Structure of Arrays (SoA) which is not only more suitable to GPU memory access, but also allows smaller allocations modes (memory is a premium in GPU).
They use a custom memory manager which speeds up memory management (instead of using system memory manager). The data structure used is the standard adjacency list (CSR) and pointers are updated when edge list has to grow. It could handle
upto 10 million updates per second (large batch sizes), and ran a static graph algorithm (triangle counting) 1-10% slower. This shows that cuSTINGER can also be used with static graph algorithms. Dynamic graph algorithms should run much faster.
Expert system of single magnetic lens using JESS in Focused Ion Beamijcsa
This work shows expert system of symmetrical single magnetic lens used in focused ion beam optical system. Java expert system shell(JESS) programming is proposed to build the intelligent agent "MOPTION"for getting an optimum magnetic flux density , and calculate the ion optical trajectory. The combination of such rule based engine and SIMION 8.1 has configured the reconstruction process and compiled the data retrieved by the proposed expert system agent to implement the pole-pieces reconstruction for lens design. The pole pieces reconstruction has been resulted in 3D graph , and under the infinite magnification conditions of the optical path, aberration (spherical / chromatic and total) disks diameters have been obtained and got the values (0.03,0.13 and 0.133) micron (μm) respectively.
cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs (NOTES)Subhajit Sahu
Highlighted notes on cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs.
While doing research work under Prof. Kishore Kothapalli.
These people wrote the first data structure for maintaining dynamic graphs with NVIDIA CUDA GPUs. Unlike STINGER which uses a block linked-list and GT-STINGER which uses Array of Structures (AoS), here they instead used Structure of Arrays (SoA) which is not only more suitable to GPU memory access, but also allows smaller allocations modes (memory is a premium in GPU).
They use a custom memory manager which speeds up memory management (instead of using system memory manager). The data structure used is the standard adjacency list (CSR) and pointers are updated when edge list has to grow. It could handle
upto 10 million updates per second (large batch sizes), and ran a static graph algorithm (triangle counting) 1-10% slower. This shows that cuSTINGER can also be used with static graph algorithms. Dynamic graph algorithms should run much faster.
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Stereo matching algorithm using census transform and segment tree for depth e...TELKOMNIKA JOURNAL
This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...cscpconf
3-dimensional object modelling of real world objects in steady state by means of multiple point cloud (pcl) depth scans taken by using sensing camera and application of smoothing algorithm
are suggested in this study. Polygon structure, which is constituted by coordinates of point cloud (x,y,z) corresponding to the position of 3D model in space and obtained by nodal points and connection of these points by means of triangulation, is utilized for the demonstration of 3D models. Gaussian smoothing and developed methods are applied to the mesh consisting of merge of these polygons, and a new mesh simplification and augmentation algorithm are suggested for the over the 3D modelling. Mesh consisting of merge of polygons can be demonstrated in a more packed, smooth and fluent way. In this study is shown that applied the triangulation and smoothing method for 3D modelling, perform to a fast and robust mesh structures compared to existing methods therewithal no remeshing is necessary for refinement and reduction.
Robust foreground modelling to segment and detect multiple moving objects in ...IJECEIAES
Last decade has witnessed an ever increasing number of video surveillance installa- tions due to the rise of security concerns worldwide. With this comes the need for video analysis for fraud detection, crime investigation, traffic monitoring to name a few. For any kind of video analysis application, detection of moving objects in videos is a fundamental step. In this paper, an efficient foreground modelling method to segment multiple moving objects is implemented. Proposed method significantly reduces noise thereby accurately segmenting region of interest under dynamic conditions while handling occlusion to a large extent. Extensive performance analysis shows that the proposed method was found to give far better results when compared to the de facto standard as well as relatively new approaches used for moving object detection.
