The document describes a new wavelet-based support vector machine (WSVM) classifier for wildfire detection using a decision fusion framework in video. The proposed system uses five subalgorithms: 1) slow moving object detection 2) smoke-colored region detection 3) region smoothness detection 4) shadow detection and elimination 5) covariance-matrix-based classification. Decision values from the subalgorithms are combined using an adaptive decision fusion method. A new wavelet kernel is also proposed to improve the generalization ability of the SVM classifier. The WSVM model utilizes wavelet analysis to extract nonlinear characteristics from image data for classification.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
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.
Development and Hardware Implementation of an Efficient Algorithm for Cloud D...sipij
This document discusses the development and hardware implementation of an efficient algorithm for cloud detection from satellite images. The algorithm uses an adaptive thresholding approach to segment clouds from background pixels in satellite imagery. It then determines the position of the segmented clouds to calculate cloud coverage percentages. The algorithm was tested on satellite images from Spot4 and Landsat archives. It was implemented on a TMS320C6713 DSK processor using Code Composer Studio and achieved accurate cloud detection and coverage calculation on images with resolutions up to 3600x3000 pixels.
Effective Audio Storage and Retrieval in Infrastructure less Environment over...IRJET Journal
1) The document proposes a system called SAoD for effective audio storage and retrieval in infrastructure-less wireless sensor networks.
2) SAoD uses a time-division cooperative recording technique to segment audio files into chunks stored across multiple sensors. It encodes chunk metadata into Bloom filters and replicates the filters to reduce communication costs.
3) The system estimates the network size using a gossip algorithm. This allows audio chunks to be replicated probabilistically across the network, guaranteeing high retrieval success rates with low communication overhead.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
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.
Development and Hardware Implementation of an Efficient Algorithm for Cloud D...sipij
This document discusses the development and hardware implementation of an efficient algorithm for cloud detection from satellite images. The algorithm uses an adaptive thresholding approach to segment clouds from background pixels in satellite imagery. It then determines the position of the segmented clouds to calculate cloud coverage percentages. The algorithm was tested on satellite images from Spot4 and Landsat archives. It was implemented on a TMS320C6713 DSK processor using Code Composer Studio and achieved accurate cloud detection and coverage calculation on images with resolutions up to 3600x3000 pixels.
Effective Audio Storage and Retrieval in Infrastructure less Environment over...IRJET Journal
1) The document proposes a system called SAoD for effective audio storage and retrieval in infrastructure-less wireless sensor networks.
2) SAoD uses a time-division cooperative recording technique to segment audio files into chunks stored across multiple sensors. It encodes chunk metadata into Bloom filters and replicates the filters to reduce communication costs.
3) The system estimates the network size using a gossip algorithm. This allows audio chunks to be replicated probabilistically across the network, guaranteeing high retrieval success rates with low communication overhead.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
5.a robust frame of wsn utilizing localization technique 36-46Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains that localization is a fundamental challenge for wireless sensor networks. It then describes two main categories of localization techniques: range-based and range-free. Range-based techniques use distance or angle measurements between nodes to determine positions but require expensive hardware. Range-free techniques estimate positions based on neighboring node information and are less expensive but less accurate. The document reviews several specific localization algorithms from previous research and discusses their advantages and limitations.
11.0005www.iiste.org call for paper.a robust frame of wsn utilizing localizat...Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains how accuracy is important. It then describes two main categories of localization techniques: range-based and range-free. Range-based uses distance or angle measurements between nodes for higher accuracy but requires expensive hardware. Range-free relies on information from nearby nodes and is less accurate but cheaper. The document reviews several specific localization algorithms from previous research and their limitations. It concludes by stating that energy efficiency is critical for wireless sensor networks due to limited battery life.
Adaptive and online one class support vector machine-based outlier detectionNguyen Duong
This document proposes three adaptive and online one-class support vector machine techniques for outlier detection in wireless sensor networks. The techniques sequentially update the model of normal sensor data behavior and take advantage of spatial and temporal correlations between sensor readings to identify outliers with high accuracy while minimizing network resource usage. Experiments on both synthetic and real wireless sensor network data show that the proposed online outlier detection techniques achieve better detection accuracy and lower false alarm rates than previous techniques.
Basic Video-Surveillance with Low Computational and Power Requirements Using ...uberticcd
V. Caglioti, A. Giusti: "Basic Video-Surveillance with Low Computational and Power Requirements Using Long-Exposure Frames".
Proc. of Advanced Concepts for Intelligent Vision Systems (ACIVS) 2008.
Granular Mobility-Factor Analysis Framework for enrichingOccupancy Sensing wi...IJECEIAES
With the growing need for adoption of smarter resource control system in existing infrastructure, the proliferation of occupancy sensing is slowly increasing its pace. After reviewing an existing system, we find that utilization of Doppler radar is less progressive in enhancing the accuracy of occupancy sensing operation. Therefore, we introduce a novel analytical model that is meant for incorporating granularity in tracing the psychological periodic characteristic of an object by emphasizing on the mobility and uncertainty movement of an object in the monitoring area. Hence, the model is more emphasized on identifying the rate of change in any periodic physiological characteristic of an object with the aid of mathematical modelling. At the same time, the model extracts certain traits of frequency shift and directionality for better tracking of the unidentified object behavior where its applicabilibility can be generalized in majority of the fields related to object detection.
The Optics Group conducts research across several areas of optics including geometrical optics, atom optics, classical optics, quantum optics, and computational imaging. Specific projects include invisibility cloaks, vector beam shaping of warm and cold atoms, quantum communication using orbital angular momentum modes, imaging of high-dimensional spatial entanglement, and real-time compressive video reconstruction using deep learning. The group engages in public outreach activities to promote understanding of optics and quantum technologies.
Energy efficient sensor selection in visual sensor networks based on multi ob...ijcsa
In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of time. In contrast with the pervious works which considered only single metric for sensor selection method, in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the problem. Besides, impact of steady state selection and generational selection method is studied on the network lifetime. Simulation results show the superiority of multi-objective optimization. NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower overlapping ratio at each point of time.
The document discusses particle image velocimetry (PIV), which is a non-intrusive method for measuring fluid flow velocities. PIV works by seeding the fluid with particles and using a laser sheet and camera to capture particle images. Software then tracks the particle movements between images to calculate velocity vectors across the flow field. PIV has applications in analyzing flows around objects like fish, helicopters, and prosthetic heart valves. Advanced PIV systems are being developed that can perform 3D motion tracking.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
Estimating coverage holes and enhancing coverage in mixed sensor networks ormarwaeng
The document presents a collaborative algorithm (COVEN) for enhancing area coverage in mixed static and mobile sensor networks. It is a two-step process: 1) Using Voronoi diagrams, the static nodes deterministically estimate the exact amount of coverage holes after random deployment. 2) The static nodes then collaborate to estimate the number and optimal positions of additional mobile nodes needed to maximize coverage. Through simulation, COVEN aims to achieve a tradeoff between deployment cost and percentage of area covered.
