Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
A ROBUST CHAOTIC AND FAST WALSH TRANSFORM ENCRYPTION FOR GRAY SCALE BIOMEDICA...sipij
In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed.
We used two chaotic logistic maps and combined chaotic encryption methods to the two-dimensional FWT of images.
The encryption process involves two steps: firstly, chaotic sequences generated by the chaotic logistic maps are used to
permute and mask the intermediate results or array of FWT, the next step consist in changing the chaotic sequences or
the initial conditions of chaotic logistic maps among two intermediate results of the same row or column. Changing the
encryption key several times on the same row or column makes the cipher more robust against any attack. We tested
our algorithms on many biomedical images. We also used images from data bases to compare our algorithm to those
in literature. It comes out from statistical analysis and key sensitivity tests that our proposed image encryption schemeprovides an efficient and secure way for real-time encryption and transmission biomedical images.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
A ROBUST CHAOTIC AND FAST WALSH TRANSFORM ENCRYPTION FOR GRAY SCALE BIOMEDICA...sipij
In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed.
We used two chaotic logistic maps and combined chaotic encryption methods to the two-dimensional FWT of images.
The encryption process involves two steps: firstly, chaotic sequences generated by the chaotic logistic maps are used to
permute and mask the intermediate results or array of FWT, the next step consist in changing the chaotic sequences or
the initial conditions of chaotic logistic maps among two intermediate results of the same row or column. Changing the
encryption key several times on the same row or column makes the cipher more robust against any attack. We tested
our algorithms on many biomedical images. We also used images from data bases to compare our algorithm to those
in literature. It comes out from statistical analysis and key sensitivity tests that our proposed image encryption schemeprovides an efficient and secure way for real-time encryption and transmission biomedical images.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Hardware software co simulation of edge detection for image processing system...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
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application.
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R
Using Machine Learning and Deep Learning Techniques, we train the ResNet CNN Model and build a model for estimating Depth using the Discrete Fourier Domain Analysis, and generate results including the explanation of the Loss function and code snippets.
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.
Optimization of image compression and ciphering based on EZW techniquesTELKOMNIKA JOURNAL
This paper presents the design and optimization of image compression and ciphering depend on optimized embedded zero tree of wavelet (EZW) techniques. Nowadays, the compression and ciphering of image have become particularly important in a protected image storage and communication. The challenge is put in application for both compression and encryption where the parameters of images such as quality and size are critical in secure image transmission. A new technique for secure image storage and transmission is proposed in this work. The compression is achieved by remodel the EZW scheme combine with discrete cosine transform (DCT). Encrypted the XOR ten bits by initial threshold of EZW with random bits produced from linear-feedback shift register (LFSR). The obtained result shows that the suggested techniques provide acceptable compression ratio, reduced the computational time for both compression and encryption, immunity against the statistical and the frequency attacks.
here it introduces an efficient multi-resolution watermarking methodology for copyright protection of digital images. By adapting the watermark signal to the wavelet coefficients, the proposed method is highly image adaptive and the watermark signal can be strengthen in the most significant parts of the image. As this property also increases the watermark visibility, usage of the human visual system is incorporated to prevent perceptual visibility of embedded watermark signal. Experimental results show that the proposed system preserves the image quality and is vulnerable against most common image processing distortions. Furthermore, the hierarchical nature of wavelet transform allows for detection of watermark at various resolutions, resulting in reduction of the computational load needed for watermark detection based on the noise level. The performance of the proposed system is shown to be superior to that of other available schemes reported in the literature.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
The objective of this paper is to develop a low bit rate encoding for VQ problems such as real time image coding.. The decision tree is generated by an offline process.. A new systolic architecture to realize the encoder of full search vector quantization VQ for high speed applications is presented here. Over past decades digital video compression technologies have become an integral part. Therefore the purpose is to improve image quality in Remote cardiac pulse measurement using Adaptive filter. It describes the approach to be used for feature extraction from many images.. This paper presents a real time application of compression of the image processing technique which can be efficiently used for the interfacing with any hardware. Therefore we have used Raspberry Pi in compression of image. We have developed an algorithm that is based on the endoscopic images that consist of the differential pulse code modulation. The compressors consist of a low cost YEF colour space converters and variable length predictive algorithm for lossless compression. Mr. Nilesh Bodne | Dr. Sunil Kumar "Design and Analysis of Quantization Based Low Bit Rate Encoding System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29289.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29289/design-and-analysis-of-quantization-based-low-bit-rate-encoding-system/mr-nilesh-bodne
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Hardware software co simulation of edge detection for image processing system...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
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application.
