TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS”

TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS” INTRODUCTION Wireless communication and MEMS - the two technologies which have revolutionalized the way we live have also resulted in the development of wireless sensor networks. These comprise of relatively inexpensive sensor nodes capable of collecting, processing, storing and transferring information from one node to another. These nodes are able to autonomously form a network through which sensor readings can be propagated. Since the sensor nodes have some intelligence, data can be processed as it flows through the network. The latter is being done wirelessly these days using networking principles. The flexibility of installation and configuration has greatly improved resulting in a flurry of research activities commencing in the field of sensor networks owing to their ready acceptance in various industries such as security, telecommunications and automobile to name a few. By early next century, sensor integration, coupled with unceasing electronic miniaturization, will make it possible to produce extremely inexpensive sensing devices. These devices will be able to monitor a wide variety of ambient conditions: temperature, pressure, humidity, soil makeup, vehicular movement, noise levels, lighting conditions, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects and so on. These devices will also be equipped with significant processing, memory and wireless communication capabilities. Emerging low-level and low-power wireless communication protocols will enable us to network these sensors. This capability will add a new dimension to the capabilities of sensors: Sensors will be able to coordinate amongst themselves on a higher-level sensing task (e.g., reporting, with greater accuracy than possible with a single sensor, the exact speed, direction, size, and other characteristics of an approaching vehicle). OBJECTIVE The problem being tackled here relates to the problem of target tracking in wireless sensor networks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. METHODOLOGY A location Tracking Protocol in which the tracking is done by the coordination of sensors. A network of sensors in a 2D plane is considered. A triangular network is considered i.e. the sensors are placed in a triangular fashion. Typically, the network is considered as a hexagonal mesh. Each sensor is aware of its physical location and that of its neighboring sensors. All the sensors have a processor, a memory and required hardware to support sensing, information gathering and communication capabilities. Each sensor has a sensing radius, r which is equal to the length of the side of the triangle. Three sensors are used to determine the location of the object. The methodology used in this case is the triangulation technique of detecting the spatial coordinates. The sensors in this case are assumed to have different sensing radii. POSSIBLE FINDINGS Applications of target tracking and/or data fusion are found in diverse civilian and military fields. Civilian applications include air traffic control, navigation, fault tolerant systems and decision problems. In the military field, applications include surveillance, target identification, command and control, sensor management and weapon guidance. The target tracking problem is widely researched due to its increasing applications in security industry due to heightened concerns about the safety of men and material in present day world. In order to keep a check on movements of suspicious people and their activities, we have to employ video monitoring and surveillance and tracking systems.

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TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS”

TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS” INTRODUCTION Wireless communication and MEMS - the two technologies which have revolutionalized the way we live have also resulted in the development of wireless sensor networks. These comprise of relatively inexpensive sensor nodes capable of collecting, processing, storing and transferring information from one node to another. These nodes are able to autonomously form a network through which sensor readings can be propagated. Since the sensor nodes have some intelligence, data can be processed as it flows through the network. The latter is being done wirelessly these days using networking principles. The flexibility of installation and configuration has greatly improved resulting in a flurry of research activities commencing in the field of sensor networks owing to their ready acceptance in various industries such as security, telecommunications and automobile to name a few. By early next century, sensor integration, coupled with unceasing electronic miniaturization, will make it possible to produce extremely inexpensive sensing devices. These devices will be able to monitor a wide variety of ambient conditions: temperature, pressure, humidity, soil makeup, vehicular movement, noise levels, lighting conditions, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects and so on. These devices will also be equipped with significant processing, memory and wireless communication capabilities. Emerging low-level and low-power wireless communication protocols will enable us to network these sensors. This capability will add a new dimension to the capabilities of sensors: Sensors will be able to coordinate amongst themselves on a higher-level sensing task (e.g., reporting, with greater accuracy than possible with a single sensor, the exact speed, direction, size, and other characteristics of an approaching vehicle). OBJECTIVE The problem being tackled here relates to the problem of target tracking in wireless sensor networks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. METHODOLOGY A location Tracking Protocol in which the tracking is done by the coordination of sensors. A network of sensors in a 2D plane is considered. A triangular network is considered i.e. the sensors are placed in a triangular fashion. Typically, the network is considered as a hexagonal mesh. Each sensor is aware of its physical location and that of its neighboring sensors. All the sensors have a processor, a memory and required hardware to support sensing, information gathering and communication capabilities. Each sensor has a sensing radius, r which is equal to the length of the side of the triangle. Three sensors are used to determine the location of the object. The methodology used in this case is the triangulation technique of detecting the spatial coordinates. The sensors in this case are assumed to have different sensing radii. POSSIBLE FINDINGS Applications of target tracking and/or data fusion are found in diverse civilian and military fields. Civilian applications include air traffic control, navigation, fault tolerant systems and decision problems. In the military field, applications include surveillance, target identification, command and control, sensor management and weapon guidance. The target tracking problem is widely researched due to its increasing applications in security industry due to heightened concerns about the safety of men and material in present day world. In order to keep a check on movements of suspicious people and their activities, we have to employ video monitoring and surveillance and tracking systems.