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs), where application developers can extract data from sensor nodes through a high level abstraction of the system. Instead of developing the entire application, task graph representation of the WSN model presents simplified approach of data collection. However, mapping of tasks onto sensor nodes highlights several problems in energy consumption and routing delay. In this paper, we present an efficient hybrid approach of task mapping for WSN – Hybrid Genetic Algorithm, considering multiple objectives of optimization – energy consumption, routing delay and soft real time requirement. We also present a method to configure the algorithm as per user's need by changing the heuristics used for optimization. The trade-off analysis between energy consumption and delivery delay was performed and simulation results are presented. The algorithm is applicable during macro-programming enabling developers to choose a better mapping according to their application requirements.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs),
where application developers can extract data from sensor nodes through a high level abstraction of the
system. Instead of developing the entire application, task graph representation of the WSN model presents
simplified approach of data collection.
An efficient reconfigurable optimal source detection and beam allocation algo...IJECEIAES
Now a days, huge amount of data is communicated through channels in wireless network. It requires an efficient parallel operation for the optimal utilization of frequency, time allocation and coding model for signal subspace factorization in smart antenna. In view of this requirement, an efficient reconfigurable optimal source detection and beam allocation algorithm (RoSDBA) is proposed. The proposed algorithm is able to allocate desired signal to the user space to reduce the noise and also for efficient allocation of subspace to remove disturbance in all directions. The proposed method efficiently utilizes the antenna array elements by accurate identification and allocation of antenna array elements such as individual radiators, radiation beam, signal strength, and disturbance factor. With respect to simulation analysis, the proposed method shows better performance for the resolution, radiation beam allocations, identification bias, distribution factor and time taken for the detection of various array arrangements and source numbers.
A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTIONcscpconf
Image reconstruction is a process of obtaining the original image from corrupted data. Applications of image reconstruction include Computer Tomography, radar imaging, weather
forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges (especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used. Our algorithm gives better output than that of the Steering Kernel Regression. The results are
compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
A new function of stereo matching algorithm based on hybrid convolutional neu...IJEECSIAES
This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
A new function of stereo matching algorithm based on hybrid convolutional neu...nooriasukmaningtyas
This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
Comparative study of optimization algorithms on convolutional network for aut...IJECEIAES
The last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications.
Deep learning, and in particular convolutional networks have become a fundamental
tool in the solution of problems related to environment identification, path planning,
vehicle behavior, and motion control. In this paper, we perform a comparative study of
the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the
development of an intelligent sensor. This sensor, part of our research in reactive
systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The
optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated
adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The
training of the deep model is evaluated in terms of convergence, accuracy, recall, and
F1-score metrics. Preliminary results show a better performance of the deep network
when using the SGD function as an optimizer, while the Ftrl function presents the
poorest performances.
Parallel field programmable gate array implementation of the sum of absolute ...TELKOMNIKA JOURNAL
Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640×480 resolution color images.
Similar to Stereo vision-based obstacle avoidance module on 3D point cloud data (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Student information management system project report ii.pdfKamal Acharya
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A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Stereo vision-based obstacle avoidance module on 3D point cloud data
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 3, June 2020, pp. 1514~1521
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i3.14829 1514
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Stereo vision-based obstacle avoidance
module on 3D point cloud data
Eko Purbo Wahyono1
, Endah Suryawati Ningrum2
, Raden Sanggar Dewanto3
, Dadet Pramadihanto4
1
Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
2
Department of Mechanical and Energy Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
3
Department of Information Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
4
Robotics and Intelligent System Center, Indonesia
Article Info ABSTRACT
Article history:
Received Aug 23, 2019
Revised Jan 29, 2020
Accepted Feb 26, 2020
This paper deals in building a 3D vision-based obstacle avoidance and
navigation. In order for an autonomous system to work in real life condition,
a capability of gaining surrounding environment data, interpret the data and
take appropriate action is needed. One of the required capability in this matter
for an autonomous system is a capability to navigate cluttered, unorganized
environment and avoiding collision with any present obstacle, defined as any
data with vertical orientation and able to take decision when environment
update exist. Proposed in this work are two-step strategy of extracting
the obstacle position and orientation from point cloud data using plane based
segmentation and the resultant segmentation are mapped based on obstacle
point position relative to camera using occupancy grid map to acquire obstacle
cluster position and recorded the occupancy grid map for future use and global
navigation, obstacle position gained in grid map is used to plan the navigation
path towards target goal without going through obstacle position and modify
the navigation path to avoid collision when environment update is present or
platform movement is not aligned with navigation path based on timed elastic
band method.
