DSP PROJECTS - ABSTRACTS
A 2D Discrete Wavelet Transform Based 7-State Hidden Markov Model for Efficient Face Recognition
A novel Discrete Wavelet Transform (DWT) based on 7-States of Hidden Markov Model (HMM) for Face Recognition (FR) is proposed in this paper. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence vectors extraction.
Extensive experiments have been conducted in our database and the FERET database shows that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available. As a novel point despite of five-state HMM used in pervious researches, we propose to use 7-state HMM to cover more specific details.
A pre-processing procedure is introduced to reduce the complexity of the proposed system. It is evident from the outcome of these experiments that more information during training yield better results.
A Fatigue Detection System with Eyeglasses Removal
A fatigue detection system is designed to alert the driver and thus prevent the possible accidents. The detection is mainly based on the result of the face/eye detection or the pupil of the eyeball to determine whether the driver in the fatigue condition or not. Most of the previous studies assume that the driver does not wear the eyeglasses. However, the eye detection is easily influenced by the eyeglasses and thus decreases the correct detection ratio.
In order to overcome the influence of the eyeglasses, a fatigue detection method with eyeglass removal is proposed in this paper. Firstly, the face area is detected by using the functions in the OpenCV library. Then, the eyeglasses are removed by using the morphological operations. After eyeglasses removal, the eye areas are detected by using the functions in the OpenCV library and tracking by using a template matching method. The binarization result of the eye area is performed the horizontal projection and Kalman filter.
Then, the open/close state of eyes is determined, and then fatigue is determined based on the series state of eyes. Four testing videos are used to evaluate the performance of the proposed method. The average correct detection ratio of the eye state is 88.5% and the fatigue detection can reach 100%. The preliminary results show that the proposed method is feasible.
A Novel Approach for Face Detection using Artificial Neural Network
In recent time face detection is of utmost importance because for its various applications. Several approaches have been implemented to date. This paper aims towards an effort to represent a novel approach for human face recognition. The proposed system consists merging both frequency and spatial domain techniques.
The proposed system selects the Region of Interest on which Ripplet Transformation is to be applied after power law transformation to calculate Standard Deviation (SD) and Mean as features.
At later stage, Feed Forward Back Propagation Neural Network (FFBPNN) is used for classification and recognition purpose. The approach is tested with non face images to show its effectiveness which is around 91.67%.
A Robust QR- Code Video Watermarking Scheme Based On SVD and DWT Composite Domain
Nowadays, Digital video is one of the popular multimedia data exchanged in the internet. Commercial activity on the internet and media require protection to enhance security. The 2D Barcode with a digital watermark is a widely interesting research in the security field.
In this paper propose a video watermarking with text data (verification message) by using the Quick Response (QR) Code technique. The QR Code is prepared to be watermarked via a robust video watermarking scheme based on the (singular value decomposition)SVD and (Discrete Wavelet Transform)DWT. In addition to that logo (or) watermark gives the authorized ownership of video document. SVD is an attractive algebraic transform for watermarking applications.
SVD is applied to the cover I-frame. The extracted diagonal value is fused with logo (or) watermark. DWT is applied on SVD cover image and QR code image. The inverse transform on watermarked image and add the frame into video this watermarked (include logo and QR code image) the video file sends to authorized customers.
In the reverse process check the logo and QR code for authorized ownership. These experimental results can achieved acceptable imperceptibility and certain robustness in video processing.
A Survey on the Successive Interference Cancellation Performance for Single-Antenna and Multiple-Antenna OFDM Systems
Interference plays a crucial role for performance degradation in communication networks nowadays. An appealing approach to interference avoidance is the Interference Cancellation (IC) methodology. Particularly, the Successive IC (SIC) method represents the most effective IC-based reception technique in terms of Bit-Error-Rate (BER) performance and, thus, yielding to the overall system robustness.
Moreover, SIC in conjunction with Orthogonal Frequency Division Multiplexing (OFDM), in the context of SIC-OFDM, is shown to approach the Shannon capacity when single-antenna infrastructures are applied while this capacity limit can be further extended with the aid of multiple antennas.
Recently, SIC-based reception has studied for Orthogonal Frequency and Code Division Multiplexing or (spread-OFDM systems), namely OFCDM. Such systems provide extremely high error resilience and robustness, especially in multi-user environments. In this paper, we present a comprehensive survey on the performance of SIC for single- and multiple-antenna OFDM and spread OFDM (OFCDM) systems.
Thereby, we focus on all the possible OFDM formats that have been developed so far. We study the performance of SIC by examining closely two major aspects, namely the BER performance and the computational complexity of the reception process, thus striving for the provision and optimization of SIC.