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R
Using Machine Learning and Deep Learning Techniques, we train the ResNet CNN Model and build a model for estimating Depth using the Discrete Fourier Domain Analysis, and generate results including the explanation of the Loss function and code snippets.
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.
Optimization of image compression and ciphering based on EZW techniquesTELKOMNIKA JOURNAL
This paper presents the design and optimization of image compression and ciphering depend on optimized embedded zero tree of wavelet (EZW) techniques. Nowadays, the compression and ciphering of image have become particularly important in a protected image storage and communication. The challenge is put in application for both compression and encryption where the parameters of images such as quality and size are critical in secure image transmission. A new technique for secure image storage and transmission is proposed in this work. The compression is achieved by remodel the EZW scheme combine with discrete cosine transform (DCT). Encrypted the XOR ten bits by initial threshold of EZW with random bits produced from linear-feedback shift register (LFSR). The obtained result shows that the suggested techniques provide acceptable compression ratio, reduced the computational time for both compression and encryption, immunity against the statistical and the frequency attacks.
here it introduces an efficient multi-resolution watermarking methodology for copyright protection of digital images. By adapting the watermark signal to the wavelet coefficients, the proposed method is highly image adaptive and the watermark signal can be strengthen in the most significant parts of the image. As this property also increases the watermark visibility, usage of the human visual system is incorporated to prevent perceptual visibility of embedded watermark signal. Experimental results show that the proposed system preserves the image quality and is vulnerable against most common image processing distortions. Furthermore, the hierarchical nature of wavelet transform allows for detection of watermark at various resolutions, resulting in reduction of the computational load needed for watermark detection based on the noise level. The performance of the proposed system is shown to be superior to that of other available schemes reported in the literature.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
The objective of this paper is to develop a low bit rate encoding for VQ problems such as real time image coding.. The decision tree is generated by an offline process.. A new systolic architecture to realize the encoder of full search vector quantization VQ for high speed applications is presented here. Over past decades digital video compression technologies have become an integral part. Therefore the purpose is to improve image quality in Remote cardiac pulse measurement using Adaptive filter. It describes the approach to be used for feature extraction from many images.. This paper presents a real time application of compression of the image processing technique which can be efficiently used for the interfacing with any hardware. Therefore we have used Raspberry Pi in compression of image. We have developed an algorithm that is based on the endoscopic images that consist of the differential pulse code modulation. The compressors consist of a low cost YEF colour space converters and variable length predictive algorithm for lossless compression. Mr. Nilesh Bodne | Dr. Sunil Kumar "Design and Analysis of Quantization Based Low Bit Rate Encoding System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29289.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29289/design-and-analysis-of-quantization-based-low-bit-rate-encoding-system/mr-nilesh-bodne
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
2. ABSTRACT
INTRODUCTION
Example and benefits
LITERATURE REVIEW
RESEARCH METHODOLOGY
IMPLEMENTATION
RESULT AND DISCUSSION
CONCLUSIONs
FUTURE SCOPE
REFERENCEs
3. The objective of this project is to locate and identify the
anomalies in an underground pipeline installation.
Anomalies are identified and classified as structural damage
and Operational Maintenance also we have to trained the
Deep Learning model for Anomaly Detection and
Classification.
For this we are using two techniques:-
(a) Convolutional Neural Network (CNN).
(b) Faster RCNN.
Automation of manual assessment of hours of video.
Reduce the human errors and reduce risk of liability issues.
4. Pipeline Anomaly Detection is an automated Condition
Assessment System for pipeline networks. It reduces
manual efforts and time needed to review and code
video scans.
It uses Artificial Intelligence (AI) and advanced neural
networks to identify, grade and score pipe anomalies.
Videos of pipeline are generated by a camera-mounted
rover into the underground pipelines.
It automates the process of identification of defects in
pipeline videos and generates a comprehensive
inspection report.
9. AUTHOR YEAR STUDY FINDING
S. Safari
&
M.
Aliyari
2005 Detection and isolation
of interior defects
based on image
processing, Journal of
Pipeline Systems
Engineering and
Practice.
Principles regarding the learning
algorithm or deep architectures in
particular of those building blocks
unsupervised learning for single-
layer models and for pipeline
systems.
S. Kumar
& D.M.
Abraham
2017 Automated defect
classification in sewer
closed circuit using
CNN.
Investigated land area is labeled
into water and the classification is
compared to per-pixel works.
R.
Fenner
2009 Approaches to sewer
maintenance a review,
Urban Water pipeline
anomaly detection.
