Showing papers in "WSEAS Transactions on Signal Processing archive in 2018"

A Review of Noise Cancellation Techniques for Cognitive Radio

Abstract: One of the fundamental challenges affecting the performance of communication systems is the undesired impact of noise on a signal. Noise distorts the signal and originates due to several sources including, system non-linearity and noise interference from adjacent environment. Conventional communication systems use filters to cancel noise in a received signal. In the case of cognitive radio systems, denoising a signal is important during the spectrum sensing period, and also during communication with other network nodes. Based on our findings, few surveys are found that only review particular denoising techniques employed for the spectrum sensing phase of cognitive radio communication. This paper aims to provide a collective review of denoising techniques that can be applied to a cognitive radio system during all the phases of cognitive communication and discusses several works where the denoising techniques are employed. To establish comprehensive overview, a performance comparison of the discussed denoising techniques are also provided.

Selection Image Points Method for Steganography Protection of Information

Abstract: The method of effective extraction of image cells for realization of steganographic protection of information is proposed in the paper. On the basis of the proposed method, a method for constructing a container is proposed as represented by a graphic file. The main units of the steganographic system are developed on the basis of the proposed method of a graphic container constructing. To increase the number of the extracted cells, noise is added to the image. Noise cells are also used to embed message bits. The schemes and VHDL models of the cell extraction unit are developed.

Identifying Outliers in HRV–Seizure Signals Using p-shift UFIR Baseline Estimates

Abstract: Heart rate variability (HRV) is typically associated with neuroautonomic activity and viewed as a major non–invasive tool to detect seizures. The HRV has been assumed and analyzed as a stationary signal. However the presence of seizures can violate estimates of statistical parameters and conventional techniques intended to remove outliers can be inaccurate. A useful approach implies setting thresholds to compute the first and third quartiles from histogram data or residuals based on the estimated baseline. In this paper, we propose an accurate method to identify outliers in HRV measurements with partial epilepsy retaining relevant information. The baseline perturbed by the seizure in the HRV data is removed using the p-shift unbiased finite impulse response (UFIR) smoothing filter operating on optimal horizons. The residuals histogram is plotted and the upper bound (UB) and lower bound (LB) are computed as thresholds. A comparison is provided of a typical points detected in HRV/seizures based on several methods used to estimate the baseline. A time/frequency analysis is supplied to show the difference between the raw HRV and HRV without outliers. The method proposed is tested by partial seizures records taken from patients during continuous EEG/ECG and video monitoring.

Counting Cells in Microscopic Image Using Two-Phase Segmentation

Abstract: Cytopathology is a branch which deals with study diseases at the microscopic cell level. Now a day, deadly disease like cancer has become a major challenge in the field of medical research. If these diseases are identified in the early stages, then they are curable. There are many methods to ease, faster and produce accurate results of this analysis. One such approach is using image processing techniques. This paper applies segmentation techniques to find important feature like cell count. This feature is further used to differentiate between normal and abnormal cells. Abnormal cells are the one which may turn into cancerous cells. Hence the early detection of such abnormal cells before they turn into cancerous cells helps to diagnose patient early. The proposed method was evaluated using cervical cells.

Design of Minimum-length, Minimum-phase, Low-Group-Delay FIR Filter Using Convex Optimization Method

Abstract: This paper proposes a new method for optimal design of minimum-length, minimum-phase, low-group-delay FIR filter by employing convex optimization, discrete signal processing (DSP), and polynomial stabilization techniques. The design of a length-N FIR filter is formulated as a convex second-order cone programming (SOCP). In order to design a minimum-phase FIR filter as the necessary condition for having low group delay, the algorithm guarantees that all the filter’s zeros are inside the unit circle (minimum-phase). In addition, the quasiconvex optimization problem is developed to minimize the length of minimumphase, low-group-delay FIR filter. To this end, for a typical low-pass FIR filter, the length of the filter is minimized such that the optimum magnitude response is satisfied, the minimum-phase characteristic is maintained, and the low-group-delay is achieved. The proposed design algorithm only relies on one parameter (cut-off frequency) and the rest of filter parameters are automatically optimized as the trade-off between having minimum-length, minimum-phase, maximum stopband attenuation and low group delay. The effectiveness and performance of proposed approach is demonstrated and compared with other approaches over a set of examples. It is illustrated that this approach converges to the optimal solution in a few iterations.