This document summarizes research on algorithms for proximity estimation in sensor networks. It discusses using sensor networks to detect events observed by nodes within a certain distance of each other. It proposes an algorithm that utilizes a distributed routing index maintained by nodes in the network to process multiple proximity queries involving different event types. The document reviews several related works on localization algorithms, data-centric sensor networks, geographic routing protocols, and node localization techniques. It evaluates different wireless sensor network simulators and deployment schemes.
This document summarizes research on coverage problems in wireless sensor networks in the presence of obstacles. It begins with definitions of key concepts related to sensor network coverage, including different types of coverage problems (point, area, barrier), deployment strategies (deterministic, random), coverage degrees, sensing models, and obstacles. It then reviews several approaches that have been proposed to address coverage problems when obstacles are present in the sensor field, including using computational geometry concepts to handle obstacles. The document concludes by noting that more work is still needed to fully address coverage problems in realistic environments with obstacles.
This document discusses quantum photonics and its applications. It begins by outlining the importance of quantum photonics in fields like information processing, communication, measurement, and nanotechnology. It then provides an introduction to basic photonics concepts like photons, quantum states, and coherence. The document outlines several applications of quantum photonics in areas like quantum information processing, quantum metrology, quantum teleportation, and quantum cryptography. It also discusses advantages like secure communication and accurate measurements. Future challenges are identified as developing quantum computers, multi-particle teleportation, and a quantum internet. The document concludes by noting that quantum photonics will continue to play a central role in future technologies.
This document discusses quantum photonics and its applications. It begins by outlining the importance of quantum photonics in fields like information processing, communication, measurement, and nanotechnology. It then provides an introduction to basic photonics concepts like photons, quantum states, and coherence. The document outlines several applications of quantum photonics in areas like quantum information processing, quantum metrology, quantum teleportation, and quantum cryptography. It also discusses advantages like secure communication and accurate measurements. Future challenges are identified as developing quantum computers, multi-particle teleportation, and a quantum internet. The document concludes by noting that quantum photonics will continue to play a central role in future technologies.
The document discusses dimensionality reduction techniques for hyperspectral data in target detection applications. It presents an innovative technique called IRVE-SRRE that aims to preserve rare vectors which may indicate targets of interest, unlike traditional methods. The technique estimates the subspace of abundant background vectors then identifies the rare vectors subspace. It was tested on a case study and shown to estimate the subspace rank accurately while being more computationally efficient than existing techniques like MOCA. The technique could improve target detection algorithms and further research may expand its applications.
Deep Learning for Hidden Signals - Enabling Real-time Multimessenger Astrophy...Daniel George
Presented at the GPU Technology Conference (GTC17) in San Jose, California on May 10, 2017
-------------------------
We introduce Deep Filtering, a new method for end-to-end time-series signal processing, which combines two deep convolutional neural networks for classification and regression to detect and characterize signals much weaker than the background noise. We applied this method for gravitational wave analysis specifically for mergers of black holes and demonstrated that it significantly outperforms conventional machine learning techniques, is far more efficient than matched-filtering allowing real-time processing of raw big data with minimal resources, and extends the range of gravitational waves that can be detected by advanced LIGO. This initiates a new paradigm for scientific research which uses massively-parallel numerical simulations to train artificial intelligence algorithms that exploit emerging hardware architectures. Our approach offers a unique framework to enable coincident detection campaigns of gravitational wave sources and their multimessenger counterparts.
This document describes a master's thesis that designed a real-time telemetry system called BANET for monitoring mobile objects in groups. The system consists of sensing devices attached to objects, a group of monitored objects, and a monitoring application. Key design factors were the tendency of objects to form groups and their mobility. The innovative aspect of the system is that it supports group behavior by having neighboring objects form clusters with clusterheads that send aggregated data, improving performance over individual reporting. The thesis studied clustering algorithms and wireless technologies to design the network architecture. A prototype implementation including sensor nodes, server, and monitoring application was developed and experiments showed that clustering can significantly reduce data transmission costs for monitoring mobile groups in real-time.
Single-photon avalanche diodes (SPADs) are novel sensors that can detect individual photons with high time resolution. SPADs allow for imaging with extreme dynamic range from low to high light conditions without saturation. They also enable minimal motion blur imaging due to their ability to precisely timestamp single photons. Recent research has demonstrated burst photography using SPAD arrays that can reconstruct non-rigid scene motion and produce almost motion-blur free images in dark environments. However, challenges remain in increasing resolution, reducing data rates and power consumption before widespread commercial applications can be realized.
This paper proposes a novel Adaptive Rood Pattern Search (ARPS) algorithm for block matching in video shot boundary detection. ARPS uses motion activity, which is measured by the magnitude of motion vectors between frames, to help predict the motion vector for each block. The algorithm works by first assuming blocks near a current block will move in a similar direction, then searching in a pattern around the predicted motion vector. Experimental results on test videos show ARPS can accurately detect shot boundaries with fewer false detections than other block matching algorithms.
1) The document proposes a ubiquitous virtual currency system as an alternative to physical currency to address issues of deforestation and environmental impact.
2) In the proposed system, every citizen registers with a recognized bank and is provided a SIM card for contactless payments using biometrics. Transactions can be initiated by holding phones near each other or requesting bank approval for larger transfers.
3) Shops and other businesses would be equipped with biometric scanners to process payments. The system aims to make currency accessible anywhere through interconnected mobile and banking technology.
5.a robust frame of wsn utilizing localization technique 36-46Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains that localization is a fundamental challenge for wireless sensor networks. It then describes two main categories of localization techniques: range-based and range-free. Range-based techniques use distance or angle measurements between nodes to determine positions but require expensive hardware. Range-free techniques estimate positions based on neighboring node information and are less expensive but less accurate. The document reviews several specific localization algorithms from previous research and discusses their advantages and limitations.
11.0005www.iiste.org call for paper.a robust frame of wsn utilizing localizat...Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains how accuracy is important. It then describes two main categories of localization techniques: range-based and range-free. Range-based uses distance or angle measurements between nodes for higher accuracy but requires expensive hardware. Range-free relies on information from nearby nodes and is less accurate but cheaper. The document reviews several specific localization algorithms from previous research and their limitations. It concludes by stating that energy efficiency is critical for wireless sensor networks due to limited battery life.
Adaptive and online one class support vector machine-based outlier detectionNguyen Duong
This document proposes three adaptive and online one-class support vector machine techniques for outlier detection in wireless sensor networks. The techniques sequentially update the model of normal sensor data behavior and take advantage of spatial and temporal correlations between sensor readings to identify outliers with high accuracy while minimizing network resource usage. Experiments on both synthetic and real wireless sensor network data show that the proposed online outlier detection techniques achieve better detection accuracy and lower false alarm rates than previous techniques.
Basic Video-Surveillance with Low Computational and Power Requirements Using ...uberticcd
V. Caglioti, A. Giusti: "Basic Video-Surveillance with Low Computational and Power Requirements Using Long-Exposure Frames".
Proc. of Advanced Concepts for Intelligent Vision Systems (ACIVS) 2008.