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TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS”

TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS” INTRODUCTION Wireless communication and MEMS - the two technologies which have revolutionalized the way we live have also resulted in the development of wireless sensor networks. These comprise of relatively inexpensive sensor nodes capable of collecting, processing, storing and transferring information from one node to another. These nodes are able to autonomously form a network through which sensor readings can be propagated. Since the sensor nodes have some intelligence, data can be processed as it flows through the network. The latter is being done wirelessly these days using networking principles. The flexibility of installation and configuration has greatly improved resulting in a flurry of research activities commencing in the field of sensor networks owing to their ready acceptance in various industries such as security, telecommunications and automobile to name a few. By early next century, sensor integration, coupled with unceasing electronic miniaturization, will make it possible to produce extremely inexpensive sensing devices. These devices will be able to monitor a wide variety of ambient conditions: temperature, pressure, humidity, soil makeup, vehicular movement, noise levels, lighting conditions, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects and so on. These devices will also be equipped with significant processing, memory and wireless communication capabilities. Emerging low-level and low-power wireless communication protocols will enable us to network these sensors. This capability will add a new dimension to the capabilities of sensors: Sensors will be able to coordinate amongst themselves on a higher-level sensing task (e.g., reporting, with greater accuracy than possible with a single sensor, the exact speed, direction, size, and other characteristics of an approaching vehicle). OBJECTIVE The problem being tackled here relates to the problem of target tracking in wireless sensor networks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. METHODOLOGY A location Tracking Protocol in which the tracking is done by the coordination of sensors. A network of sensors in a 2D plane is considered. A triangular network is considered i.e. the sensors are placed in a triangular fashion. Typically, the network is considered as a hexagonal mesh. Each sensor is aware of its physical location and that of its neighboring sensors. All the sensors have a processor, a memory and required hardware to support sensing, information gathering and communication capabilities. Each sensor has a sensing radius, r which is equal to the length of the side of the triangle. Three sensors are used to determine the location of the object. The methodology used in this case is the triangulation technique of detecting the spatial coordinates. The sensors in this case are assumed to have different sensing radii. POSSIBLE FINDINGS Applications of target tracking and/or data fusion are found in diverse civilian and military fields. Civilian applications include air traffic control, navigation, fault tolerant systems and decision problems. In the military field, applications include surveillance, target identification, command and control, sensor management and weapon guidance. The target tracking problem is widely researched due to its increasing applications in security industry due to heightened concerns about the safety of men and material in present day world. In order to keep a check on movements of suspicious people and their activities, we have to employ video monitoring and surveillance and tracking systems.

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TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS”

