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nCORETech-2016
NCORETECH-16
🏫 Organized by: LBS College of Engineering, Kasaragod
Character recognition and Period prediction of ancient Kannada Epigraphical scripts
Sachin S Bhat, H.V. Balachandra Achar
LQR based tuning of PID controller for magnetic levitation system and its performance comparison with conventional method
Anupama B., Shijoh Vellayikot
Integrated Bidirectional DC-DC converter for EV charger with G2V, V2G and V2H capabilities
Prasoon Chandran Mavila, Nisha B. Kumar
Design and Analysis of DSTATCOM and Comparison of Various Control Algorithms
Elby Varghese K V, Jayaprakash P
A Review of Reconstruction of Hyperspectral Images from Random Projections
Layana Raj, Nishanth Augustine
A Low Power Control System for Wireless Body Area Networks using Adaptive Fuzzy Logic
Ahana N.K, Rensi Sam Mathew
Efficient Detection and Segmentation of Pulmonary Nodules using Quantization Approach
Laxmi Priya .K
Automated Electroencephalogram Based Advanced Diagnosis of Diseases
Fathimath Shahina C.A, Rensi Sam Mathew
Abstract
Difference of Gaussian on Frame Differenced Image
Risha K.P., Chempak Kumar A., Sindhu C.S.
Abstract:
In this paper, we presented a method to find the edges of a moving objects in video. Moving objects can be detected by using various methods. In this we use frame difference method for moving object detection. After detection of moving object the Difference of Gaussian method is applied to get edges. So by using this method output will be better in less computation.
Keywords:
Background subtraction, Difference of Gaussian, Frame difference, Gradient operator.
Abstract
A Review of Feature Extraction Methods in Content Based Image Retrieval
Naveena A.K, N.K. Narayanan
Abstract:
Image retrieval is an active research area for the last two decades. This area is gaining more importance as the growth of multimedia content over the internet is increasing. Content Based Image Retrieval (CBIR) uses the visual content of images like color, shape and texture for image comparison and retrieval. Compared to the text based retrieval content based image retrieval is used for better accuracy. Content based means search makes use of the visual content of the images. These contents are extracted from the images and are described by multi-dimensional vectors. Similarity measurement and visual feature extraction are two important issues in CBIR. The accuracy of the retrieved images mainly depends on the features extracted. A review of feature extraction methods is present in this paper.
Keywords:
Content Based Image Retrieval, Feature Extraction, Color, Shape, Texture.
Abstract
A Review on MRI Brain Image Segmentation Techniques
Reema Mathew A, Dr.Babu Anto P
Abstract:
Brain tumor is a life threatening disease and its early detection is very important to save life. The tumor region can be detected by segmentation of brain Magnetic Resonance Image (MRI). Once a brain tumor is clinically suspected, radiologic evaluation is required to determine the location, the extent of the tumor, and its relationship to the surrounding structures. This information is very important and critical in deciding between the different forms of therapy such as surgery, radiation, and chemotherapy. The segmentation must be fast and accurate for the diagnosis purpose. Manual segmentation of brain tumors from magnetic resonance images is a tedious and time-consuming task. Also the accuracy depends upon the experience of expert. Hence, the computer aided automatic segmentation has become important. MRI scanned images offer valuable information regarding brain tissues. MRI scans provide very detailed diagnostic pictures of most of the important organs and tissues in our body. It is generally painless and noninvasive. It does not produce ionizing radiation. So MRI is one of the best clinical imaging modalities. Several automated segmentation algorithms have been proposed. But still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. The aim of this research work is to propose and implement an efficient system for tumor detection and classification. The different steps involved in this work are image pre-processing for noise removal, feature extraction, segmentation and classification. Proposed work plans to make a study on different techniques exists for each step and to propose a method well suitable to get an accurate solution.
Keywords:
Tumor, Image segmentation, MRI image, image processing.