Granular Mobility-Factor Analysis Framework for enrichingOccupancy Sensing wi...IJECEIAES
With the growing need for adoption of smarter resource control system in existing infrastructure, the proliferation of occupancy sensing is slowly increasing its pace. After reviewing an existing system, we find that utilization of Doppler radar is less progressive in enhancing the accuracy of occupancy sensing operation. Therefore, we introduce a novel analytical model that is meant for incorporating granularity in tracing the psychological periodic characteristic of an object by emphasizing on the mobility and uncertainty movement of an object in the monitoring area. Hence, the model is more emphasized on identifying the rate of change in any periodic physiological characteristic of an object with the aid of mathematical modelling. At the same time, the model extracts certain traits of frequency shift and directionality for better tracking of the unidentified object behavior where its applicabilibility can be generalized in majority of the fields related to object detection.
The Optics Group conducts research across several areas of optics including geometrical optics, atom optics, classical optics, quantum optics, and computational imaging. Specific projects include invisibility cloaks, vector beam shaping of warm and cold atoms, quantum communication using orbital angular momentum modes, imaging of high-dimensional spatial entanglement, and real-time compressive video reconstruction using deep learning. The group engages in public outreach activities to promote understanding of optics and quantum technologies.
Energy efficient sensor selection in visual sensor networks based on multi ob...ijcsa
In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of time. In contrast with the pervious works which considered only single metric for sensor selection method, in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the problem. Besides, impact of steady state selection and generational selection method is studied on the network lifetime. Simulation results show the superiority of multi-objective optimization. NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower overlapping ratio at each point of time.
The document discusses particle image velocimetry (PIV), which is a non-intrusive method for measuring fluid flow velocities. PIV works by seeding the fluid with particles and using a laser sheet and camera to capture particle images. Software then tracks the particle movements between images to calculate velocity vectors across the flow field. PIV has applications in analyzing flows around objects like fish, helicopters, and prosthetic heart valves. Advanced PIV systems are being developed that can perform 3D motion tracking.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
Estimating coverage holes and enhancing coverage in mixed sensor networks ormarwaeng
The document presents a collaborative algorithm (COVEN) for enhancing area coverage in mixed static and mobile sensor networks. It is a two-step process: 1) Using Voronoi diagrams, the static nodes deterministically estimate the exact amount of coverage holes after random deployment. 2) The static nodes then collaborate to estimate the number and optimal positions of additional mobile nodes needed to maximize coverage. Through simulation, COVEN aims to achieve a tradeoff between deployment cost and percentage of area covered.
This document summarizes research on algorithms for proximity estimation in sensor networks. It discusses using sensor networks to detect events observed by nodes within a certain distance of each other. It proposes an algorithm that utilizes a distributed routing index maintained by nodes in the network to process multiple proximity queries involving different event types. The document reviews several related works on localization algorithms, data-centric sensor networks, geographic routing protocols, and node localization techniques. It evaluates different wireless sensor network simulators and deployment schemes.
This document summarizes research on coverage problems in wireless sensor networks in the presence of obstacles. It begins with definitions of key concepts related to sensor network coverage, including different types of coverage problems (point, area, barrier), deployment strategies (deterministic, random), coverage degrees, sensing models, and obstacles. It then reviews several approaches that have been proposed to address coverage problems when obstacles are present in the sensor field, including using computational geometry concepts to handle obstacles. The document concludes by noting that more work is still needed to fully address coverage problems in realistic environments with obstacles.
This document discusses quantum photonics and its applications. It begins by outlining the importance of quantum photonics in fields like information processing, communication, measurement, and nanotechnology. It then provides an introduction to basic photonics concepts like photons, quantum states, and coherence. The document outlines several applications of quantum photonics in areas like quantum information processing, quantum metrology, quantum teleportation, and quantum cryptography. It also discusses advantages like secure communication and accurate measurements. Future challenges are identified as developing quantum computers, multi-particle teleportation, and a quantum internet. The document concludes by noting that quantum photonics will continue to play a central role in future technologies.
This document discusses quantum photonics and its applications. It begins by outlining the importance of quantum photonics in fields like information processing, communication, measurement, and nanotechnology. It then provides an introduction to basic photonics concepts like photons, quantum states, and coherence. The document outlines several applications of quantum photonics in areas like quantum information processing, quantum metrology, quantum teleportation, and quantum cryptography. It also discusses advantages like secure communication and accurate measurements. Future challenges are identified as developing quantum computers, multi-particle teleportation, and a quantum internet. The document concludes by noting that quantum photonics will continue to play a central role in future technologies.
The document discusses dimensionality reduction techniques for hyperspectral data in target detection applications. It presents an innovative technique called IRVE-SRRE that aims to preserve rare vectors which may indicate targets of interest, unlike traditional methods. The technique estimates the subspace of abundant background vectors then identifies the rare vectors subspace. It was tested on a case study and shown to estimate the subspace rank accurately while being more computationally efficient than existing techniques like MOCA. The technique could improve target detection algorithms and further research may expand its applications.
Deep Learning for Hidden Signals - Enabling Real-time Multimessenger Astrophy...Daniel George
Presented at the GPU Technology Conference (GTC17) in San Jose, California on May 10, 2017
-------------------------
We introduce Deep Filtering, a new method for end-to-end time-series signal processing, which combines two deep convolutional neural networks for classification and regression to detect and characterize signals much weaker than the background noise. We applied this method for gravitational wave analysis specifically for mergers of black holes and demonstrated that it significantly outperforms conventional machine learning techniques, is far more efficient than matched-filtering allowing real-time processing of raw big data with minimal resources, and extends the range of gravitational waves that can be detected by advanced LIGO. This initiates a new paradigm for scientific research which uses massively-parallel numerical simulations to train artificial intelligence algorithms that exploit emerging hardware architectures. Our approach offers a unique framework to enable coincident detection campaigns of gravitational wave sources and their multimessenger counterparts.
This document describes a master's thesis that designed a real-time telemetry system called BANET for monitoring mobile objects in groups. The system consists of sensing devices attached to objects, a group of monitored objects, and a monitoring application. Key design factors were the tendency of objects to form groups and their mobility. The innovative aspect of the system is that it supports group behavior by having neighboring objects form clusters with clusterheads that send aggregated data, improving performance over individual reporting. The thesis studied clustering algorithms and wireless technologies to design the network architecture. A prototype implementation including sensor nodes, server, and monitoring application was developed and experiments showed that clustering can significantly reduce data transmission costs for monitoring mobile groups in real-time.
Single-photon avalanche diodes (SPADs) are novel sensors that can detect individual photons with high time resolution. SPADs allow for imaging with extreme dynamic range from low to high light conditions without saturation. They also enable minimal motion blur imaging due to their ability to precisely timestamp single photons. Recent research has demonstrated burst photography using SPAD arrays that can reconstruct non-rigid scene motion and produce almost motion-blur free images in dark environments. However, challenges remain in increasing resolution, reducing data rates and power consumption before widespread commercial applications can be realized.