Using this approach we have to
reconstruct the frames from the
video and from that frames again
extracted the I- frames from it and
then display the result.
10. Using Convolutional Neural Network (CNN) techniques for
implementing deep neural networks.
Extracting Frames from the underground pipeline video and
then train those frames with this algorithm.
Provide the underground pipeline frames dataset as an input.
We have to use Optical Character Recognition (OCR) and
Pytesseract for extracting the text from the given images.
Use OCR and Pytesseract for extracting text from image got
35% and 60% accuracy.
11. Also use Autoencoders for clearing the blur
images.
Extraction of I- Frames from the dataset using
different codes.
Detecting defects in the given video with a
camera-mounted rover and create a GUI
Application to get the output.
Got 90% accuracy using Faster RCNN.
12. For Extracting the text from the given pipeline video we
use:
(a) Optical Character Recognition (OCR)
(b) Pytesseract
(a) Optical Character Recognition (OCR):-
It is the method of extracting text from the given image.
It converts the image to gray scale, after that it smooth the image
and then it filters.
Detect lines, words and characters.
13. (b) Tesseract:-
Python-tesseract is a tool used for OCR.
It is used an API to extract printed text from images.
It supports a wide variety of languages.
Tesseract was dependant on the multi-stage process
where we can differentiate steps like Word Finding,
Line Finding and Character Classification.
14. Autoencoders:
An autoencoder is a type of artificial neural network
used to learn efficient data in an unsupervised manner.
The aim of an autoencoder is to learn a representation
for a set of data, typically for dimensionality reduction,
by training the network to ignore signal “noise”.
16. If we want to extract just a single frame (I-Frame)
from the video into an image file we use I frame for
that.
An I Frame (Intra coded frame) is a complete image
like a JPG or JPEG image file.
The basic need is to compare the quality of the image
from the dataset and the actual I Frame image.
18. (A) Convolutional Neural Network:
In deep learning, a convolutional neural network is a class of
deep neural networks, most commonly applied to analyzing
visual imagery.
They are regularized versions of multilayer perceptrons. It
usually mean fully connected networks, that is, each neuron
in one layer is connected to all neurons in the next layer.
output
19. The Accuracy for Convolutional Neural Network (CNN) for this dataset is 70%
In this model we train our dataset into 50 epoch and finally get this
accuracy.
20. (B) FASTER RCNN:
Faster R-CNN with CNN features is the object
detection architecture and the pioneer approach
that applies deep models to object detection.
The function of this network is to generate
good features from the image.
The output of this network maintains the shape
and structure of the original image.
22. The Accuracy for Faster RCNN for this dataset is 90%.
In this model we train our dataset into 50 epoch and finally get this
accuracy.
23. Using CNN and Faster RCNN to train our dataset for Pipeline
Anomaly Detection.
A combination of CCTV, microwave sensor, neutron
and gamma ray, and hydro chemical sensors would be a
powerful tool.
The most significant factor requiring water authorities to
undertake such exercises, however, will be the public
perception of sewer leakage.
In our project the accuracy for CNN is near about 70% and
for Faster RCNN it is coming 90%.
24. Any deep learning application that we are using has big
scope in future. The function of output video frames needs to
be improved in the next stage of the research.
In addition, the output needs to be enhanced by including
more information (e.g., condition grade of the defects) to
realize the automation of the pipeline assessment.
Also the size of the training dataset is to cover as many
features as possible, which will lead to improving the
performance of the model.
25. [1] R. Fenner, 2002, Approaches to sewer maintenance a review Urban Water, New York, IEEE.
[2] K. Baah, B. Dubey, R. Harvey, 2009, A risk-based approach to sanitary sewer pipe asset management,
Germany, IEEE.
[3] M.D. Yang, T.C Su, 2014, Automated diagnosis of sewer pipe defects based on machine learning approaches,
Australia, IEEE, ICLR.
[4] Xinchen Yan, Jimei Yang, 2016, Conditional Image Generation from Visual Attributes, North Korea, IEEE,
ECCV
[5] O. Moselhi, 2004, Automated detection of surface defects in water and sewer pipes, Washington D.C, IEEE.
[6] S.K. Sinha, 2003, Computer vision techniques for automatic structural assessment of underground pipes,
India ECCV, IEEE.
[7] M.J. Anbari, 2017, Risk assessment model to prioritize sewer pipes inspection in wastewater collection
networks in Environ. Manag, Germany, IEEE ECCV .
[8] S.T. Ariaratnam, 2015, Financial outlay modeling for a local sewer rehabilitation strategy and Constr. Eng.
Manag, Russia, IEEE.