Abstract
Developing an algorithm for Tomato leaf disease detection and classification
Vidyaraj K, Priya S
Abstract:
Tomato is widely cultivated economical crop in the India; so diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Therefore, automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring fields of crops, and automatically detect the symptoms of diseases. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. Mostly diseases are seen on the leaves. Therefore, looking for fast, less expensive and accurate method to automatically detect the diseases from the symptoms that appear on the plant leaf is of great realistic significance. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. Hence the algorithm is to design, implement and evaluate an image processing based software solution for automatic detection and classification of plant leaf diseases. The method used in this work is divided into two major phases. First phase concerns with training of healthy sample and diseased sample. Second phase concerns with the training of test sample and generates result based on the segmentation and feature extraction. And classifies the diseases into fungal, bacterial and viral. It also helps the farmer to take superior decision about many aspects of crop development process.
Keywords:
Image Processing, Image Segmentation, Feature Extraction, Disease Detection, Disease Classification.
Abstract
Modularity Based Color Image Segmentation
Reshma S Nair, Vineetha K V
Abstract:
Image segmentation directly influences the successive processing and analyzing of an image. Thus segmentation becomes one of the biggest problems in image processing. Inspired by the application of community detection algorithms in large-scale social networks, we attempt to view an image from the perspective of a network and consider the image segmentation problem as a community detection problem. For a network, modularity is a crucial quantity, which is used to evaluate the performance of various community detection algorithms. A graph based method with modularity, can avoid the over segmentation problem which is present in all traditional segmentation methods. Compared with other existing segmentation algorithms, proposed algorithms start with initial segmentation techniques and lead to the computation of modularity, and try to achieve better segmentation to some extent.
Keywords:
Image Segmentation, Graph based image segmentation, Modularity.
Abstract
Character recognition and Period prediction of ancient Kannada Epigraphical scripts
Sachin S Bhat, H.V. Balachandra Achar
Abstract:
Epigraphs are the important source for reshaping the history and the culture of our ancient civilizations. They have a remarkable importance to mankind. In India, the scripts of modern languages have evolved over a period of time and has finally transformed to the present form. Modern epigraphists find it difficult to interpret the scripts of olden days. The characters have changed over the centuries from one form to another. Therefore, for reading ancient scripts the period of that script has to be determined, so as to have knowledge of which character set of ancient days is to be employed for automatic reading. In this paper we demonstrate period identification of various ancient Kannada scripts using advanced recognition algorithms. Proposed algorithm involves various modules including image acquisition, noise removal, segmentation of character sets for feature extraction, classification and recognition of segmented characters. A system is proposed for prediction of the era and it is being done by examining a few characters in Kannada inscription of various periods referred to as test characters. These test characters are sampled from the script automatically and matched with the characters available for different periods using machine intelligence. This classifier is tested on quite number samples of Kannada epigraphical document images belonging to different periods. Issue taken here is to produce a computer perceivable image from a raw epigraphical script and predict the era of ancient script. Prediction of period of ancient scripts is the first step in automatically deciphering epigraphical scripts. Automatic period identification for a given document image, of a script facilitates the selection of the script specific OCR in an environment where scripts of various periods are given as input.
Keywords:
Inscriptions, Palaeography, period prediction, character segmentation.
Abstract
LQR based tuning of PID controller for magnetic levitation system and its performance comparison with conventional method
Anupama B., Shijoh Vellayikot
Abstract:
This paper proposes a Linear Quadratic Regulator (LQR) based tuning of PID controller and is implemented for controlling the ball position of magnetic levitation system. Since the system is highly nonlinear in nature, nonlinear differential equations are used to model the system. For the controller design, this nonlinear model is linearized around the operating point using Taylor series expansion method. Performance of the proposed method is compared with that of PID controller which is tuned using conventional Ziegler Nichol's method. From the simulation studies it is clear that even though both the schemes are capable to control the ball position of magnetic levitation system the proposed method yields better result. Quantitative performance comparison is also made based on the time domain specifications such as settling time, maximum overshoot and steady state error. The result shows that LQR based PID controller outperforms its counterpart as the settling time and maximum overshoot are reduced considerably.
Keywords:
Proportional-integral-derivative (PID), Linear Quadratic Regulator (LQR), Magnetic Levitation System, Zeigler Nichol's Method (ZN).