Our main objective is to point out the state-of-the-art on research activity for SIC-OF(C)DM systems, applied on a variety of well-known network implementations, such as cellular, ad hoc and infrastructure-based platforms. Furthermore, we introduce a Performance-Complexity Tradeoff (PCT) in order to indicate the contribution of the approaches studied in this paper.
Finally, we provide analytical performance comparison tables regarding to the surveyed techniques with respect to the PCT level.
Adaptive Local Tone Mapping Based on Retinex for High Dynamic Range Images
In this paper, we present a new tone mapping technique for high dynamic range images based on the retinex theory. Our algorithm consists of two steps, global adaptation and local adaptation of the human visual system.
In the local adaptation process, the Gaussian filter of the retinex algorithms is substituted with a guided filter to reduce halo artifacts. To guarantee good rendition and dynamic range compression, we propose a contrast enhancement factor based on the luminance values of the scene.
In addition, an adaptive nonlinearity offset is introduced to deal with the strength of the logarithm function's nonlinearity. Experiments show that our algorithm provides satisfactory results while preserving details and reducing halo artifacts.
An Adaptive Blind Video Watermarking Technique based on SD-BPSO and DWT-SVD
Data Exchange in the form of multimedia has seen a tremendous increase in the past decade thereby requiring a need for better security and protection for proprietary rights. Blind Watermarking is a well-established authentication technique and in this paper, we propose a novel algorithm for watermarking videos.
The proposed concepts include Adaptive Frame Selection using SD-BPSO to ensure the watermarks have least detrimental effects on the video as a whole. The integrity of the video is validated using the Peak Signal to Nose Ratio (PSNR).
The robustness of the algorithm is also tested by subjecting the videos to several standard attacks such as - rotation, cropping, image shift, histogram equalization and image sharpening. Bit Error Rate (BER) is also used, in order to determine the efficiency of the system in retaining the watermark.
Analysis of Multispectral Image Using Discrete Wavelet Transform
In this paper we have analyzed the discrete wavelet transform of multispectral image of Bareilly region using MatLab tool. The wavelet transform is one of the most useful computational tools for a variety of signal and image processing applications.
The wavelet transform is used for the compression of digital image because smaller data are important for storing images using less memory and for transmitting images faster and more reliably. Wavelet transforms are useful for images to reduce unwanted noise and blurring.
A discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. A key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and time domain information.
Binary Plane Technique for Super Resolution Image Reconstruction Using Integer Wavelet Transform
Super Resolution (SR) image reconstruction is the process of producing a high resolution (HR) image from many low resolution (LR) images. SR image reconstruction can be considered as the second generation restoration technique.
In this paper we propose SR image reconstruction from clean, noisy and blurred images using binary plane technique (BPT) encoding and Integer wavelet Transform (IWT).
Integer wavelet transform maps an integer data set into another integer data set. Objective and subjective analysis of the reconstructed image has a better super resolution factor and a higher qualitative metrics.
Brain Tumor Classification Using Discrete Cosine Transform and Probabilistic Neural Network
In this paper, a new method for Brain Tumor Classification using Probabilistic Neural Network with Discrete Cosine Transformation is proposed. The conventional method for computerized tomography and magnetic resonance brain images classification and tumor detection is by human inspection.
Operator assisted classification methods are impractical for large amounts of data and are also non reproducible. Computerized Tomography and Magnetic Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies in classification. The use of Neural Network techniques shows great potential in the field of medical diagnosis.
Hence, in this paper the Probabilistic Neural Network with Discrete Cosine Transform was applied for Brain Tumor Classification. Decision making was performed in two steps, i) Dimensionality reduction and Feature extraction using the Discrete Cosine Transform and ii) classification using Probabilistic Neural Network (PNN). Evaluation was performed on image data base of 20 Brain Tumor images.
The proposed method gives fast and better recognition rate when compared to previous classifiers. The main advantage of this method is its high speed processing capability and low computational requirements.
Children Detection Algorithm Based on Statistical Models and LDA in Human Face Images
Advances in different software and systems, and the everyday increasing demand on internet networks highlights the need for a system being able to give service to its clients based on their age. Children and adolescents are the most vulnerable group in the society. Therefore we have to look for an algorithm that can categorize immature from adult. I
n this paper, a practical algorithm in children classification from adults by their facial image is proposed. In this algorithm, statistical modelling of the face is used to extract the age dependent face features and then by applying Linear Discriminant Analysis (LDA) on the face parameters, useful specifications are extracted.
By transferring into a one dimension feature space Euclidean distance is used as a dissimilarity function. The proposed algorithm obtains accuracy rate of 85% on a standard FG-NET aging face database.