TITLE –“ TARGET TRACKING IN WIRELESS SENSOR NETWORKS” INTRODUCTION Wireless communication and MEMS - the two technologies which have revolutionalized the way we live have also resulted in the development of wireless sensor networks. These comprise of relatively inexpensive sensor nodes capable of collecting, processing, storing and transferring information from one node to another. These nodes are able to autonomously form a network through which sensor readings can be propagated. Since the sensor nodes have some intelligence, data can be processed as it flows through the network. The latter is being done wirelessly these days using networking principles. The flexibility of installation and configuration has greatly improved resulting in a flurry of research activities commencing in the field of sensor networks owing to their ready acceptance in various industries such as security, telecommunications and automobile to name a few. By early next century, sensor integration, coupled with unceasing electronic miniaturization, will make it possible to produce extremely inexpensive sensing devices. These devices will be able to monitor a wide variety of ambient conditions: temperature, pressure, humidity, soil makeup, vehicular movement, noise levels, lighting conditions, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects and so on. These devices will also be equipped with significant processing, memory and wireless communication capabilities. Emerging low-level and low-power wireless communication protocols will enable us to network these sensors. This capability will add a new dimension to the capabilities of sensors: Sensors will be able to coordinate amongst themselves on a higher-level sensing task (e.g., reporting, with greater accuracy than possible with a single sensor, the exact speed, direction, size, and other characteristics of an approaching vehicle). OBJECTIVE The problem being tackled here relates to the problem of target tracking in wireless sensor networks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. METHODOLOGY A location Tracking Protocol in which the tracking is done by the coordination of sensors. A network of sensors in a 2D plane is considered. A triangular network is considered i.e. the sensors are placed in a triangular fashion. Typically, the network is considered as a hexagonal mesh. Each sensor is aware of its physical location and that of its neighboring sensors. All the sensors have a processor, a memory and required hardware to support sensing, information gathering and communication capabilities. Each sensor has a sensing radius, r which is equal to the length of the side of the triangle. Three sensors are used to determine the location of the object. The methodology used in this case is the triangulation technique of detecting the spatial coordinates. The sensors in this case are assumed to have different sensing radii. POSSIBLE FINDINGS Applications of target tracking and/or data fusion are found in diverse civilian and military fields. Civilian applications include air traffic control, navigation, fault tolerant systems and decision problems. In the military field, applications include surveillance, target identification, command and control, sensor management and weapon guidance. The target tracking problem is widely researched due to its increasing applications in security industry due to heightened concerns about the safety of men and material in present day world. In order to keep a check on movements of suspicious people and their activities, we have to employ video monitoring and surveillance and tracking systems.

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Project Based on MATLAB IEEE 2014