Abstract
Block-based Hybrid DWT-SVD Watermarking Technique
Anitha M P, Lijina S S
Abstract:
This paper presents a hybrid image watermarking technique for data hiding over Internet. The idea of the proposed technique is based on fusing multiple watermark images using wavelet fusion algorithm. Then, the resultant fused watermark is embedded in the original image using hybrid DWT - block based SVD watermarking algorithm to produce the watermarked image. The image watermarking technique using the hybrid DWT-block based SVD is more robust than that using the SVD only. By using block based SVD instead of using traditional SVD, the watermarked images that are tampered can also be identified. That is, the extraction of the fused watermark is possible in the presence of severe attacks. Also this watermarking technique improves both the capacity of the embedded information and robustness without affecting the perceptual quality of the original image.
Keywords:
Data Hiding, Wavelet Fusion, Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD).
Abstract
Leakage Current Mitigation in Roof-Top Grid Tied Photo Voltaic Systems
Bibin K. Joseph, Shahin M.
Abstract:
Photovoltaic is desirable due to its ubiquity, abundance, sustainability, ecofriendly nature, cheapness etc. Solar plants are struck by several technical as well as non-technical challenges. Transformer less inverters are highly efficient, light and cost effective. Leakage current is a major threat for transformer less PV systems. Parasitic capacitance and high frequency varying common mode voltage is responsible for leakage current. This cologne current cause losses, radiated interferences, damage of PV array, harmonic distortion in grid and safety disasters. The causes, magnitude, path, detrimental, point of measurement and preventive measures of leakage current are analyzed. Details of leakage current study in a rooftop grid tied systems near to the college is cited to substantiate problem identification. DC decoupling implemented through H5 topology of inverter can mitigate leakage current to great extent. An efficient system is designed with solar panel, MPPT using perturb and observe method utilizing cuk converter and H5 inverter. LLCL filter is used to suppress harmonics. MATLAB simulation is done for 230V, 50 Hz system to get topological verification. The leakage current profile is compared with conventional H bridge, HERIC and H6 topologies. H5 topology gave minimum leakage current.
Keywords:
Leakage current, Common mode voltage, differential mode voltage, parasitic capacitance, transformer less inverter, Maximum Power Point Tracking (MPPT), LLCL filter, hysteresis control.
Abstract
A Review of Advances in Synthetic Aperture Sonar Imaging Algorithms
Jibin George and Vinodkumar
Abstract:
Synthetic aperture techniques use coherent addition over many pings to create an aperture whose extent can be increased with range to maintain a constant along-track resolution. This paper is a review, dealing the past works in, and the recent status of, active Synthetic Aperture Sonar (SAS), covering the early developments in SAS. By constructing the SAS, it can eliminate the huge hardware requirement. A new image recovery method is proposed using the compressive sensing. The proposed method deals with a reconstruction of SAS image using the sparse recovery method. The number of samples for recovery is low when using Compressive Sensing.
Keywords:
Signal processing, Synthetic Aperture Sonar, Compressed sensing, Sparse recovery, Review, SAS.
Abstract
Integrated Bidirectional DC-DC converter for EV charger with G2V, V2G and V2H capabilities
Prasoon Chandran Mavila, Nisha B. Kumar
Abstract:
The importance of Electric vehicles are increasing as they will form major sustainable transportation system in near future with less environmental pollution, fuel economy and energy efficiency. Bi-directional chargers adds the benefit of EVs by enabling energy transfer from vehicle to grid (V2G) or vehicle to home (V2H) in addition to charging from grid to vehicle(G2V). Typically, Bidirectional chargers consists of a AC-DC stage followed by DC-DC stage. This paper presents a non-isolated integrated bi-directional DC-DC converter for interfacing the vehicle battery to the DC link in both charging modes and discharging modes. The DC-DC converter is formed by integrating buck and boost converters and thus it is able to operate in buck as well as boost modes in both direction (charging and discharging). During charging (G2V) the dc link voltage is stepped down by the DC-DC converter to battery voltage and provides required charging current by current control. This is buck operation during charging. During V2G or V2H operation the function of the DC-DC converter is to boost the battery voltage to provide the dc link voltage. As it can also operate in boost mode in charging direction, the converter can charge the battery from a low voltage DC source such as photovoltaic panels by providing it directly to dc stage of the charger.