Visual Enhancement Method for Intelligent Vehicle Based on Brightness Guide Filtering Algorithm

Abstract: In order to improve the low visibility and poor contrast of video, in this study, we propose a haze video enhancement algorithm based on guide filtering method. In the paper, firstly, we simplified the atmospheric attenuation model. Then, the current concentration of haze is estimated based on dark channel priori theory. After that, a guide filter of brightness channel was used to obtain current haze coverage. Videos are recovered based on the results of estimation of haze concentration and coverage. To improve the efficiency of the algorithm, highly time-consuming stage of haze concentration is implemented in initialization phase, and an indicator of video definition is used to control the procedure of subsequent module. Experimental results show that the algorithm can effectively improve the contrast and sharpness of video while with highly computational efficiency. The proposed method can meet the needs of video haze removing

The Compression of Digital Imaging and Communications in Medicine Images Using Wavelet Coefficients Thresholding and Arithmetic Encoding Technique

Abstract: The Image denoising is one of the challenges in medical image compression field. The Discrete Wavelet Transform and Wavelet Thresholding is a popular tool to denoising the image. The Discrete Wavelet Transform uses multiresolution technique where different frequency are analyzed with different resolution. In this proposed work we focus on finding the best wavelet type by applying initially three level decomposition on noise image. Then irrespective to noise type, in second stage, to estimate the threshold value the hard thresholding and universal threshold approach are applied and to determine best threshold value. Lastly Arithmetic Coding is adopted to encode medical image. The simulation work is used to calculate Percentage of Non – Zero Value (PCDZ) of wavelet coefficient for different wavelet types. The proposed method archives good Peak Signal to Noise Ratio and less Mean Square Error and higher Compression Ratio when wavelet threshold and Uniform Quantization apply on Arithmetic Coder

SOPC for Real Time Multi-Video Treatments with QoS Requirements

Abstract: Today, a significant number of embedded systems focus on multimedia applications with almost insatiable demand for low-cost and high performance. Generally, the majority of video applications need to execute parallel tasks with simultaneous access to the memory. One fact is that these parallel tasks have different bandwidth requirements that have to be satisfied separately when granting access to the memory. In this paper, we propose a general purpose architecture for video applications which satisfies parallel access to the memory with different bandwidth requirements. The proposed solution is based on the Multi Port Memory Controller MPMC. The management of memory accesses is assured by using the BGPQ algorithm to guarantee QoS requirements. We demonstrate the important role of this solution in multi-video applications when multiple bandwidths are required. In fact, to successful the deployment of DRAM, it is mandatory to use a flexible and scalable interface with the appropriate arbitration algorithm. The proposed architecture is implemented using the Xilinx Virtex-5 FPGA and its available resources like embedded memory, DCM’s and others. It also introduces diverse modules such as video zoom-in and out. This provides the utility of using this architecture as a universal video processing platform according to different applications requirement.

Detection of Speaker Identities from Cochannel Speech Signal

Abstract: Supervised speech segregation for cochannel speech signal can be made easier if we use predetermined speaker’s models instead of taking models for all the population. Here we propose a signal to signal ratio (SSR) independent method to detect speaker identities from a cochannel speech signal with unique speaker specific features for speaker identification. Proposed Kekre’s Transform Cepstral Coefficient (KTCC) features are the robust acoustic features for speaker identification. A text independent speaker identification system is utilized for identifying speakers in short segments of test signal. Gaussian mixture modeling (GMM) classifier is used for the identification task. We compare the proposed method with a system utilizing conventional features called Mel Frequency Cepstral Coefficient (MFCC) features. Spontaneous speech utterances from candidates are taken for experimentation instead of utterances that follow a command like structure with a unique grammatical structure and have a limited word list in speech separation challenge (SSC) corpus. Identification is performed on short segments of the cochannel mixture. Two Speakers who have been identified for most of segments of the cochannel mixture are selected as two speakers detected for the same cochannel mixture. Average speaker detection accuracy of 93.56% is achieved in case of two speaker cochannel mixture for of KTCC features. This method produces best results for cochannel speaker identification even being text independent. Speaker identification performance is also checked for various test segment lengths. KTCC features outperform in speaker identification task even the length of speech segment is very short.