Keywords:
3D Vision
Obstacle avoidance
Obstacle extraction
Timed elastic band
This is an open access article under the CC BY-SA license.
Corresponding Author:
Eko Purbo Wahyono,
Department of Electrical Engineering,
Politeknik Eplektronika Negeri Surabaya,
Kampus PENS, Jl. Raya ITS, Keputih, Kec. Sukolilo, Kota SBY, Jawa Timur 60111, Surabaya, Indonesia.
Email: eko.pw00@gmail.com
1. INTRODUCTION
Recent development on autonomous platform geared towards assisting human in their daily activity
mandates all autonomous platform to have a capability to perceive the surrounding environment, and make a
suitable decision for action. One essential action is to safely navigate cluttered environment and avoiding
collision with any obstacle that presents within the environment. One challenge present is avoiding collision
with obstacle and navigating in an unstructured environment [1]. Since unstructured environment have so many
possibilities of obstacle position, orientation and or shape, challenge present involved in gaining obstacle
position, orientation and size in order to safely plan a path to avoid a collision. One commonly used solution
to deal with these problems is 3D vision [2, 3] based solution to gain obstacle position, orientation and size
since with 3D vision the system did not need to recognize and learn the object to acquire the information on
object position, orientation and size since the raw data itself is already 3D. Many solutions exist on the field of
gaining 3D information from the environment, one of the solution is using computer stereo vision [4–6] to
2. TELKOMNIKA Telecommun Comput El Control
Stereo vision-based obstacle avoidance module on 3D point cloud data (Eko Purbo Wahyono)
1515
create an point cloud data [7]. On solving the problems on 3D vision and obstacle avoidance, as the system
become progressively more complex and demanding with processing power, the demands for more details on
input on environment and faster execution time become more clear. From the input processing side, with 3D
vision that create a 3D representation of surrounding environment with great details comes with the price of
enormous data size and demand more processing power to process the data. One solution to this problem is to
reduce data size while keeping the characteristics, normally this result in reduced environment details.
RANSAC (random sample consensus) based method with SAC model [8] is one of the most used method
used to reduce data size without sacrificing too much details on environment data and robust in presence of
noise [9], This work uses NaN, passthrough and voxel grid filter based on RANSAC to reduce data size. To
further reduce computation, parallel computing are used [9–11]. Based from [9] comparison this work used
CUDA (compute unified device architecture) parallel computing based on [12]. The reason for parallel
computing is to minimize execution time in order to enable the obstacle avoidance module to be in a processing
unit combined with another function for platform movement and automation.
Environment data must be classified into regions with similar characteristic to facilitate further
processing. One of data classifying is data segmentation. This process was required to differentiate data to
acquire usable environment data [13-16]. This work using plane-based segmentation to differentiate between
obstacle which defined as any point cloud data with vertical plane orientation and the horizontal walking plane
itself. This step is used to segment the walking plane and obstacle data.Obstacle data, a representation of
environment condition are used to plan the collisionfree path. Since the platform movement lies on x,y axis,
the obstacle data in form of point cloud can be converted to grid map [17, 18]. Recent research produce an
algorithm that not only able to plan safe path but also deal with environment change and platform disturbances
that cause the platform to stray from planned path. One of the algorithm is timed-elastic-band [19]. Many
research has been done on improving TEB (timed elastic band) method [20–24], this work base the TEB
application from research by [20, 22]. The final goal of this work is a system module with integrated processing
for 3D point cloud data similar to [25] with an output of obstacle detection and obstacle position information
to produce path planning with an ability of updating the path to deal with environment changes and or platform
deviation while record the environment data for future usage and subsequent global path planning.