This paper proposes a novel Adaptive Rood Pattern Search (ARPS) algorithm for block matching in video shot boundary detection. ARPS uses motion activity, which is measured by the magnitude of motion vectors between frames, to help predict the motion vector for each block. The algorithm works by first assuming blocks near a current block will move in a similar direction, then searching in a pattern around the predicted motion vector. Experimental results on test videos show ARPS can accurately detect shot boundaries with fewer false detections than other block matching algorithms.
1) The document proposes a ubiquitous virtual currency system as an alternative to physical currency to address issues of deforestation and environmental impact.
2) In the proposed system, every citizen registers with a recognized bank and is provided a SIM card for contactless payments using biometrics. Transactions can be initiated by holding phones near each other or requesting bank approval for larger transfers.
3) Shops and other businesses would be equipped with biometric scanners to process payments. The system aims to make currency accessible anywhere through interconnected mobile and banking technology.
Evaluation of Biocontrol agents against Lasiodiplodia theobromae causing Infl...IOSR Journals
This study evaluated the biocontrol potential of Trichoderma viride and Aspergillus niger against Lasiodiplodia theobromae, the causal agent of inflorescence blight disease in cashew. In dual culture tests, both T. viride and A. niger significantly inhibited the growth of L. theobromae compared to the control. A. niger exhibited the highest inhibition, reducing pathogen growth by 74.7% in one technique. In a second technique, T. viride showed the strongest antagonism, limiting pathogen growth by 90.5%. The study suggests that both tested biocontrol agents, particularly A. niger, have potential for managing inflorescence
Performance Appraisal and Ranking of DCCBs through Malmquist Index and Super-...IOSR Journals
This document discusses evaluating the performance of District Central Co-operative Banks (DCCBs) in Andhra Pradesh, India from 2006-2011 using Data Envelopment Analysis (DEA). DEA was used to assess the technical efficiency of DCCBs and rank them based on their efficiencies. The Malmquist Index was also used to analyze total factor productivity, technical change, and technological change of DCCBs over this period. Super-efficiency DEA was applied to resolve any ties in the rankings of efficient DCCBs. The analysis found that intermediation factors contributed to performance appraisal of DCCBs and there were efficiency trends over time.
Are you interested in increasing your Google PageRank?believe52
Google PageRank is an algorithm that determines where a website ranks in Google search results. It is based on the number and quality of inbound links to a site, with more popular sites that link to a page resulting in a higher PageRank. Improving a site's PageRank is important because it leads to more traffic and visibility in Google searches. Website owners can boost their PageRank through link campaigns that aim to get links from relevant, popular sites.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Multicast Routing Protocol with Group-Level Congestion Prediction and Perman...IOSR Journals
This document proposes a cross-layered model for congestion prediction and management in mobile ad hoc networks that aims to efficiently distribute network resources. The model incorporates two algorithms: Group-level Congestion Prediction (GCP) that predicts congestion levels at relay nodes with high accuracy, and Group-level Egress Permanence (GEP) that works sequentially with GCP for congestion detection and management. The document discusses related work on multicast routing protocols and energy-efficient multicasting. It then describes the proposed congestion control mechanism under constrained energy utilization and outlines the proposed model with relevant notations before focusing on the GCP and GEP algorithms.
Characterization of Arsenic contaminated Rice (Oryza Sativa L.) through RAPD ...IOSR Journals
This study characterized rice genotypes for arsenic contamination using RAPD markers. Forty rice genotypes from India and other countries were tested for arsenic levels in fields with known arsenic contamination. DNA was extracted from plants and amplified using RAPD primers. Fourteen primers produced polymorphic bands that were scored. Cluster analysis grouped genotypes into four main clusters, separating japonica types from indica. Varieties previously found to have low or high arsenic levels clustered separately, indicating RAPD may help discriminate arsenic uptake ability. Further research with more markers is needed to better predict arsenic accumulation through genetics. This study provides a preliminary analysis of using molecular markers to study genetic control of arsenic uptake in rice.
1. The document presents a hybrid algorithm that combines Kernelized Fuzzy C-Means (KFCM), Hybrid Ant Colony Optimization (HACO), and Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to improve clustering of electrocardiogram (ECG) beat data.
2. The algorithm maps data into a higher dimensional space using kernel functions to make clusters more linearly separable, addresses issues with KFCM being sensitive to initialization and prone to local minima.
3. It uses HACO to optimize cluster centers and membership degrees, and FAPSO to evaluate fitness values and optimize weight vectors, forming usable clusters for applications like ECG classification.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
La persona describe sus relaciones cercanas con su mascota, su mejor amiga, su mejor amigo, su banda y su hermana consentida, y cómo forman parte importante de su familia y apoyo.
IOSR Journal of Applied Chemistry (IOSR-JAC) is an open access international journal that provides rapid publication (within a month) of articles in all areas of applied chemistry and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Chemical Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
- The document is a collection of essays, stories, poems and other writings by Christopher G. Burley that examine philosophical and social issues.
- It explores concepts like duality, free will, omniscience, and the relationship between the limited and unlimited.
- One story describes a magician who creates a woman but then struggles with loneliness, desire, and the friction between subject and object. God later explains to him the nature of duality and the human condition.
This document discusses the fatty acid composition of melon seed oil and its potential application in synthesizing alkyd resins for use as surface coatings. Gas chromatography-mass spectrometry analysis revealed that the most abundant fatty acid in melon seed oil is octadec-14,17-dienoic acid at 56.86%. Four sets of alkyd resins were synthesized using varying percentages of crude and refined melon seed oil. The properties of the alkyd resins such as drying time, hardness, gloss, color, and resistance to chemicals were evaluated and compared to commercially available soybean alkyd paints. The short oil alkyd made from crude and refined melon seed oil exhibited the best
Reconstruction of Objects with VSN M.Priscilla - UG Scholar,
B.Nandhini - UG Scholar,
S.Manju - UG Scholar,
S.Shafiqa Shalaysha – UG Scholar,
Christo Ananth - Assistant Professor,
Department of ECE,
Francis Xavier Engineering College, Tirunelveli, India
An optimized framework for detection and tracking of video objects in challen...ijma
Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as
cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult
problem, especially in case of multiple moving objects. Object detection in the presence of camera noise
and with variable or unfavourable luminance conditions is still an active area of research. This paper
propose a framework which can effectively detect the moving objects and track them despite of occlusion
and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision
algorithm which uses a multi-background model. The video object tracking is able to track multiple objects
along with their trajectories based on Continuous Energy Minimization. In this work, an effective
formulation of multi-target tracking as minimization of a continuous energy is combined with multibackground
registration. Apart from the recent approaches, it focus on making use of an energy that
corresponds to a more complete representation of the problem, rather than one that is amenable to global
optimization. Besides the image evidence, the energy function considers physical constraints, such as target
dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track
multiple objects despite of occlusions under dynamic background conditions.