Compare the performance analysis for FFT based MIMO-OFDM with DWT based MIMO-OFDM
In that paper examines the enactment of ripple (otherwise called as wavelet) based Multi-user MIMO OFDM systems and also compare with the performance of FFT based MIMO OFDM. Wavelet based OFDM has lot of advantages compare to the FFT based OFDM. There is no need for cyclic prefix, flexibility and optimal resolution.
Wavelets want existed suitably in almost absolutely the arenas of wireless communication schemes with OFDM which is a durable applicant for next peers of wireless scheme. That is third generation of partnerships project (3GPP) networks.
Simulation created examination will be jumble-sale to simulate the double multicarrier schemes, DWT with Haar mother constructed multicarrier in addition the predictable OFDM, less than the consequence of taking multiple antennas scheme, by BPSK also QPSK as dual modulation schemes in additive white Gaussian noise channel (AWGN).
Established on the bit error rate presentation to the transmission ability, the DWT constructed multicarrier scheme was establish to be higher to the predictable OFDM scheme.
Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images
This letter presents a novel contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. The proposed algorithm computes brightness-adaptive intensity transfer functions using the low-frequency luminance component in the wavelet domain and transforms intensity values according to the transfer function.
More specifically, we first perform discrete wavelet transform (DWT) on the input images and then decompose the LL subband into low-, middle-, and high-intensity layers using the log-average luminance. Intensity transfer functions are adaptively estimated by using the knee transfer function and the gamma adjustment function based on the dominant brightness level of each layer.
After the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. Although various histogram equalization approaches have been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions.
The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques.
The proposed method can effectively enhance any low-contrast images acquired by a satellite camera and is also suitable for other various imaging devices such as consumer digital cameras, photorealistic 3-D reconstruction systems, and computational cameras.
Design and implementation of an automatic traffic sign recognition system on TI Processor
This paper discusses the design and processor implementation of a system that detects and recognizes traffic signs present in an image. Morphological operators, segmentation and contour detection are used for isolating the Regions of Interest (ROIs) from the input image, while five methods - Hu moment matching, histogram based matching, Histogram of Gradients based matching, Euclidean distance based matching and template matching are used for recognizing the traffic sign in the ROI.
A classification system based on the shape of the sign is adopted. The performance of the various recognition methods is evaluated by comparing the number of clock cycles used to run the algorithm on the Texas Instruments TMS320C6748 processor.
The use of multiple methods for recognizing the traffic signs allows for customization based on the performance of the methods for different datasets. The experiments show that the developed system is robust and well-suited for real-time applications and achieved recognition and classification accuracies of upto 90%.
Digital watermarking using DWT and DES
The digital data are transmitted using the Internet. So digital data must be secure, copyright protected, and authenticated at the same time. This paper proposes an algorithm to protect digital data by embedding watermark that is encrypted by DES algorithm. Two level discrete wavelet transformation (DWT) is applied to the original image. This ensure robustness of the proposed scheme.
DES encryption to the watermark with a key and iterating operations ensure security of the watermark information. Encryption and decryption key is same for both the process. If we want to extract the watermark image, we must obtain the secret key. The experimental result shows that the watermark is robust against various attacks.
Digital Watermarking with copyright authentication for image communication
The advancing world of digital multimedia communication is faces problems related to security and authenticity of digital data. In the context of multimedia communication, digital images and videos have numerous applications in entertainment world like TV channel broadcasting.
Digital Watermarking algorithms used to protect the copyright of digital images and to verify multimedia data security. Most watermarking algorithms transform the host image and embedding of the watermark information by robust way. Uncompressed digital images need a lot storage capacity and bandwidth so efficient image transmission need image compression.
The solution is becoming more complex with the growth of data. We propose Digital Watermarking by proposed transform Algorithm based on DCT-DWT watermarking. By this method we can do secure image transmission.
Discrete Wavelet Transform and Data Expansion Reduction in Homomorphic Encrypted Domain
Signal processing in the encrypted domain is a new technology with the goal of protecting valuable signals from insecure signal processing. In this paper, we propose a method for implementing discrete wavelet transform (DWT) and multiresolution analysis (MRA) in homomorphic encrypted domain.
We first suggest a framework for performing DWT and inverse DWT (IDWT) in the encrypted domain, then conduct an analysis of data expansion and quantization errors under the framework. To solve the problem of data expansion, which may be very important in practical applications, we present a method for reducing data expansion in the case that both DWT and IDWT are performed.
With the proposed method, multilevel DWT/IDWT can be performed with less data expansion in homomorphic encrypted domain. We propose a new signal processing procedure, where the multiplicative inverse method is employed as the last step to limit the data expansion.