A Robust Waveform Design for Targets Tacking in Cognitive MIMO Radars

Abstract: An adaptive waveform is optimized in order to maximize the information returned from the targets, then the targets informaion is approximated by using a particle filter. This study propose a method to due with the uncertainties due to the dynamics of the targets, e.g., when the number of moving targets is unknown and changing over time. Thus, A decay constant is added to the estimated prior target information before optimizing the waveform by minimizing Cramer-Rao Lower Bound. Jeffreys prior is used to weight the parameters of each ´ targets. Furthermore, the dynamic state space of the targets is estimated by a particle filter. Finally, the simulation results demonstrate the capability of the system to track targets.

High Resolution Image Reconstruction Using Fast Compressed Sensing Based on Iterations

Abstract: As a powerful high resolution image modeling technique, compressive sensing (CS) has been successfully applied in digital image processing and various image applications. This paper proposes a new method of efficient image reconstruction based on the Modified Frame Reconstruction Iterative Thresholding Algorithm (MFR ITA) developed under the compressed sensing (CS) domain by using total variation algorithm. The new framework is consisted of three phases. Firstly, the input images are processed by the multilook processing with their sparse coefficients using the Discrete Wavelet Transform (DWT) method. Secondly, the measurements are obtained from sparse coefficient by using the proposed fusion method to achieve the balance resolution of the pixels. Finally, the fast CS method based on the MFR ITA is proposed to reconstruct the high resolution image. In addition, the proposed method achieves good PNSR and SSIM values, and has shown faster convergence rate when performed the MFR ITA under the CS domain. Furthermore the graphical representation demonstrated that the proposed method achieves better performance in terms of the SNR reconstruction and the probability of successfully recovered signal, and also outperforms several other methods.

APC Heartbeats UFIR Smoothing and P-wave Features Analysis Using Rice Distribution

Abstract: Heart diseases are one of most frequent causes of death in the modern world. Therefore, the ECG signal features have been under peer review for decades to improve medical diagnostics. In this paper, we provide smoothing of the atrial premature complex (APC) of the electrocardiogram (ECG) signal using unbiased finite impulse response (UFIR) smoothing filtering. We investigate the P-wave distribution using the Rice law and determine the probabilistic confidence interval based on a database associated with normal heartbeats. It is shown that the abnormality in the APC is related to the P-wave morphology. Different filtering techniques employing predictive and smoothing filtering are applied to APC data and compared experimentally. It is demonstrated that UFIR smoothing provides better performance among others. We finally show that the P-wave confidence interval defined for the Rice distribution can be used to provide an automatic diagnosis with a given probability.

Implementation of Spatial Interaction System Using Transparent Display

Abstract: This study proposes a method for controlling a 3D entity shown on a transparent display by distinguishing between the hand and object using a single depth camera and by extracting relevant information for each. The hardware configuration for controlling a 3D entity has been presented. To enable the control of a 3D entity, a target area where the distinction between the hand and the object is made is extracted in the preprocessing stage. The extracted target area images are normalized to an identical size, and projected onto Zernike moment basis functions. The database is built based on moment values converted from projected images to distinguish the hand from object when input images are presented on a real time basis. This study also presents a method for interacting with a 3D entity using hand and object. To validate the performance of the system, an evaluation of recognition rate and time was performed.

Adaptive Analysis of Sparse Signals

Abstract: We introduce an adaptive method for analysis of sparse signals using bandpass filters obtained by modulated Slepian sequences. Similar to the recently introduced empirical wavelet transform, the proposed method decomposes a signal into different modes which corresponds to segmenting the Fourier spectrum and filtering the existing support. The simulations illustrate the correct signal decomposition for a multiband signal which has a sparse spectrum. The proposed method can be used as an alternative to empirical wavelet transform

Optical Fiber Cables Networks Defects Detection Using Thermal Images Enhancement Techniques

Abstract: Image enhancement is a process to output an image which is more suitable and useful than original image for specific application. Thermal image enhancement includes many techniques used in Quality Control, Problem Diagnostics, and Insurance Risk Assessment. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE), Fast Fourier Transform which results in Highlighting interesting detail in images, removing noise from images, making images more visually appealing, edge enhancement and increase the contrast of the image. This research article explains how could the various stated techniques and operations will be useful in the detection of the defects for the optical fiber cables and their connectors and most of optical devices to be more effective in Optical fiber based communication systems