SUPPORT VECTOR MACHINE-BASED FIRE OUTBREAK DETECTION SYSTEMijscai
This study employed Support Vector Machine (SVM) in the classification and prediction of fire outbreak based on fire outbreak dataset captured from the Fire Outbreak Data Capture Device (FODCD). The fire outbreak data capture device (FODCD) used was developed to capture environmental parameters values used in this work. The FODCD device comprised DHT11 temperature sensor, MQ-2 smoke sensor, LM393 Flame sensor, and ESP8266 Wi-Fi module, connected to Arduino nano v3.0.board. 700 data point were captured using the FODCD device, with 60% of the dataset used for training while 20% was used for testing and validation respectively. The SVM model was evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), Accuracy, Error Rate (ER), Precision, and Recall performance metrics. The performance results show that the SVM algorithm can predict cases of fire outbreak with an accuracy of 80% and a minimal error rate of 0.2%. This system was able to predict cases of fire outbreak with a higher degree of accuracy. It is indicated that the use of sensors to capture real world dataset, and machine learning algorithm such as support vector machine gives a better result to the problem of fire management.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Forest Fire Detection Using Deep Learning and Image RecognitionIRJET Journal
This document describes a proposed system for forest fire detection using deep learning and image recognition techniques. The system aims to build a more accurate fire detection model using a customized VGG16 convolutional neural network. It involves collecting fire and non-fire images to train and test the model. The proposed system is expected to achieve higher accuracy than existing sensor-based systems by directly analyzing images to classify fires versus other heat sources.
The document summarizes research related to computational imaging being conducted by the Imaging Concepts group led by Prof. Andy Harvey at the University of Glasgow. Specific areas discussed include:
- Developing low-cost infrared camera array systems using computational techniques as an alternative to more complex optical setups.
- Applying techniques like synthetic aperture, light field imaging, temporal interpolation and pixel super-resolution to camera arrays.
- Demonstrating snapshot pixel super-resolution and integral imaging using a 5x5 camera array to produce higher resolution images.
- Developing a low-cost multispectral LWIR system using 6 FLIR LEPTON cameras and filters to enable applications like gas detection and classification
Fog computing factory in alliance nearly bovine computing, optimizing the use of this resource. Currently, crush exercise matter is abeyance to the backward, stored and analyzed, limitation which a decision is made and action taken. But this practices isn’t efficient. Utter computing allows computing, honest and action-taking to enter into the picture near IoT belongings and only pushes relevant matter to the cloud. “Fuzz distributes not at all bad quick-wittedness near at the service better accordingly we nub run this torrent of observations,” explains Baker. “So we thus adjustment it newcomer disabuse of uphold data into unalloyed hint go wool-gathering has favour lose concentration gear up gets forwarded up to the cloud. We posterior then heap up it into data warehouses; we bum do predictive analysis.” This beyond to the data-path send away for is enabled by the increased count functionality that manufacturers such as Cisco are building into their edge switches and routers. Fog Computing plays a role. Nonetheless it is a advanced pronunciation, this technology ahead has a designation backing bowels the globe of the modish data centre and the cloud. Bringing details adjust to the user. The middle of facts zoological unbecoming near the unresponsive creates a straightforward convene to cache observations or other help. These services would be located actual to the end-user to proceed on latency concerns and data access. Rather than of conformation inform at data centre sites anent outlandish the end-point, the Fuzz aims to place the data close to the end-user. Creating purblind geographical distribution. Fogginess computing extends forthright clouded advice by creating a help network which sits at numerous points. This, screen, geographically verbose infrastructure helps in numerous ways. Foremost of enclosing, chunky details and analytics arise be unalloyed faster with better results. Gifted-bodied, administrators are able to on ice location-based
Traditional fire detection depends on smoke sensors. This strategy, however, is unsuited for big and open buildings, as well as outdoor regions. As a result, based on computer vision systems, this research proposes an effective method for recognizing flames in open areas. To minimize data size without losing important information, integer Haar lifting wavelet transform is used to frame and analyze the input video. Then, three color spaces (binary, hue, saturation, value (HSV), and YCbC) are used in simultaneous color detection. In binary space, Otsu’s approach is utilized to determine automated intensity pixels. Additionally, using frame differences to reduce false alarms. According to the experimental results, the approach achieves 99% accuracy for offline videos and surpasses 93% accuracy for real-time videos while maintaining a lower level of complexity.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
A Study of Motion Detection Method for Smart Home SystemAM Publications
Motion detection surveillance technology give ease for time-consuming reviewing process that a normal video
surveillance system offers. By using motion detection, it save the monitoring time and cost. It has gained a lot of interests
over the past few years. In this paper, a proposed motion detection surveillance system, through the study and evaluation
of currently available different methods. The proposed system is efficient and convenient for both office and home uses as
a smart home security system technology.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
1) The document proposes a novel clustering routing and coverage optimization algorithm called Multiverse Crow Conscious Clustering Routing and Coverage Optimization Algorithm (MVCCRO) to address issues in wireless sensor networks during the COVID-19 pandemic.
2) MVCCRO aims to maximize network coverage while minimizing energy consumption and maintaining balanced energy usage across sensor clusters. It uses a hybrid approach combining the Multiverse optimization algorithm and Crow Search algorithm.
3) The algorithm is evaluated against other nature-inspired wireless sensor network optimization methods and is shown to increase coverage significantly while maintaining optimized network performance.
JPN1414 Distributed Deployment Algorithms for Improved Coverage in a Networ...chennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document summarizes a study that evaluated two approaches for classifying amphibian species using a wireless multimedia sensor network (WMSN). The first approach performed classification at each sensor node, while the second performed classification at the sink node. Both approaches were tested in simulations. The results showed the first approach was 13% more energy efficient than the second approach for a network of 25 sensor nodes transmitting data every 30 seconds to a single sink node. Performing classification at the sensor nodes significantly increases the network lifespan by reducing energy consumption compared to performing classification at the central sink node.
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networksambitlick
The document summarizes an algorithm called WMCL that improves the sampling efficiency and localization accuracy of existing SMC-based localization algorithms for mobile sensor networks. WMCL achieves higher sampling efficiency by further reducing the size of sensor nodes' bounding boxes, which restrict the scope from which candidate samples are selected, by up to 87%. This improves the sampling efficiency by up to 95%. WMCL also improves localization accuracy by using estimated position information from sensor neighbors, achieving similar accuracy with less communication and computation compared to other algorithms using similar methods.
A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videosijtsrd
The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g
This document describes a vision-based intelligent fire detection system that analyzes video data in real-time to detect fires. It analyzes specific low-level visual features of potential fire regions from frame to frame, including color, area size, boundary roughness, and surface coarseness. These features allow fast processing and are powerful discriminants due to the flickering and random nature of fire. The method evaluates changes in each feature and combines results using fuzzy logic for robust fire recognition in real-time video, providing an improvement over previous color-based or motion-based fire detection methods.
AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NET...csandit
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous advancement in various scientific and industrial fields. WSNs offer favorable low cost and readily deployable solutions to perform the monitoring, target tracking, and recognition of physical events. The foremost step required for these types of ad-hoc networks is to deploy all the sensor nodes in their positions carefully to form an efficient network. Such network should satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement problems. In fact, we present a new optimization approach based on the Flower Pollination Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We have compared the performance of the resulting algorithm, called FPACO, with the original practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances, FPACO performs better than all other algorithms.
IRJET- Surveillance of Object Motion Detection and Caution System using B...IRJET Journal
This document describes a proposed surveillance system using a block matching algorithm for motion detection. The system would use IP cameras to stream video that is monitored for unauthorized activity. Motion detection is performed by comparing frames using the block matching algorithm to detect changes in pixel intensity values, which would trigger an alarm. The block matching algorithm divides frames into blocks of pixels and validates the maximum and minimum intensity of each pixel. Comparing blocks between frames identifies motion if intensity values change beyond a threshold. If motion is detected in a designated sensitive area, the system saves the video and sends alerts by email and mobile notification to users.
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 9, Issue 5 (Mar. - Apr. 2013), PP 52-59
www.iosrjournals.org
A New Wavelet Based SVM Classifier for Wild Fire Detection
Using Decision Fusion Framework in Video
S.R Raji and Radha Krishnan B.L
Abstract: There has been an increasing interest in the study of video based fire detection algorithms as video
based surveillance systems become widely available for indoor and outdoor monitoring applications. Although
many video based smoke-detection algorithms have been developed and applied in various experimental or real
life applications, but the standard method for evaluating their quality has not yet been proposed. In this
framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields
its own decision as a real number centered around zero, representing the confidence level of that particular
subalgorithm. In this project, the wavelet support vector machine (WSVM)-based model is used for Wild fire
detection (WFD). Decision values are linearly combined with weights that are updated online according to an
active fusion method based on performing entropic projections onto convex sets describing subalgorithms. The
new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM).
More-over, the proposed model utilizes the principle of wavelet analysis to facilitate nonlinear characteristic
extraction of the image data. To reduce misclassification due to fog, an efficient fog removal scheme using
adaptive normalization method.
Index Terms—Active fusion, wildfire detection using video, Smoke detection, Wavelets Support vector machine,
Video processing.
I. Introduction
Forest fires can be a very severe and serious problem in regions with hot climate and extensive
vegetation. Video surveillance has become a widely used tool for monitoring wild fire. It is useful in many
fields such as law enforcement, security, and protection of the environment. Early detection of forest fires is
very important to reducing fire damage. Flames may not be visible to the monitoring camera if the flames occur
in a long distance or are obscured by obstacles like mountains or buildings. Several flame detection methods
have been proposed and recently the focus has shifted to smoke detection. Smoke is a good indicator of a forest
fire, but it can be difficult to identify smoke in images because it does not have a specific shape or color
patterns. Most of the flame detection systems are either based on pixel intensity recognition or on motion
detection.
In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each
of which yields its own decision. The final decision is reached based on a set of real numbers representing
confidence levels of various subalgorithms. Decision values are linearly combined with weights that are updated
online using an active fusion method based on performing entropic projections (e-projections) onto convex sets
describing the subalgorithms. A multiple-classifier system is useful for difficult pattern recognition problems,
particularly when large class sets and noisy data are involved, by allowing the use of arbitrary feature
descriptors and classification procedures at the same time.
A new wavelet kernel is proposed to improve the generalization ability of the support vector machine
(SVM). To reduce misclassification due to fog, an efficient fog removal scheme using adaptive normalization
method is used. The new wavelet kernel can vary among different kernels according to specific applications,
which makes the WSVM acquire better generalization ability than the SVM with an RBF kernel. Moreover, the
proposed model utilizes the principle of wavelet analysis to facilitate nonlinear characteristic extraction of the
image data. Therefore, the proposed WSVM-based model [1] is superior to the conventional method. The
support vector machine (SVM) is a new universal learning machine, which is applied to both regression and
pattern recognition. An SVM uses a device called kernel mapping to map the data in input space to a high-
dimensional feature space in which the problem becomes linearly separable. The decision function of an SVM is
related not only to the number of SVs and their weights, but also to the a priori chosen kernel that is called as
support vector kernel.
It is valuable to study the problem of whether a better performance could be obtained if the wavelet
technique is combined with SVMs. An admissible SV kernel, which is a wavelet kernel constructed in this
project. It implements the combination of the wavelet technique with SVMs. The wavelet kernel has the same
expression as a multidimensional wavelet function; therefore, the goal of the WSVMs is to find the optimal
approximation or classification in the space spanned by multidimensional wavelets or wavelet kernels.
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2. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
II. Existing Works
The studies in the field of collective recognition, which were started in the mid 1950s, found wide
application in practice during the last decade, leading to solutions to complex large scale applied problems.
Ko et al. [2] have proposed a non-linear classification method using support vector machines and
luminescence maps, showing that the method is robust in several scenarios compared to features used earlier for
flame detection. Guillemant and Vicente [3] propose an algorithm based on fractals for smoke detection in forest
fire scenario with impressive results. Thou-Ho et al. [4] propose a rule based system to detect smoke which is
based on pixel intensity. They perform intensity based characterization of smoke. Xu et al. [5] use single stage
wavelet energy and a back propagation neural network on a small dataset for smoke detection. The system
requires a high processing power which is unavailable in CCD camera networks. Piccinini et al. [6] propose a
Bayesian framework for smoke motion detection using the wavelet energy of 8 × 8 pixel block and intensity of
pixels. Vezzani et al. [7] propose a similar system in the context of ViSOR repository. Yang el al. [8] propose a
support vector machine based approach using motion detection as the feature to detect the smoke contour.
Recently, Yuan et al. [9] have reported a block by block approach based on chrominance and motion
orientation. They propose a new fast algorithm for motion orientation estimation. However, the chrominance
based methods they use have a disadvantage in their dependence on the color of smoke. Also, the motion
estimation algorithm is very time consuming in the context of smoke detection. Ferrari et al. [10] have proposed
a block based approach similar to the proposed approach for steam detection in oil sand mines. They use
Wavelet and Hidden Markov Model for feature extraction and support vector machine for classification with
very good accuracy of over 90%. However, the system is fine tuned to the oil sand application. Moreover, only
the steam is characterized in their approach where as this paper presents a novel algorithm for smoke detection
which has the ability to detect smoke in various scenarios.
III. Proposed System
In this paper, the EADF framework is applied to a computer-vision-based wildfire detection problem.
The system based on this method is currently being used in more than 60 forest-fire lookout towers in the
Mediterranean region. The proposed automatic video-based wildfire detection algorithm is based on five
subalgorithms: 1) slow moving video object detection; 2) smoke-colored region detection; 3) wavelet-transform-
based region smoothness detection; 4) shadow detection and elimination; and 5) covariance-matrix-based
classification. Each sub-algorithm separately decides on the existence of smoke in the viewing range of the
camera. Decisions from subalgorithms are combined with the adaptive decision fusion (ADF) method. Initial
weights of the subalgorithms are determined from actual forest-fire videos and test fires. They are updated by
using e-projections onto hyperplanes defined by the fusion weights. It is assumed that there is an oracle
monitoring the decisions of the combined algorithm. In the wildfire detection case, the oracle is a security guard.