Taking a 2-D Haar wavelet transform as an example, we conduct a few experiments to demonstrate the advantages of our method in secure image processing. We also provide computational complexity analyses and comparisons. To the best of our knowledge, there has been no report on the implementation of DWT and MRA in the encrypted domain.
Dual Transform Based Steganography Using Wavelet Families and Statistical Methods
Steganography is the discipline of exchanging top secret information by embedding it into a multimedia carrier. The ultimate aim, here is to hide the very existence of the embedded information within seemingly innocuous carriers.
The proposed method extracts either Discrete Wavelet Transform (DWT) or Integer Wavelet Transform (IWT) coefficients of both cover image and secret image. After that two extracted coefficient values are embedded by fusion processing technique. Then the stego image is obtained by applying various combinations of DWT and IWT on both images.
In this method, we concentrated for perfecting the visual effect of the stego image and robustness against the various attacks by using different wavelet families. Finally performance evaluation is done on dual transform steganography using wavelet families and statistical methods. In our method achieved acceptable imperceptibility and certain robustness.
Foggy Image Enhancement Using Wavelet Decomposition, Quadratic Thresholding and Auto-Adaptive LUM Filter
An image captured in a bad weather suffers from poor contrast. As one of the most common weather conditions, fog whitens the scenery that is the captured image and decreases the atmospheric visibility which leads to the decline of image contrast, gained by optical equipment and produces fuzzy look to the images.
All the problems mentioned above might bring great difficulty to the image information extraction, outdoor image monitoring, automatic navigation, target identification, tracking and etc,.. Therefore, it is necessary that image captured in a bad weather or the foggy image is enhanced.
In this paper, a new method for foggy image enhancement has been proposed, that integrates multilevel wavelet decomposition, the auto-adapted LUM filter, quadratic thresholding function and so on.
Firstly, the multilevel wavelet decomposition is done to the image secondly low-frequency and high-frequency components of the image is obtained and finally the auto-adapted LUM filter is applied to low-frequency component. This new shrinkage function based on wavelet packet approximations turn out to be more flexible than the soft and hard-thresholding function and eventually carrying on wavelet restructuring to the processed components.
General Framework to Histogram-Shifting-Based Reversible Data Hiding
Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH.
By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction.
In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.
Hardware Implementation of a Digital Watermarking System for Video Authentication
This paper presents a hardware implementation of a digital watermarking system that can insert invisible, semifragile watermark information into compressed video streams in real time. The watermark embedding is processed in the discrete cosine transform domain.
To achieve high performance, the proposed system architecture employs pipeline structure and uses parallelism. Hardware implementation using field programmable gate array has been done, and an experiment was carried out using a custom versatile breadboard for overall performance evaluation.
Experimental results show that a hardware-based video authentication system using this watermarking technique features minimum video quality degradation and can withstand certain potential attacks, i.e., cover-up attacks, cropping, and segment removal on video sequences.
Furthermore, the proposed hardware-based watermarking system features low power consumption, low cost implementation, high processing speed, and reliability.
Image authentication and restoration by multiple watermarking techniques with advance encryption standard in digital photography
In the past few years, semi-fragile watermarking techniques have become increasingly important to secure and verify the multimedia content and also to localize the tampered areas, while tolerating some non-malicious manipulations or attacks. In digital photography, watermarking schemes are very important for copyright protection purposes.
In this paper, a multimedia authentication and restoration scheme is proposed with the security of AES-128 ciphered watermarking and correlated watermarking. An encrypted or ciphered image embedding is done by modified version of Closest Point Transform (CPT) in a digital photograph.
We performed several security attacks e.g. noise attack, compression attack, and cropping attack on multiple watermarked photographs and evaluated the proposed watermarking technique to examine the system robustness. Image Authentication is done by locating the tempered areas and restoration is performed by correlated watermark on the tempered region of watermarked photograph.
The PSNR values are checked to evaluate the proposed watermarking technique. The results of PSNR, MSE, and SSIM show that the imperceptibility of our scheme is high compared to existing methods.
Image steganography in DWT domain using double-stegging with RSA encryption
The need for preserving secrecy of sensitive data has been ever-increasing with the new developments in digital communication. In this paper, we present an advanced method for embedding encrypted secret data in grayscale images to provide high level security of data for communication over unsecured channels.
The proposed system combines the features of Cryptography and Steganography. Cryptography involves converting the secret message into a non-recognizable cipher. Steganography is then applied using Double-stegging to embed this encrypted data into a cover media and hides its existence.
Implementation and performance analysis of DCT-DWT-SVD based watermarking algorithms for color images
Digital image watermarking is one such technology that has been developed to protect digital content (text, images, audio, and video) from illegal manipulations.