2. PROPOSED SYSTEM OVERVIEW
Below are the general overview of steps taken to process environment data to plan the obstacle
avoidance action:
− Pre-Processing. Raw point cloud data from a stereo camera are filtered to down sample the data using voxel
grid method and remove unnecessary points.
− Obstacle extraction. Environment data in form of point cloud are separated between vertical and horizontal
plane based on point data orientation, environment interpreted into a grid to get the information of safe
moving space and obstacle area, the resultant grid are recorded for future use and global navigation.
− Path planning. Plan a path towards goal through safe moving area using occupancy map, keeping clear of
obstacles area while updating environment grid and adapt the path when environment data and platform
position are changed, publish array of waypoints along the global path and create new waypoints array
towards global path target.
3. RESEARCH METHOD
3.1. Point cloud engine
On camera initialization, camera calibration and pre-set parameters are loaded to enable image
acquisition. Data taken from Stereo 2D camera with focal point 𝑓 and baseline 𝐽 are stereo matched,
and triangulated to gain depth data by:
𝑑 =
(𝐽 ∗ 𝑓)
(𝑋𝑙, 𝑌𝑙 − 𝑋𝑟, 𝑌𝑙)
(1)
where 𝑋𝑙, 𝑌𝑙 and 𝑋𝑟, 𝑌𝑟 are matching pixel between left and right camera respectively, the resultant depth data
is combined with RGB data from the camera and converted into 3D point cloud data format by normalizing
the pixel coordinate 𝑂(𝑋, 𝑌, 𝑑) into 3D coordinate 𝑃(𝑥, 𝑦, 𝑧) with camera baseline 𝐽 using:
𝑦 = 𝑑, 𝑥 =
𝑥𝑙 ∗ 𝑑
𝐽
, 𝑧 =
𝑦𝑙 ∗ 𝑑
𝐽
(2)
to be processed afterwards by subsequent sub-systems. Point cloud resultant of 1 and 2 have parallel planes in
real condition shown as parallel planes, therefore eliminating the need of calculating camera perspective.
All these processes are done in an early stage inside the camera itself.
3. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 3, June 2020: 1514 - 1521
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3.2. Filtering
Since dense point cloud data while provide more details on environment leads to longer execution
time needed for processing, therefore a method for keeping environment details while reducing point count
that leads to faster execution is needed [26]. This system uses voxel grid filtering which group data with similar
characteristics and replace the group a single data that represent the whole group, this filter was chosen for the
robust characteristic of the filter. Point cloud data is down-sampled using voxel grid method. A defined
Oriented bounding box 𝐵 along with the minimum and maximum value of point cloud in 𝑥, 𝑦, 𝑧 axis are used
to calculate voxel grid 𝐷 𝑘𝑙𝑚 in size of 𝑣𝑥 × 𝑣 𝑦 × 𝑣𝑧 which defined as:
𝐷 𝑘𝑙𝑚(0 < 𝑘 < 𝑥, 0 < 𝑙 < 𝑦, 0 < 𝑚 < 𝑧) (3)
with the voxel grid size defined, assign each grid to each point 𝑃𝑛 in point cloud 𝑃. After each grid 𝐷 𝑘𝑙𝑚
assigned and the center gravity point 𝑣𝑥𝑦𝑧 for each grid 𝐷 𝑘𝑙𝑚 with 𝑘𝑖, 𝑙𝑖, 𝑚𝑖 and 𝑘 𝑛, 𝑙 𝑛, 𝑚 𝑛 as
the outmost point of the 𝐷 𝑘𝑙𝑚 grid calculated by:
𝑣𝑥 =
𝐷 𝑘 𝑖
+ 𝐷 𝑘 𝑛
2
, 𝑣 𝑦 =
𝐷𝑡 𝑖
+ 𝐷𝑡 𝑛
2
, 𝑣𝑧 =
𝐷 𝑚 𝑖
+ 𝐷 𝑚 𝑛
2
(4)
all points 𝑃𝑥𝑦𝑧 in voxel are replaced by center gravity point of each grid 𝑣𝑥𝑦𝑧 with final position point set 𝑃´.