Whenever a fire is detected, the decision should be acknowledged by the security guard. The decision algorithm
will also produce false alarms in practice. Whenever an alarm occurs, the system asks the security guard to
verify its decision. If it is incorrect, the weights are updated according to the decision of the security guard.
Image pre-processing can significantly increase the reliability of an optical inspection. Several filter
operations which intensify or reduce certain image details enable an easier or faster evaluation. Preprocessing in
images are having lots of advantages. It can reduce noise and can perform image enhancements operations such
as smoothening, sharpening etc.
When the input data to an algorithm is too large to be processed and it is suspected to be notoriously
redundant (e.g. the same measurement in both feet and meters) then the input data will be transformed into a
reduced representation set of features (also named features vector). Transforming the input data into the set of
features is called feature extraction. If the features extracted are carefully chosen it is expected that the features
set will extract the relevant information from the input data in order to perform the desired task using this
reduced representation instead of the full size input. To reduce misclassification due to fog, an efficient fog
removal scheme using adaptive normalization method is used in the preprocessing step.
IV. Compound Algorithm
The compound algorithm be composed of M-many detection subalgorithms D1, D2,… Dm. Upon
receiving a sample input at time step n, each subalgorithm yields a decision value Di(x,y) £ R centered around
zero. If Di(x,y) > 0 , it means that the event is detected by the th subalgorithm. Otherwise, it is assumed that the
event did not happen. The type of the sample input may vary depending on the algorithm. It may be an
individual pixel, or an image region, or the entire image depending on the subalgorithm of the computer vision
problem.
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3. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
A. Detection of Slow Moving Object
Video objects at far distances to the camera seem to move slower (px/sec) in comparison to the nearby
objects moving at the same speed. Assuming the camera is fixed, two background images, Bfast(x, n) and
Bslow(x,n) corresponding to the scene with different update rates ,where x is the location of the pixel at frame
number n. A background image B(x, n + 1) at time instant n + 1 is recursively estimated from the image frame
I(x, n) and the background image B(x, n) depends on the location of pixel x at frame number n.
I(x, n) represent the intensity value of the pixel at location x in the nth video frame I, and a is a
parameter between 0 and 1. Initially, Bfast(x, 0) and Bslow(x, 0) can be taken as I(x, 0). Background images
Bfast(x,n) and Bslow(x, n) are updated with different update rates. If there exists a substantial difference between
the two images for some period of time, then an alarm for slow moving region is raised, and the region is
marked. The decision value indicating the confidence level of the first sub-algorithm is determined by the
difference between background images. The decision function D1(x, n) will give the confidence value.
B. Detection of Smoke-Colored Regions
Whenever a slow moving region is detected, its color content is analyzed. Smoke due to forest fires is
mainly composed of carbon dioxide, water vapor, carbon monoxide, particulate matter, hydrocarbons and other
organic chemicals, nitrogen oxides, trace minerals and some other compounds. The grayish color of the rising
plume is primarily due to water vapor and carbon particles in the output fire composition. Such regions can be
identified by setting thresholds in the YUV color space. Also, luminance value of smoke regions should be high
especially at the initial phases of a wildfire. On the other hand, the chrominance values should be very low in a
smoke region. Confidence value corresponding to this sub-algorithm should account for these characteristics.
The decision function D2(x, n) takes values between 1 and -1 depending on the values of the Y (x,n),
U(x,n) and V (x, n) channel values. Y (x, n), U(x, n) and V (x, n) are the luminance and chrominance values of
the pixel at location x of the input image frame at time step n, respectively. The confidence level of D2(x, n) is -
1 if Y (x, n) is below T1. The reason that we have the threshold T1 is to eliminate dark regions which also have
low chrominance values.
C. Detection of Rising Regions
Wildfire smoke regions tend to rise up into the sky at the early stages of the fire. This characteristic
behavior of smoke plumes is modeled with three-state Hidden Markov Models [11] (HMM). Temporal variation
in row number of the upper-most pixel belonging to a slow moving region is used as a one dimensional (1-D)
feature signal, F = f(n), and fed to the Markov models. One of the models (λ1) corresponds to genuine wildfire
smoke regions and the other one (λ 2) corresponds to regions with clouds and cloud shadows. The state S1 is
attained, if the row value of the upper-most pixel in the current image frame is smaller than that of the previous
frame (rise-up). If the row value of the upper-most pixel in the current image frame is larger than that of the
previous frame, then S2 is attained and this means that the region moves-down. No change in the row value
corresponds to S3.
D. Shadow Detection & Removal
Shadows of slow moving clouds are major source of false alarms for video based wildfire smoke detection
systems. Unfortunately, shadows of clouds have very low U and V values, similar to smoke regions due to
wildfires. The decision function for shadow regions are defined based on the shadow detection method. Average
RGB values are calculated for slow moving regions both in the current and the background images. Let S(n)
represent a slow moving region in the image I at frame number n.
E. Covariance-Matrix-Based Region Classification
The fifth subalgorithm deals with the classification of the smoke-colored moving regions. We first obtain a
mask from the intersection of the first two subalgorithms and use the obtained smoke-colored moving regions as
the input to the fifth algorithm. The regions are passed as bounding boxes of the connected regions of the mask
[12]. A region covariance matrix consisting of discriminative features is calculated for each region.
F. Wavelet Support Vector Machine
The support vector machine (SVM) is a new universal learning machine, which is applied to both
regression and pattern recognition. A Support Vector Machine (SVM) performs classification by constructing an
N-dimensional hyper plane that optimally separates the data into two categories. Support Vector Machine
(SVM) models are a close cousin to classical multilayer perception neural networks. Using a kernel function,
SVM’s are an alternative training method for polynomial, radial basis function and multi-layer perception
classifiers in which the weights of the network are found by solving a quadratic programming problem with
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4. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
linear constraints, rather than by solving a non-convex, unconstrained minimization problem as in standard
neural network training.
Figure 1: Group of classification
Figure 2: Margin of support vectors
The goal of the WSVMs is to find the optimal approximation or classification in the space spanned by
multidimensional wavelets or wavelet kernels.
Let h(x) be a mother wavelet and let a and c denote the dilation and translation, respectively. x, c, a Є R. If
x, x` Є RN, then dot-product wavelet kernels are
K ( x, x`) = ΠNi=1 h( xi- ci / a) h(x`- c`i/ a)
and translation-invariant wavelet kernels that satisfy the translation invariant kernel theorem are
K ( x, x`) = ΠNi=1 h( xi- x`i / a).
The translation-invariant wavelet kernel by a wavelet function is
h(x) = cos (1.75x) exp (- x2 / 2).
The mother wavelet and the dilation a, a ,x Є R. If x, x` Є RN, the wavelet kernel of this mother wavelet is
K ( x, x`)= ΠNi=1 h( xi- x`i / a)
= ΠNi (cos( 1.75 ×( xi- x`i / a))exp(-║xi- x`i║2/ 2a2))
which is an admissible SV kernel.