In this paper we proposed implementation and performance analysis of two different watermarking schemes based on DCT-DWT-SVD. Both are non blind techniques.
One is based on SVD of DC coefficients using second level DWT decomposition and other is consider SVD of all DCT values of second level DWT composition of cover image. To check effectiveness of both techniques for Imperceptibility and robustness PSNR and NCC parameters are used.
Intensity Range Based Background Subtraction for Effective Object Detection
In this letter, we propose an intensity range based object detection scheme for videos with fixed background and static cameras. The scheme suggests two different algorithms; the first one models the background from initial few frames and the second algorithm extracts the objects based on local thresholding.
The strength of the scheme lies in its simplicity and the fact that, it defines an intensity range for each pixel location in the background to accommodate illumination variation as well as motion in the background.
The efficacy of the scheme is shown through comparative analysis with competitive methods. Both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance.
Irregular Moving Object Detecting and Tracking Based on Color in Real-time System
This paper describes an efficient approach for irregular moving object detecting and tracking in real-time system based on color and shape information of the target object from realistic environment.
Firstly, the data is gotten from a real-time camera system at a stable frame rate. And then, each frame is processed by using proposed method to detect and track the target object immediately in consecutive frames.
Finally, the target position based modifying controlling signal is used to control pan-tilt-zoom camera (PTZ camera) in order to automatically follow the target object. Our experiment results were obtained by using pan-tilt-zoom camera Sony EVI D70 under variety environments in real-time and our algorithm is verified that it can be implemented effectively and accurately at high frame speed, even 29.97 fps.
Least Significant Bit Matching Stegoanalysis Based on Feature Analysis
Steganography is a science of hiding messages into multimedia documents. In steganography, there is a technique in which the least significant bit is modified to hide the secret message, known as the least significant bit (LSB) steganography.
Several steganalyzers are developed to detect least significant bit (LSB) matching steganography. Least significant bit matching images are still not well detected, especially, at low embedding rate.
In this paper, we have improved the least significant bit steganalyzers by analyzing and manipulating the features of some existing least significant bit matching steganalysis techniques. A comprehensive set of experiments is carried out to justify proposed method's applicability and evaluate its performance against the existing least significant bit matching steganalysis techniques.
MIMO-OFDM Transceivers With Dual-Polarized Division Multiplexing and Diversity for Multimedia Broadcasting Services
This brief paper presents multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with dual-polarized division multiplexing (PDM) and diversity for multimedia broadcasting services. In particular, the polarized diversity is realized by transmitting two independent and distributed space-frequency coded signals through two sets of dual-polarized transmit antennas.
In the corresponding polarized receptions, MIMO zero-forcing (ZF) detection and ZF detection combined with a group-wise or a symbol-wise successive cancelation (SIC) are used to minimize a depolarization effect caused by a cross-polarization discrimination (XPD). Furthermore, we analyze the output signal-to-interference-plus-noise ratio (SINR) of such MIMO detections in terms of the XPD effect.
It is shown that the output SINR of MIMO ZF detection increases as the absolute XPD value increases. It is also shown that the output SINR of ZF-SIC detection converges to that of ZF detection as the absolute XPD value increases.
Finally, we compare the performances of uni-polarized and dual-polarized MIMO-OFDM systems over a multipath Rician channel with XPD. From simulation results, we conclude that as the absolute XPD value increases, the polarized MIMO-OFDM systems provide a lower symbol error rate than uni-polarized MIMO-OFDM systems.
Novel Techniques to reduce PAPR in OFDM Systems Using Threshold SLM
Orthogonal Frequency Division Multiplexing (OFDM) is proven technology in modern wireless communication because of its high data rate, more immunity to delay spread. In this paper, we proposed probabilistic threshold Selective Mapping Technique which has low PAPR. The simulation result shows that modified technique has better PAPR reduction performance.
Novel Windowing Scheme for Cognitive OFDM Systems
In this paper a new shaping window is designed to force the side-lobe of OFDM transmission to decay faster than conventional raised-cosine windowing. The proposed window is a soft-window based on the characteristics of functions with vestigial symmetry, which are common in digital data transmission.
By using the vestigial symmetry and enforcing several null derivatives at the extremes of the window, it is possible to control the fast out-of-band decay.
Results show that by using a lower roll-off factor (10%) the proposed window can achieve the same performance of raised-cosine (100 %) with around 90% overhead savings, making it of interest for OFDM cognitive radio.
Optimal 16-Ary APSK Encoded Coherent Optical OFDM for Long-Haul Transmission
In this letter, an optimal 4+12 amplitude and phase shift keying (APSK) modulated coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) system is proposed (the numbers 4 and 12 represent the constellation points located in the inner ring and the outer ring, respectively).