Process all voxel grid to acquire 𝑃´ by:
𝑃′
= 𝑋 𝑣 𝑥𝑦𝑧
(0 < 𝑥 < 𝑥 𝑚𝑎𝑥,0 < 𝑦 < 𝑦 𝑚𝑎𝑥, 0 < 𝑧 < 𝑧 𝑚𝑎𝑥) (5)
Defined size of a voxel is used to group neighbouring point of an initial point Pi with similar
characteristics and using the Pi as centroid all other point removed leaving the centroid point data afterwards.
Small voxel size, while keep more details of raw point cloud data, leads to longer execution time while bigger
voxel size have smaller execution time but loses more details. Resultant of this step are as shown on Figure 1
while voxel grid is slower, it represents the underlying surface more accurately without sacrificing keypoint
of 3D point cloud data [27]. This characteristic will enable the segmentation process to segment the data
properly. The resultant data from this step still contains all data within camera’s field of view, since the system
do not need roof information and existence of roof data in occupancy grid map process can lead to no available
moving space, hence the removal of roof point cloud data is needed. The system use a passthrough filter with
maximum vertical parameters to exclude roof point cloud [27], if passthrough filter is not exist, the roof
segmented will be defined as an obstacle since the roof plane itself lies parallel with walking plane, this
happened because the segmentation process only separate vertical and horizontal point cloud data orientation.
Because the roof will not hinder platform movement and including roof as obstacles result in no free obstacle
area, the system exclude ceiling point cloud data from being processed by the segmentation process by filter
the data using passthrough method:
𝑃𝑥𝑦𝑧 = 𝑋 𝑝 𝑥𝑦𝑧
(𝑧|𝑚 < 𝑧 < 𝑛) (6)
with P as the resultant point cloud, 𝑝 as an individual point, m as the minimum z axis value and n as maximum
z axis value.
Figure 1. Filtering result
4. TELKOMNIKA Telecommun Comput El Control
Stereo vision-based obstacle avoidance module on 3D point cloud data (Eko Purbo Wahyono)
1517
3.3. Segmentation
After pre-processing step, the point cloud data segmented to gain information on walking plane as
free moving region and obstacle data. With the definition of walking plane as a plane in 𝑥, 𝑦 axis.
By defining the plane’s mathematical model as:
𝜑 = 𝛼𝑥 + 𝛽𝑦 + 𝛾𝑧 + 𝛿 = 0 (7)
where 𝛼, 𝛽, 𝛾 on (8) are the parameters of the mathematical model of the plane and k,l,m are
the independent variables of the model to define a plane. To determine if point 𝑃 = (𝑥0, 𝑦0, 𝑧0) fit plane
𝜑 = 𝛼 × 𝑘 + 𝛽 × 𝑙 + 𝛾 × 𝑚 + 𝛿, the perpendicular distance of the point to the plane was calculated using:
𝑅( 𝑃, 𝜙) =
|𝛼 × 𝑥0 + 𝛽 × 𝑦0 + 𝛾 × 𝑧0 + 𝛿|
√𝛼2 + 𝛽2 + 𝛾22 (8)
If the point is below the threshold value, it can be considered as an inlier to the plane. By using (7) to
define the walking plane model and isolate the walking plane point cloud data from others using (8) resulted
in separation of the plane inlier from other point, the system has gained both free region in form of inlier points
and the obstacle point cloud data itself to plan the obstacle avoidance path. Result of this step is shown
by Figure 2.