From the expression of wavelet kernels, we can take them as a kind of multidimensional wavelet function.
The goal of our WSVM is to find the optimal wavelet coefficients in the space spanned by the multidimensional
wavelet basis.
The estimate function of WSVMs for the approximation
f(x)= ∑1i=1 (αi- α*i)ΠNj=1 h( xj- xji / ai) + b
and the decision function for classification is
f(x)= sgn (∑1i=1 αiyi ΠNj=1 h( xj- xji / ai) + b)
where the xji denotes the jth component of the ith training example.
Decision results of five subalgorithms, D1, D2, D3, D4 and D5 are linearly combined by means of active
decision fusion algorithm to reach a final decision on a given pixel whether it is a pixel of a smoke region or not.
Active Decision Fusion(x,n)
for i = 1 to M do
wi(0) = 1/M ; Initialization
end for
ŷ(x,n) =Pi wi(n)Di(x, n)
if ŷ(x, n) >= 0 then
return 1
else
return -1
end if
e(x, n) = y(x,n) - ŷ(x, n)
for i = 1 to M do
wi(n) ← wi(n) + μ (e(x,n) /║ D(x,n)║2) Di(x,n)
end for
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5. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
Figure 3: The pseudo-code for the active decision fusion algorithm
G. Update Weight
The main wildfire detection algorithm is composed of five subalgorithms. Each algorithm has its own
decision function yielding a zero mean real number for slow moving regions at every image frame of a video
sequence. Decision values from subalgorithms are linearly combined and weights of subalgorithms are
adaptively updated in our approach.
The l1-norm based minimization approaches provide successful signal reconstruction results in
compressive sensing problems. However, the l1-norm-based cost functions used in compressive sensing
problems are not differentiable everywhere. The entropy functional approximates the-norm. Therefore, it can be
used to find approximate l1-norm ∑I|wi(n)| for wi(n) >0 solutions to the inverse problems defined and other
applications requiring l1-norm minimization. Bregman developed convex optimization algorithms in the 1960s,
and his algorithms are widely used in many signal reconstruction and inverse problems [13]. Bregman’s method
provides globally convergent iterative algorithms for problems with convex, continuous, and differentiable cost
functionals as follows:
min W€ C g(w)
Such that DT(x,n)w(n) = y for each time index n
w(n)
w(n+1)
Y(x,n) = DT(x,n)w
Figure 4: Orthogonal Projection: Find the vector w(n+1) on the hyperplane y(x, n) = DT (x, n)w minimizing the distance
between w(n) and the hyperplane.
In the EADF framework, the cost function is
g(w)= ∑iM wi(n) log((wi(n))
Let w(n) denote the weight vector for the nth sample. Its e-projection w* onto a closed convex set C
with respect to a cost functional g(w) is defined as follows:
w* = arg min W€ C L (w, w(n))
Where
L (w, w(n)) = g(w) – g (w(n)) - <Δg(w),w – w(n)>
The e-projection onto the hyperplane H(x,n) leads to the following update equations:
wi(n+1) = wi(n)eλDi(x,n) , i=1, 2,…..,M
Where the Lagrange multiplier λ is obtained by inserting into the hyperplane equation
DT(x,n)w(n+1) = y(x,n)
If the intersection of hyperplanes is an empty set, then the updated weight vector simply satisfies the
last hyperplane equation [14].
for i=1 to M do
wi(0) = 1/M, Initialization
end for
For each sample at time step .
for λ = λmin to λmax do
for i=1 to M do
υ i(n) = wi(n)
υ i(n) ←υi(n)eλDi(x,n)
end for
if ║y(x,n) - ∑i υi(n)Di(x,n)║2 < ║y(x,n) - ∑i wi(n)Di(x,n)║2 then
wT(n) ← v(n)
end if
end for
w(n) ← wT(n)
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6. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
for i=1 to M do
wi(n) ← wi (n) ∕ ∑j wj(n)
end for
ŷ(x,n) = wi(n)Di(x,n)
if ŷ(x,n) >= 0 then
return 1
else
return 1
end if
Figure 5: The pseudocode for the EADF algorithm
For the wildfire detection problem, it is desirable that each subalgorithm should contribute to the compound
algorithm because they characterize a feature of wildfire smoke. Therefore, weights of algorithms should be
between 0 and 1.
V. Experimental Results
The proposed wildfire detection schema based active learning method is compared with one of the
previous smoke detection method using multiple classifier. Decision results of five subalgorithms are linearly
combined by means of active decision fusion algorithm to reach a final decision on a given pixel whether it is a
pixel of a smoke region or not. Results are summarized in table 1 and table 2, in terms of the true detection rates
and the miss detection rates. The true detection rate is defined as:
True Detection Rates
Multiple
Video Frames WSVM
Classifier
V1
679 72.41% 86.62%
V2 300 78.56% 83.45%
V3 450 75.31% 81.89%
V4 1006 68.49% 89.91%
V5 785 74.85% 90.01%
V6 410 70.35% 88.41%
V7 335 65.32% 81.43%
V8 1000 72.30% 93.07%
V9 330 74.46% 82.31%
Table 1
and the miss detection rate is defined as:
Miss Detection Rates
Multiple
Video Frames WSVM
Classifier
V1
679 17.98% 7.78%
V2 300 13.34% 6.43%
V3 450 15.34% 8.56%
V4 1006 11.46% 6.67%
V5 785 14.82% 8.43%
V6 410 12.27% 5.89%
V7 335 9.87% 5.32%
V8 1000 20.34% 9.43%
V9 330 12.48% 7.04%
Table 2
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7. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
(a) Video 1 (b) Video 4
( c) Video 6 (d) Video 8
VI. Conclusion
An EADF is proposed for image analysis and computer vision applications with drifting concepts. This
general framework is applied to a real computer vision problem of wildfire detection. The proposed adaptive
decision fusion strategy takes into account the feedback from guards of forest watch towers. The proposed
framework for decision fusion is suitable for problems with concept drift. At each stage of the algorithm, the
method tracks the changes in the nature of the problem by performing a nonorthogonal e-projection onto a
hyperplane describing the decision of the oracle.
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8. A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in
BL Radhakrishnan received his B.E. Degree in Computer Science and Engineering from
Anna University, Chennai, India. In addition, he received the M.Tech degree in Computer
Science and Engineering from Dr.MGR University, Chennai, India. Currently, he is an
Assistant Professor in the department of Computer Science and Engineering, Marthandam
College of Engineering and Technology, India. His research interests include Image
Processing, Databases and Cloud Computing. He is a Life member of ISTE.
SR Raji received her B.Tech Degree in Computer Science and Engineering from Cochin
University, kerala, India. Currently, she is doing her post graduate course (M.E) in the
department of Computer Science and Engineering, Marthandam College of Engineering and
Technology, India. Her research interests include Image Processing, Networking, Operating
System and Cloud Computing.
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