Keywords:
Bidirectional dc-dc converter, Electric vehicle charger, Vehicle to Grid (V2G), Vehicle to Home (V2H).
Abstract
Seven Level Inverter Topologies: A Comparative Study
Sanoop P., Vinita Chellappan
Abstract:
In the recent years, multilevel inverters (MLI) are highly being used for medium voltage and high power applications due to their various advantages such as low voltage stress on the power switches, low electromagnetic interferences (EMI), low dv/dt ratio to supply lower harmonic contents in the output voltage and current. Multilevel inverters have become more popular over the years in electric high power application with the promise of very low disturbances and the possibility to function at lower switching frequencies than ordinary two-level inverters. This paper presents different topologies, emphasizing mainly on seven level inverters. The different topologies compared are the diode-clamped inverter (neutral-point clamped), capacitor clamped (flying capacitor), and cascaded multi-cell with separate DC sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. Finally a seven level inverter topology with lesser number of switches is discussed and compared along with the other seven level inverter topologies. Simulation studies of diode clamped seven level inverter, cascade seven level inverter and seven level inverter with reduced number of switches are done. The seven level inverter with reduced number of switch with PWM (Pulse Width Modulation) switching is also simulated. It is observed that the pulse generation using PWM switching leads to further reduction of THD.
Keywords:
Multilevel Converter, Multilevel inverter (MLI), Power converters, Total Harmonic Distortion.
Abstract
Design and Analysis of DSTATCOM and Comparison of Various Control Algorithms
Elby Varghese K V, Jayaprakash P
Abstract:
This paper discusses about the three phase three wire distribution static compensator (DSTATCOM) and its performance under various control algorithms. DSTATCOM is a shunt connected advanced power electronic device which provides reactive power compensation, harmonic elimination and source current balancing. It consists of a three phase IGBT inverter module, dc link capacitor, interfacing inductor and the control circuit. Various control algorithms such as Instantaneous Reactive Power Theory (IRPT), Synchronous Reference Frame Theory (SRF), Adaline-based algorithm and Back Propagation control algorithm are compared and its advantage over other are discussed. These control algorithms are used for extracting the reference current signals from the load current to generate the switching pulses for IGBTs of the VSC of the DSTATCOM. Synchronous Reference Frame Theory and Back Propagation control based DSTATCOM are simulated with MATLAB using SIMULINK for various types of loads like linear, nonlinear and unbalanced loads. Linear load used is resistive load and nonlinear load is a three phase diode bridge rectifier feeding a RL load. Simulation results demonstrate the performance of DSTATCOM under these control algorithms.
Keywords:
DSTATCOM, IRP Theory, SRF Method, Adaline-based control algorithm, Back Propagation Control algorithm.
Abstract
A Review of Reconstruction of Hyperspectral Images from Random Projections
Layana Raj, Nishanth Augustine
Abstract:
Hyperspectral imagery provide accurate and detailed information extraction than possible with any other type of remotely sensed data. This improvement comes with computational complexity and over dimensionality. There is an increase in interest in dimensionality reduction through random projections due in part to the emerging paradigm of compressed sensing. CS exploits the fact that many signals are sparse in the sense that they have concise representation in certain basis called dictionary. Traditionally some transform based fixed dictionaries such as DFT, DCT, DWT are used which are relatively easy to analyse. But they are over simplistic and for certain real time data such as hyperspectral images they offer less accuracy. An approach that has been recently proven to be very effective is adaptive dictionary learning technique in which the dictionary is constructed adaptively using the input image for better sparsity. While learning the dictionary the most important computational challenge is the solution of corresponding optimization problem. The reconstruction strategies like compressive projection principal component analysis, multi-hypothesis prediction method and several class dependent strategies were proposed for the reconstruction of hyperspectral imagery from random projections. In this paper, a brief review of various dictionary learning methods and reconstruction techniques for HSI is presented along with their performance evaluation.
Keywords:
Hyper Spectral Images, Compressive Sensing, Principal Component Analysis (PCA), Multi Hypothesis Prediction.