Although the 4+12APSK modulation format has smaller average Euclidean distance than 16 quadrature amplitude modulation (QAM), the 4+12APSK modulated CO-OFDM system has the best transmission performance, with increased tolerance towards both amplified spontaneous emission noise and fiber nonlinearities compared with 16QAM and 8+8APSK modulated OFDM systems.
The maximum reach at Q factor=8.2 dB (when bit-error rate (BER) equals 5 × 10-3) is evaluated to assess the system performance based on a single-channel and five-channel wavelength division multiplexing (WDM) CO-OFDM system, and illustrates that the transmission performance of 4+12APSK modulated OFDM outperforms 8+8APSK modulated OFDM and 16QAM modulated OFDM by approximately 10.5%, and in the WDM system, it surpasses 8+8APSK modulated OFDM by approximately 5.6% and 16QAM modulated OFDM by 18.75%.
Performance Analysis of OFDM Systems with Selected Mapping in the Presence of Nonlinearity
In the presence of nonlinearity, we analyze the impact of the selected mapping (SLM) technique on bit-error-rate (BER) performance of orthogonal frequency division multiplexing (OFDM) systems in an additive white Gaussian noise channel.
The peak-to-average-ratio (PAR) reduction gain of SLM can be increased by improving the PAR statistics at the cost of complexity in the OFDM transmitter, thereby helping to decrease the required power amplifier (PA) output backoff (OBO).
However, since the PAR statistics focus only on the statistical distribution of the highest peak in an OFDM symbol, the statistics cannot be used to quantify BER performance degradation in the presence of nonlinearity such as that caused by a PA or digital-to-analog converter (DAC). We first derive a closed-form expression for the envelope power distribution in an OFDM system with SLM.
Then, using this derived envelope power distribution, we investigate the BER performance and the total degradation (TD) of OFDM systems with SLM under the existence of nonlinearity. We discuss peak backoff (PBO) and the clipping ratio, which determine the operating point of the PA and dynamic range of the DAC, respectively.
Lastly, we consider the total degradation (TD), which indicates the tradeoff between the OBO and the E_b/N_o penalty due to nonlinearity, and numerically compute the PBO and clipping ratio that minimize the TD. The TD-minimizing PBO and clipping ratio are given as functions of the number of candidate signals in SLM.
Performance analysis of SLM for PAPR reduction of OFDM signal in Marine Channel
Orthogonal frequency division multiplexing (OFDM) modulation using orthogonal subcarriers reduces the delay spread by increasing robustness to multipath fading and can use overlapped bandwidth due to orthogonality on frequency domain.
The main drawback of Orthogonal Frequency Division Multiplexing (OFDM) systems is the high peak-to average power ratio (PAPR), which significantly reduces the efficiency of the transmit high power amplifier (HPA).
Several methods have been proposed in the literature to reduce the peak power of OFDM signals and substantial gains were reported. In SLM technique of PAPR reduction, side information needs to be transmitted for demodulation while degrading transmission efficiency. Though many studies have been carried out to analyze the performance of various PAPR reduction methods for land, the studies for sea environment is hardly addressed in literature.
In this paper, we have analyzed the effectiveness of SLM technique utilizing cyclic shifting of pilots for Marine Channel.
Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance
We address the problem of camera calibration for traffic scene surveillance, which supplies a connection between 2-D image features and 3-D measurement. It is helpful to deal with appearance distortion related to view angles, establish multiview correspondences, and make use of 3-D object models as prior information to enhance surveillance performance. A convenient and practical camera calibration method is proposed in this paper.
With the camera height H measured as the only user input, we can recover both intrinsic and extrinsic parameters of the camera based on redundant information supplied by moving objects in monocular videos. All cases of traffic scene layouts are considered and corresponding solutions are given to make our method applicable to almost all kinds of traffic scenes in reality.
Numerous experiments are conducted in different scenes, and experimental results demonstrate the accuracy and practicability of our approach. It is shown that our approach can be effectively adopted in all kinds of traffic scene surveillance applications.
Real Time Edge Detected Advanced Image Acquisition System using RGB Analysis
In modern era the real time edge detection of moving elements in image sequences is the most important step in many image & video acquisition systems including automated visual surveillance. In this paper, we present an advanced framework for detecting some very important issues, like people identification and people flow count in restricted organizations for security measures.
The objectives is to constant monitor activities at restricted regions for detecting unwanted congestions and predict the people flow which assists in regulating the crowd density at the target place.