OMM SOFTWARE INNOVATION PVT LTD Company URL-www.ommsoftware.com Phone No: 09021557095 IMAGE PROCESSING IEEE 2014 Sr.NO Project Title 1.Image quality assessment: from error visibility to structural similarity 2.Image Super-Resolution Via Sparse Representation 3.Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising 4.Region filling and object removal by exemplar-based image inpainting 5.Hyperspectral Image Classification Through Bilayer Graph-Based Learning 6.Robust Online Multiobject Tracking With Data Association and Track Management 7.Patch-Ordering-Based Wavelet Frame and Its Use in Inverse Problems 8.QR Images: Optimized Image Embedding in QR Codes 9.A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition 10.Phase-Based Binarization of Ancient Document Images: Model and Applications 11.Fast Generic Polar Harmonic Transforms 12.Scene Text Recognition in Mobile Applications by Character Descriptor and Structure Configuration 13.OSRI: A Rotationally Invariant Binary Descriptor 14.BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification 15.Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling 16.Progressive Image Denoising 17.Rate-Constrained 3D Surface Estimation From Noise-Corrupted Multiview Depth Videos 18.LBP-Based Edge-Texture Features for Object Recognition 19.Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition 20.On Scanning Linear Barcodes From Out-of-Focus Blurred Images: A Spatial Domain Dynamic Template Matching Approach OMM SOFTWARE INNOVATION PVT LTD Company URL-www.ommsoftware.com Phone No: 09021557095 21.Abandoned Object Detection System. 22.Novel PCB defect and Damaged Component Analysis using Image Processing. 23.Image processing based Automated Guided Vehicle. 24.Analysis of transformer oil by using Image Processing. 25.Cost effective smart remote controller based on invisible IR-LED using image processing. 26.Automatic inspection of outdoor insulators using image processing and intelligent techniques. 27.Consumer electronics-based intelligent alert system for unattended elderly residents. 28.Disk Space Optimization during video recording in continuous surveillance systems. 29.Real-time intelligent alarm system of driver fatigue based on video sequences. 30.Real world automated detection of traffic violations. 31.Automatic system for determination of blood types using image processing techniques. 32.Image authentication and restoration by multiple watermarking techniques with advance encryption standard in digital photography. 33.Multi-sensor Image Fusion 34.Image Steganography using BPCS (Bit Plane Complexity Steg.) System 35.Image Steganography using TBPC (Tree Based Parity Check) System. 36.Separable reversible encrypted data hiding in encrypted image using AES Algorithm and Lossy technique. 37.Innovative algorithms for vision defect identification system. 38.Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection 39.Energy saver with localised occupancy detection based on image processing. 40.An Automated Recognition of fake or destroyed Indian Currency note in machine vision. OMM SOFTWARE INNOVATION PVT LTD Company URL-www.ommsoftware.com Phone No: 09021557095 41.Recognition of human actions using Motion History Information extracted from the compressed video. 42.A Digital Image Stabilization Method Based on the Hilbert–Huang Transform. 43.A novel detection approach using bio-inspired vision for enhanced object. 44.A novel approach of assisting the visually impaired to navigate path and avoiding obstacle collisions. 45.Video Segmentation using Dynamic Texture Linear Binary Pattern (DT-LBP). 46.Photographic Enhancement Using Multiple Exposure Images. 47.Offline Signature Verification using Grid based Feature Extraction. 48.Vehicle Classification and Measurement using Image Processing. 49.Vehicle License Plate Detection and Recognition Using VEDA algorithm. 50.Scene Text Detection via Connected Component Clustering and Nontext Filtering 51.Counting objects in an image by marker controlled watershed segmentation and thresholding. 52.Image Processing Based Traffic Control System. 53.Fire Detection using LAB colour-space. 54.Fruit Quality Management System using K-Means Clustering. 55.Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns. 56.Seed Quality Analysis using Image Processing. 57.Leaf Disease Recognition. 58.Digital watermarking using Discrete Wavelet Transform and Principal Component Analysis. 59.Device Control using hand gestures. 60.American Sign Language Recognition using image processing. OMM SOFTWARE INNOVATION PVT LTD Company URL-www.ommsoftware.com Phone No: 09021557095 61.Diabetes Recognition using Iridology. 62.Non-invasive Haemoglobin Detection Using image Processing. 63.Iris Recognition using Phase Based Matching. 64.Detection of abnormalities in retinal images. 65.Face Recognition based Car-Locking System. 66.User Authentication Using Keyboard and Mouse Dynamics. 67.Personal Authentication Using finger Knuckle. 68.Personal Authentication Using Finger print. 69.Malaria Parasite Detection using SVM Classifier. 70.Mouse Pointer Control using hand gestures. 71.Lung Cancer Detection using Watershed Segmentation. 72.Automatic retrival of MRI brain image using Multiqueries system. 73.Brain Tumour Detection using CT and MRI image fusion. 74.Tissue density classification in mammographic images using local features. 75.Breast Cancer Detection using SVM Classifier. 76.Change Detection in SAR images using fuzzy Clustering. 77.Image Morphing. 78.Fabric Textile Defect Detection using Wavelet Coefficients. 79.Performance evaluation of traditional and adaptive lifting based wavelets with SPIHT for lossy image compression. 80.Application of temperature compensated ultrasonic ranging for blind person and verification using MATLAB. 81.Super-pixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening. 82.Colour and texture feature-based image retrieval by using hadamard matrix in discrete wavelet transform OMM SOFTWARE INNOVATION PVT LTD Company URL-www.ommsoftware.com Phone No: 09021557095 SPEECH, SIGNAL and COMMUNICATION Sr.NO Project Title 83.Acoustic interference cancellation for a voice-driven interface in smart TVs 84.Signal detection for cognitive radar. 85.Least-Mean-Square algorithm based adaptive filters for removing power line interference from ECG signal. 86.Wavelet-based analysis for heart sound monitoring system. 87.Automated pitch-based gender recognition using an adaptive neuro-fuzzy inference system. 88.Fuzzy rule based voice emotion control for user demand speech generation of emotion robot. 89.Speech recognition based wireless automation of home loads with fault identification for physically challenged. 90.Wind Signal Forecasting Based on System Identification Toolbox of MATLAB. 91.Advanced modulation formats for free-space laser communication. 92.A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid 93.On Spatial Domain Cognitive Radio Using Single-Radio Parasitic Antenna Arrays. 94.Full-dimension MIMO (FD-MIMO) for next generation cellular technology. 95.Modelling of OFDM-ODSB-FSO Transmission System under Different Weather Conditions. 96.Accelerometer Based Digital Pen for Handwritten Digit and Gesture Recognition 97.Extraction of FECG signal using ICA (Independent Component Analysis). 98.ECG Signal Classification 99.EEG Signal Classification using PSD Features. 100.Acceleration Sensor based Mouse. 101.Device Control Using Speech Recognition. 102.Speech Emotion Recognition. 103.Drowsiness Detection Using EEG Signals. 104.Simulation of OFDM System in Matlab (BER,Multipath,PAPR) 105.Simulation Of smart Antenna using Beamforming Technique.

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