Abstract
A Low Power Control System for Wireless Body Area Networks using Adaptive Fuzzy Logic
Ahana N.K, Rensi Sam Mathew
Abstract:
Wireless body sensor networks (WBSNs) for medical applications, such as vital signal monitoring and the diagnose assistant has received tremendous attention in recent years. Wireless sensing system tends to focus on low power consumption. Firstly, an adaptive fuzzy controller is designed and a statistical analysis of the performance of the system is conducted. An adaptive-resolution control system based on a fuzzy control technique is designed for wireless body sensor networks in order to develop a high quality and low power system. The concept of the adaptive resolution control technique is to produce the control signals by selecting different clock frequencies with fuzzy decision technique. The results show that this work can improve the quality of ECG signals in abnormal region and also reduce transmission power for wireless body sensor networks. Results prove that adaptive fuzzy logic can adapt rapidly and successfully to the changing dynamic situation with which it is presented.
Keywords:
Adaptive, fuzzy logic, health care monitoring, wireless body sensor networks.
Abstract
A Review on Lossless Compression Methods for Binary Images
Nithin P P, Sheeba K
Abstract:
An efficient storage and transmission is a great challenge in digital imaging, since it requires large number of bits to represent an image. Image compression reduces the number of bits needed to represent an image. Lossless compression techniques are used in applications which cannot tolerate any difference between the original and compressed image. Binary image is used in many digital imaging applications such as document imaging, finger print databases and geographical information systems. This paper is a study of various lossless image compression techniques. A comparison of performance of different lossless compression algorithm is also made.
Keywords:
Huffman Coding, Run length Coding, Arithmetic Coding, Lempel-Ziv-Welch Coding.
Abstract
Fused Iris Biometrics for Person Identification
Mithja P, Sambhu D
Abstract:
The conventional authentication methods such as proxy based and biometrics based are not user centric and endangers security and privacy, system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris authentication is a biometric modality which can be used to identify a person. In this paper, proposes a method in which a reference subject (RS) is securely fused with user's biometrics, developing a Biocapsule (BC) from the fused biometrics for authentication. Selection of the reference subject can be a physical RS or a logical RS. The pre-processing techniques are done before the fusion process. Features of user biometrics and RS biometrics are extracted. Individual features make the proposed BC mechanism a user-centric authentication approach. Fusing the transformed user biometrics and RS biometrics, a BC is developed from fused biometrics. The generated BC is matched with BC in the database which is already stored during the enrolment stage. If the matching is true, user identity is correct else wrong identification. Fusion aims to increase the security of the biometrics because through the fusion, user biometrics is hidden by RS biometrics, thus provides secure biometric that is privacy preserving. BC carries no hints that the user is weighted more than the RS and equally treats user and RS. This biometric system possesses various properties such as security, privacy preservation, cross matching resistance, etc.
Keywords:
Authentication, privacy-preserving, Biocapsule (BC), secure fusion, Reference subject (RS).
Abstract
Efficient Detection and Segmentation of Pulmonary Nodules using Quantization Approach
Laxmi Priya .K
Abstract:
Cancer is one of the most serious illnesses in the world, among which lung cancer is the deadliest form. The survival rate of lung cancer in five years is only 54% and the early diagnosis rate is merely 15%. Lung cancer spreads to the different parts of the body rapidly before it is being diagnosed. Therefore the early detection has a crucial role in increasing the survival rate. This paper proposes a vector quantization (VQ) based approach for the detection of lung nodules from computed tomography (CT) scan images. Lungs are extracted from the surrounding tissues using simple thresholding. VQ is performed in two levels, first level of VQ is for lung segmentation and the second level is for the segmentation of lung nodules. Morphological closing operation is performed to refine the lung mask and to ensure the detection of juxta pleural nodules. False positives are reduced using Support vector machine (SVM) classifier. Experimental results shows improved performance comparing to existing computer aided detection (CAD) systems.
Keywords:
Vector quantization (VQ), Computed tomography (CT) scan, False positives (FP), Computer aided detection (CAD), Support vector machine (SVM).