Context-Aware Model Applied to HOG Descriptor for People Detection

Abstract: This work proposes and implements a method based on Context-Aware Visual Attention Model (CAVAM), but modifying the method in such way that the detection algorithm is replaced by Histograms of Oriented Gradients (HOG). After reviewing different algorithms for people detection, we select HOG method because it is a very well known algorithm, which is used as a reference in virtually all current research studies about automatic detection. In addition, it produces accurate results in significantly less time than many algorithms. In this way, we show that CAVAM model can be adapted to other methods for object detection besides Scale-Invariant Feature Transform (SIFT), as it was originally proposed. Additionally, we use TUD dataset image sequences to evaluate and compare our approach with the original HOG algorithm. These experiments show that our method achieves around 2x speed-up at just 2% decreased accuracy. Moreover, the proposed approach can improve precision and specificity by more than 2%.

A Novel Approach for the Restoration of AFM Images that were Produced Using the Impulse Response Technique at Different Scanning Speeds

Abstract: The atomic force microscope is a very useful tool for use in biology and in nano-technology, since it can be used to measure a variety of objects such as cells and nano-particles in a variety of different environments. However, the images produced by the AFM are distorted and do not accurately represent the true shape of the measured cells or particles, even though many researchers do not take this fact into account. In this paper we determine the impulse response of AFM using experimental results gathered from measuring the cylindrical sample via AFM. Once the AFM impulse response is estimated, the Lucy- Richardson algorithm is used to calculate the deconvolution between the resultant AFM impulse response and the blurred AFM image. This produces a more accurate AFM image. Also in this paper, we compare raw experimental AFM images with the Restored AFM images quantitively and the proposed algorithm is shown to provide superior performance.

Muti-Scale Feature Extraction for Vehicle Detection Using PHis-LBP

Abstract: Multi-resolution object detection faces several drawbacks including its high dimensionality produced by a richer image representation in different channels or scales. In this paper, we propose a robust and lightweight multi-resolution method for vehicle detection using local binary patterns (LBP) as channel feature. Algorithm acceleration is done using LBP histograms instead of multi-scale feature maps and by extrapolating nearby scales to avoid computing each scale. We produce a feature descriptor capable of reaching a similar precision to other computationally more complex algorithms but reducing its size from 10 to 800 times. Finally, experiments show that our method can obtain accurate and considerably faster performance than state-of-the-art methods on vehicles datasets.

Interval Extension of Bézier Curve

Abstract: By extending definition interval of the classical Bernstein basis functions to be dynamic, a class of Bernstein basis functions with a shape parameter is constructed in this work. The new basis functions are simple extension of the classical Bernstein basis functions. Then the corresponding Bezier-like curve is generated on base of the introduced basis functions. The new curve not only has most properties of the classical Bezier curve, but also can be adjusted by altering value of the shape parameter when the control points are fixed. Because the proposed curve is a polynomial model of the same degree and having most properties of the classical Bezier curve, it has more advantages than some existing similar models.

SVD-Aided UKF for Estimation of Nanosatellite Rotational Motion Parameters

Abstract: Singular Value Decomposition (SVD) and unscented Kalman filter (UKF) are interlaced using the Euler angles as the attitude parameter in order to estimate the satellite s angular motion parameters about center of mass. Magnetometer and sun sensor are used as the vector measurements for SVD in addition to the angular ate measurements from rate gyro for UKF; therefore, the output of the SVD shaped the nontraditional approach as SVD aided UKF algorithm using the linear measurements.

Adaptive Decompositions of General Flows and their Applications

Abstract: Adaptive algorithms of spline-wavelet decomposition in a linear space over metrized fields are proposed. The algorithms provide a priori given estimate of the deviation of the main flow from the initial one. Comparative estimates of data of the main flow under different characteristics of the irregularity of the initial flow are done. The limiting characteristics of data, when the initial flow is generated by abstract differentiable functions, are discussed. The constructions of adaptive grid and pseudo-equidistant grid and relative quantity of their knots are considered, flows of elements of linear normed spaces and formulas of decomposition and reconstruction are discussed. Wavelet decomposition of the flows is obtained with using of spline-wavelet decomposition. Sufficient condition of the construction is obtained. Applications to different spaces of matrix of fixed order and to spaces of infinite-dimension vectors with numerical elements (rational, real, complex and p-adic elements) are considered