Figure 2. Segmentation step comparison
3.4. Occupancy grid map
Segmented point cloud data are used for building occupancy grid map to acquire obstacle position
relative to the platform and the subsequent map can be recorded for future use or for global path planning to
already recorded area. Occupancy grid map created using obstacle point cloud data to determine if a grid
occupied by an obstacle that will cause collision [18]. Each cell Ci = x, y with a fixed size for each
point Pi = x, y, z
𝐹𝑙𝑎𝑔 𝑓𝑟𝑒𝑒 {
𝐹𝑎𝑙𝑠𝑒∀𝑃𝑖 𝑥,𝑦
= 𝐶𝑖 𝑥,𝑦
𝑇𝑅𝑈𝐸∀𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (9)
This step gains areas where the platform can safely move without risk of collision without the need
of single out every obstacle and calculate the centroid and position of each obstacle by measuring each area
and comparing the width with the minimum width required by the platform. The result as shown on Figure 3.
3.5. Path planning
This part of the system have two parallel process, the first one, path planner create a global solution
for the platform to move towards the target. The second system handle disturbance force on a platform
and output correction command to deal with small changes in environment and unexpected obstacle.
Based on timed-elastic-band method by Quinlan [19] with development on sparse model [21] to advanced
trajectories [20] with MPC approach towards TEB methods [22]. Overview of this step is as shhown on
Figure 4. This method was used because the behavior of the platform is not determined at the planning level,
yet local disturbances will not hinder the system ability to reach goals. This system deals with local disturbances
by deforming the path when environment changes are detected. Despite this step, the system still maintain a
complete collision-free path towards a determined goal. Find the shortest path towards determined goals.
5. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 3, June 2020: 1514 - 1521
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Figure 3. Occupancy grid map result
Figure 4. Path planning
𝑤( 𝑣, 𝑆𝑖) = {
0
min{𝑤(( 𝑢, 𝑣)) + 𝑑(𝑣, 𝑆𝑖)}
∞
(10)
With (10) produces global path result that used by TEB to create a series of waypoints. The shortest path, taken
as a series of a fixed number 𝑛 of waypoint 𝑃𝑖 with tolerance radius 𝑟 which comprise.
𝑃𝑖 = (
𝑥𝑖
𝑦𝑖
) (11)
Array of waypoint 𝑃𝑖 can be denoted as:
𝑄 = { 𝑃𝑖}𝑖=1,2,….,𝑛 (12)
6. TELKOMNIKA Telecommun Comput El Control
Stereo vision-based obstacle avoidance module on 3D point cloud data (Eko Purbo Wahyono)
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This system does not pursue fastest trajectory and in a context of timed elastic band, it only optimizes
the location of intermediate waypoint as the system does not have a boundary of final platform state, therefore
the change in time for each consecutive waypoint (∆𝑇𝑖) are fixed and can be denoted as (13).
𝜏 = {Δ𝑇𝑖}𝑖=1,2,…,𝑛−1 (13)
Timed elastic band function consisted of (14).
𝐵 ∶= (𝑄, 𝜏) (14)
Optimal band calculation is calculated by minimizing the objective function.
𝑓( 𝐵) = ∑ 𝛾 𝑘Γk(𝐵)
𝑘
(15)
With an array of waypoints Pi from path planning result, the system compare current platform position
and orientation information with waypoints coordinate and update waypoints array when the system achieve
waypoints or if the system stray from global path and previous waypoint array.