Abstract
A Survey on Fractal Image Compression Techniques
Arjun Purushothaman, Sheeba K
Abstract:
Fractal Image Compression (FIC) is a lossy compression technique, where we can obtain a large amount of compression by representing the image as a contractive transform. The main drawback of FIC is its complexity in encoding and lack of speed. At the same time it has several advantages such as, zooming the image without degrading the quality due to its resolution independent nature. FIC have a good trade off between the compression ratio and PSNR when comparing with other standard image compression techniques. In order to improve the performance of the fractal image compression several methods are adopted. This paper is a study of various fractal image compression methods.
Keywords:
Fractal encoding, Image compression, PSNR, Compression ratio, Discrete Cosine Transform, Discrete Wavelet Transform.
Abstract
Image Steganography Using Discrete Wavelet Transform â A Review
Tushara M, K. A. Navas
Abstract:
Steganography is the art and science of hiding information. Steganography methods are categorised into spatial domain and transform domain methods. Transform domain methods are gaining importance as they provide better security compared to spatial domain methods. Among the other transform domain steganography techniques, techniques that use discrete wavelet transform are becoming increasingly popular because DWT has excellent properties suitable for embedding. This paper presents a review on steganography techniques that use discrete wavelet transform.
Keywords:
Digital image steganography, spatial domain, frequency domain, DWT.
Abstract
Automated Electroencephalogram Based Advanced Diagnosis of Diseases
Fathimath Shahina C.A, Rensi Sam Mathew
Abstract:
Automatic disease detection is important in relieving the heavy workload of examining prolonged electroencephalograph (EEG). Manually Diagnosing disease in EEG is a tedious process and it consumes tens of hours of EEG recording. Early diagnosis and classification of diseases is very important in clinical practice. With recent development in the biomedical engineering and instruments, EEG recording instruments are able to record the electric activities of brain with high accuracy, which founds EEG as a most important tool for diagnosing the abnormalities of brain. This paper represents automated electroencephalogram based advanced diagnosis of diseases using FastICA (fast independent component analysis) and artificial neural networks (ANNs). FastICA is an efficient method to identify artifact and actual EEG from their mixtures. EEG signals carry the information of human brain with artifacts. These artifacts are removed by FastICA algorithm. Further, an ANN is designed to achieve process like a brain. The clean EEG is fed to feed forward back propagation neural network to diagnosis disease. Training parameters and type of neural networks are decided by operators on the interface. Performance of this model is evaluated using overall accuracy.
Keywords:
EEG Signals; Artificial Neural Network; Epilepsy; FastICA; electroencephalograph; ANN; feed forward back propagation.
Abstract
Real Time Face Liveness Detection
Sreenath Narayanan K, Mary Reena K.E
Abstract:
Face recognition is a widely used biometric approach. But face recognition systems are vulnerable to spoof attacks made by non-real faces, where a photo or video of an authorized person's face could be used to gain access to facilities or services. A secure system needs liveness detection in order to guard against such spoofing attacks. Here an efficient real time face liveness detection algorithm based on image distortion analysis (IDA) is proposed. Two different features such as blurriness and chromatic moment are extracted from the image. A fuzzy classifier is used to distinguish between live and spoof faces. The proposed approach is implemented in MATLAB 2013a tool box.
Keywords:
Face recognition, liveness detection, spoofing, image distortion analysis, fuzzy classifier.
Abstract
Area and Delay Efficient DSP Architecture
Deepthi Dinesh Nair, Pramod P
Abstract:
An area and delay efficient Digital Signal Processor (DSP) system is proposed in this paper. The proposed system implements Fast Fourier transform, correlation and convolution on a single platform. For implementing a system with reduced area and delay, a modified carry look ahead adder and array multiplier has been utilized. This complete DSP system is described using VHDL and is synthesized by using Xilinx synthesis tool. The Fast Fourier Transform (FFT) is one of the rudimentary operations in the digital signal and image processing field. Some of the very vital applications of the fast Fourier transform include Sound filtering, Partial differential equations, Signal analysis, Data compression, Image filtering ,Multiplication etc. Fast Fourier transform (FFT) is an highly efficient implementation of the Discrete Fourier Transform (DFT). This paper concentrates on the implementation of the Fast Fourier Transform (FFT), based on Decimation-In-Time (DIT) domain by using Radix-2 algorithm. By utilizing a fixed geometry addressing, block fixed point structure and pipeline designing, the data will acquire higher precision and dynamic range. The results show that the design is strongly extensive, efficient and occupies little resource. This proves to be a good method to meet the digital signal processing requirements at high-speed. In this paper, we have extended the utility of the system towards convolution and correlation applications, which are the most important applications in digital signal processing.