This unique approach help us as the system can sense the frame of target object, detect instantly the edges of the target, automatically process the captured footage and provides the filtered version of the target image/video. In addition with, our proposed system can also track the motion of the target object (people). Also color histograms (RGB) help us to make prompt decisions while capturing the total range of existing digital image/video.
In this paper the proposed system with such modern features combined is designed using MAT Lab/SIMULINK V.7.10.0 and simulated. The related experimental results are shown and evaluated successfully.
Retinal Blood Vessel Segmentation using an Extreme Learning Machine Approach
Diabetic retinopathy is a vascular disorder caused by changes in the blood vessels of the retina. The proposed work uses an Extreme Learning Machine (ELM) approach for blood vessel detection in digital retinal images.
This approach is based on pixel classification using a 7-D feature vector obtained from preprocessed retinal images and given as input to an ELM. Classification results categorizes each pixel into two classes namely vessel and non-vessel. Finally, post processing is done to fill pixel gaps in detected blood vessels and removes falsely-detected isolated vessel pixels.
The proposed technique was assessed on the publicly available DRIVE and STARE datasets. The approach proves vessel detection is accurate for both datasets.
Robust Watermarking of AES Encrypted Images for DRM Systems
Digital image capturing, processing and distribution has showed a remarkable growth over recent years. This media content is sometimes distributed in encrypted format and watermarking of these media items for proof of ownership, media authentication needs to be carried out in encrypted domain to improve image security.
Therefore it is sometimes necessary to embed watermark in encrypted media items for ownership declaration or copyright management purposes. DRM system is one such example where there is a challenge to watermark these encrypted data as the encryption would have randomized the incoming data.
In this paper, a block cipher called AES-128 bit key encryption algorithm and DCT combined with DWT based watermarking algorithm to watermark the encrypted image were proposed which increases robustness of the watermark. These method embeds the binary watermark in encrypted image and decryption is done after extraction of watermark.
Satellite Image Enhancement Using Discrete Wavelet Transform and Threshold Decomposition Driven Morphological Filter
Satellite color images are being used in many fields of research. One of the major issues of these types of color images are their poor perception. In this letter a new method to enhance the satellite image which using the concept of wavelets and threshold decomposition.
The proposed enhancement technique uses DWT to decomposed input image into different sub bands. Threshold decomposition is a powerful theoretical tool, which is used in nonlinear image analysis.
Detecting the positions of the edges through threshold decomposition and these edges are sharpened by using morphological filters. This method will give better qualitative and quantitative results.
Satellite Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform and Nonlocal Means
Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing high-frequency contents (which results in blurring). The discrete-wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property).
A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands.
The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high-frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance).
The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.
Segmentation of Tissues in MR Images using Modified Spatial Fuzzy C Means Algorithm
MRI brain images are widely used in medical applications for research, diagnosis, treatment, surgical planning and image guided surgeries. These MR brain images are often corrupted with Intensity Inhomogeneity artifact cause unwanted intensity variation due to non- uniformity in RF coils and Rician noise, the dominant noise in MRI due to thermal vibrations of electrons and ions and movement of objects during acquisition which may affect the performance of image processing techniques used for brain image analysis.
Due to this type of artifact and noises, sometimes one type of normal tissue in MRI may be misclassified as other type of normal tissue and it leads to error during diagnosis. In this work, a method is proposed which automatically segments normal tissues such as White Matter, Gray Matter and Cerebrospinal Fluid from MR images with Rician noise and Intensity Inhomogeneity artifact.
The proposed method consists of preprocessing using wrapping based curvelet transform to remove noise and Modified Spatial Fuzzy C Means segments normal tissues by considering spatial information because neighboring pixels are highly correlated and also construct initial membership matrix using initial cluster center by incorporating spatial neighborhood information to improve strength of clustering.
This combination reduces Intensity Inhomogeneity artifact by providing higher segmentation accuracy. In this proposed work, the accuracy, sensitivity and specificity are improved with better segmentation over other previous methods.
Separable Reversible Encrypted Data Hiding in Encrypted Image Using AES algorithm and Lossy Technique
The field steganography is very much popular technique for sending secrete message and lots of research are going in it. To overcome the limitation of previous work we proposed separable and reversible encrypted data hiding in encrypted image using AES Algorithm and Lossy technique as solution.
In this sender encrypt data and image separately using AES algorithm, hides encrypted data in encrypted image using LSB technique, system auto generate the all 3 respective keys.
Sender sends the file through existing mail system. Receiver can perform operation as per respective keys like if he has only data hiding and image decryption key then he can only get the image in original form or if he have data hiding and data decryption key then he can get original data, system also provides protection for auto generated keys and system auto generate mail if user fail to perform any operation.