4. RESULTS AND ANALYSIS
The result of voxel grid filtering with 0.05 m size voxel, the resultant cloud shows a drop of 98.2% in
point cloud count from 921600 point to 16664 point. With only 1.8% of the data remains while maintains
similar characteristics with the raw data. This result shows that voxel grid preserved underlying plane
of the raw point cloud data while reducing point cloud data size as shown on Figure 1. Filtering sub-system
takes 86 ms average execution time with 720p raw data size with an acceptable result for segmentation while
reducing the data size and removal of unnecessary data proven by the success on segmentation step as shown
on 2a, faster execution time leads to more loss of details, which result in failure of segmentation. Execution
time for filtering subsystem can be improved by the usage of parallel computing for each filtering model for
faster execution. Similar method can be applied for segmentation subsystem which takes 260 ms average
execution time. Shorter execution time leads to faster map update and faster decision result. Segmentation
resulted in all point cloud data with vertical plane orientation are separated from the point cloud data with
horizontal plane orientation. Compared to colour based point cloud clustering, the result shows that
the colour based segmentation result are still detecting some vertical object as a same group as the horizontal
walking plane, while the system’s segmentation based on each point orientation separate all points with vertical
plane orientation. Despite system success of segmentation between horizontal walking plane and obstacle data,
the system fail to process some data when stereo matching failure exist, this characteristics make neighbouring
pixel with a significant distance in 3D environment result in an almost horizontal gradient, therefore the system
segmentation does not segment this area. The comparison between plane based segmentation and colour based
segmentation are shown on Figure 2 occupancy grid map resulted in information of each obstacle data position
therefore safe moving area are acquired where no obstacle are present.
The system will calculate the safe area around the obstacle grid to account for platform size and
odometry drift. All these information are successfully recorded proven by the ability of the system to plan
navigation path through already mapped area Figure 3 show that where obstacle point cloud exist,
the corresponding cells show as black cells with no failure for all obstacle point information. Path planning
resulted the shortest path in regard to platform size to avoid collision with obstacle area and modify initial path
to target to avoid an obstacle and adapt to new environment data this is proven by moving the camera according
to path planning resulted in arriving to target coordinate without any collision shown on Figure 5 (a) and
Figure 5 (b) shows the same goal position with updated environment information and platform position.
Figure 5 (b) shows the system update the path plan towards the goal when the platform is moved outside the
planned path and new obstacle information is acquired from the environment and update path when the
platform moved inside planned path Figure 5 (c). The system only produce goal array position as long as the
area width for the path planning are wider than the required width of the platform and will terminate the path
planning when the required condition are not met, the system will produce path as close as the target as possible
when the condition are met, shown on Figure 5 (d), farthest arrow’s position have area width of more than
0.7 m, or shown at the Figure 5 (d) as the blue circle diameter are 7 grid with each grid have the width of
0.1 m, while further possible position did not met this requirement, therefore the system are fulfilling the target
of creating safe path without possible collision.
7. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 3, June 2020: 1514 - 1521
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(a) (b)
(c) (d)
Figure 5. Obstacle avoidance path modification, (a) initial path data, (b) after environment and position
update (c) After position update, (d) Minimum width requirement not met
4. CONCLUSION
Filtering sub-system perform amicably with total execution time of 96ms on average. This result
of execution time is done on 720p raw point cloud data size with 0.05m voxel size and limit passthrough
of 1.2m above ground. All sub-sub-system of filtering step perform as intended.Obstacle Extraction sub-system
extract all obstacle data and mapped with appropriate result compared to the real environment. While
segmentation process have a point of failure when the resultant of stereo matching did not produce the same
result as the real condition which result in segmentation failure while occupancy grid map produces the exact
result of every segmented obstacle data.
Path planning sub-system produces appropriate path and correction command when dealing with
environment and platform update while keeping the recorded data for future use and for global navigation.
This system did not deal with global path planning with previously mapped environment and has no
superposition odometry present. This system has provided a capability of obstacle avoidance based on 3D point
cloud data interpreted by equation 10 to form basic intelligence based on TEB model to deal with collisionfree
path planning and deals with platform deviation.
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