Keywords:
Digital signal processor (DSP), Fast fourier transform, Convolution, Correlation, Carry look ahead adder.
Abstract
A survey of Classical Methods and New Trends in Hyperspectral Unmixing
Anitha.K, Nishanth Augustine
Abstract:
A break through development in remote sensing is Hyperspectral Imaging. Imaging Spectrometers, often referred to as hyperspectral cameras (HSCs) are used for hyperspectral imaging and they acquire images with higher spectral resolution than multispectral cameras. Due to low spatial resolution of HSCs, spectra measured by HSCs are mixtures of spectra of materials in a scene and each pixel is assumed to be a mixture of few materials, called endmembers. This necessitates unmixing which involves estimating the number of endmembers, their spectral signatures and their abundances at each pixel. Various algorithms like HYSIME, VCA, DECA, NMF, N-Finder were introduced for hyperspectral unmixing. A sparse regression scheme based on compressive sensing is also used for identifying pure form of pixels of a scene. This approach reduces the number of endmembers needed to represent the data and provides more robust solutions. A collaborative Sparse Regression method is also developed which can be implemented in parallel nature and thus improves the speed of operation and accuracy. In this paper a brief study of various unmixing algorithms were presented along with a comparison of their performance.
Keywords:
Hyperspectral Imaging, Hyperspectral Unmixing (HU), endmembers, Compressive sensing, Hysime, VCA, DECA, Spectral library.
Abstract
An Efficient DLMS Adaptive Filter Architecture
Swathi K V, Pramod P
Abstract:
An efficient adaptive filter architecture using booth multiplier has been developed. For deriving low-complexity and high-speed implementation a precise analysis of the critical path of the least-mean-square (LMS) adaptive filter has been done in this paper. For achieving area-delay-power efficient implementation and lower adaptation-delay, we use efficient partial product generator and propose a method for optimization of the time-consuming combinational blocks of the structure by using pipeline stages. We proposed an area-delay-power efficient lower adaptation delay architecture for implementation of LMS adaptive filter. We used partial product generator for efficient implementation of bitwise multiplications and inner-product computation by sharing the common sub expressions. In multiplier major part of the delay is contributed by partial product addition. In order to reduce the delay Carry look ahead adder is used as the adder to perform partial product addition in the adder tree. The propagation delay occurred in the parallel adders can be eliminated by carry look ahead adder. The proposed structures involves significantly less adaptation delay and provide significant saving of ADP compared to the existing structures. We use Xilinx 14.7 to provide VHDL coding for our architecture. Result shows that proposed structure has better performance than existing algorithms.
Keywords:
Adaptive filter, Least Mean Square, Carry Look Ahead Adder, Booth Multiplier.
Abstract
Evaluation of Reversible Image Data Hiding with Contrast Enhancement
Athira K
Abstract:
Reversible data hiding (RDH) embeds a piece of information into a host signal to generate a marked one, so that the original signal is exactly recovered after the extraction of embedded data. For the images obtained with poor illumination, visual quality is more important than high PSNR value. The DH algorithm keeps the PSNR value high and enhances the contrast of the host image to improve the visual quality. The highest two bins in the histogram of the input image are shifted for data embedding, such that histogram equalization can also performed simultaneously by repeating the embedding process. The original image is completely recoverable by embedding side information along with the message bits to form a host image. Evaluation of images is an important step after data hiding, for determining how much the contrast has been enhanced. Quality of image is usually assessed using image quality metrics relative contrast error (RCE), relative entropy error (REE), relative mean brightness error (RMBE), relative structural similarity (RSS), peak signal to noise ratio (PSNR) and global contrast factor (GCF). This paper is a study of the various quantitative metrics for evaluating contrast enhancement. The results sho
Keywords:
Reversible data hiding, Contrast Enhancement, Histogram binning, Steganography.