Shadow Removal for Background Subtraction using Illumination Invariant Measures
In this paper, we propose methods for shadow removal in static background environment. Accurate background subtraction is essential to detect and track various objects. Shadow and objects similar in color are major problems in background subtraction and tracking.
In order to address these problems, we propose shadow removal method for background subtraction using illumination invariant measures. First, we computed a reference background image and the illumination invariant measures were applied to the reference background image and an input image.
We compared the proposed method with some existing background subtraction methods. The experimental results showed that the proposed method produced more accurate results.
Spectrum-Efficient Coherent Optical OFDM for Transport Networks
Orthogonal frequency division multiplexing (OFDM) is a promising technology for the next-generation optical transmission systems beyond 100 Gb/s. To further improve the spectral efficiency and system reliability, we propose a flexible coherent zero padding OFDM (CO-ZP-OFDM) scheme with signaling-embedded preamble and polarization-time-frequency (PTF) coded pilots for high-speed optical transport networks. Our judicious design embeds signaling in the specially designed preamble whose Delta-like correlation function helps to simultaneously achieve very accurate timing and frequency synchronization.
Unlike the periodically inserted training symbols, the PTF-coded pilots are properly distributed within the time-frequency grid of the ZP-OFDM payload symbols and used to realize low-complexity multiple-input multiple-output (MIMO) channel estimation at high accuracy.
Compared with the conventional optical OFDM systems, CO-ZP-OFDM increases payload by about 6.68%, and the low-density parity-check (LDPC) coded bit error rate only suffers from no more than 0.3 dB compared with the back-to-back case even when the channel dispersion impairments are severe.
Study and Analysis of PCA, DCT & DWT based Image Fusion Techniques
Image Fusion is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images.
This paper discusses the Formulation, Process Flow Diagrams and algorithms of PCA (principal Component Analysis), DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) based image fusion techniques.
The results are also presented in table & picture format for comparative analysis of above techniques. The PCA & DCT are conventional fusion techniques with many drawbacks, whereas DWT based techniques are more favorable as they provides better results for image fusion. In this paper, two algorithms based on DWT are proposed, these are, pixel averaging & maximum pixel replacement approach.
Texture classification using color local texture features
This Paper proposes a new approach to extract the features of a color texture image for the purpose of texture classification. Four feature sets are involved. Dominant Neighbourhood Structure (DNS) is the new feature set that has been used for color texture image classification.
In this feature a global map is generated which represents measured intensity similarity between a given image pixel and its surrounding neighbours within a certain window. Addition to the above generated feature set, features obtained from DWT are added together with DNS to obtain an efficient texture classification. Also the proposed feature sets are compared with that of Gabor wavelet, LBP and DWT.
The texture classification process is carried out with the robust SVM classifier. The experimental results on the CUReT database shows that the proposed method is an efficient method whose classification rate is higher when compared with the other methods.
Track Creation and Deletion Framework for Long-Term Online Multi face Tracking
In many visual multi-object tracking applications, the question when to add or remove a target is not trivial due to, for example, erroneous outputs of object detectors or observation models that cannot describe the full variability of the objects to track. In this paper, we present a real-time, online multi-face tracking algorithm that effectively deals with missing or uncertain detections in a principled way.
The tracking is formulated in a multi-object state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step relying on head detections, likelihood models, and long term observations as well as object track characteristics has been designed to take the decision on when to add or remove a target from the tracker.
The proposed method applied on three challenging datasets of more than 9 hours shows a significant performance increase compared to a traditional approach relying on head detection and likelihood models only.
Wavelet Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems
With the growing number of aging population and a significant portion of that suffering from cardiac diseases, it is conceivable that remote ECG patient monitoring systems are expected to be widely used as Point-of-Care (PoC) applications in hospitals around the world.
Therefore, huge amount of ECG signal collected by Body Sensor Networks (BSNs) from remote patients at homes will be transmitted along with other physiological readings such as blood pressure, temperature, glucose level etc. and diagnosed by those remote patient monitoring systems. It is utterly important that patient confidentiality is protected while data is being transmitted over the public network as well as when they are stored in hospital servers used by remote monitoring systems.
In this paper, a wavelet based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its corresponding patient confidential data and other physiological information thus guaranteeing the integration between ECG and the rest.
To evaluate the effectiveness of the proposed technique on the ECG signal, two distortion measurement metrics have been used: the Percentage Residual Difference (PRD) and the Wavelet Weighted PRD (WWPRD). It is found that the proposed technique provides high security protection for patients data with low (less than 1% ) distortion and ECG data remains diagnosable after watermarking (i.e. hiding patient confidential data) and as well as after watermarks (i.e. hidden data) are removed from the watermarked data.
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