Cumulant based identification approaches for nonminimum phase FIRsystems

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the second- and third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identification algorithms, new procedures for determining the order of an FIR system using only the output cumulants are also presented. To illustrate the effectiveness of the methods, various simulation examples are presented     

Hilbert Transform and Statistical Analysis for Channel Selection and Epileptic Seizure Prediction

This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the signals in the Hilbert domain. The operation in the Hilbert domain guarantees working on the low-pass spectra of EEG signal segments to avoid artifacts. Signal attributes in the Hilbert domain including amplitude, derivative, local mean, local variance, and median are analyzed statistically to perform the channel selection and seizure prediction tasks. Pre-defined prediction and false-alarm probabilities are set to select the channels, the attributes, and bins of probability density functions (PDFs) that can be useful for seizure prediction. Due to the multi-channel nature of this process, there is a need for a majority voting strategy to take a decision for each signal segment. Simulation results reveal an average prediction rate of 96.46%, an average false-alarm rate of 0.028077/h and an average prediction time of 60.1595 min for a 90-min prediction horizon.

     

Compressive sensing based high-resolution passive bistatic radar

Passive bistatic radar (PBR) systems using different communication signals can only offer low-resolution target detection due to their inherent low bandwidth. In this paper, compressive sensing (CS) is applied to multichannel FM and GSM PBR to achieve improved range-Doppler resolutions and avoid some limitations of classical multiband PBR processing. In CS context, block-structured time-domain dictionary which is formed from multichannel signals suffers from coherence when fine range resolution is employed. To overcome such a pitfall, this work first transforms the dictionary to spectral domain where only the most important spectral components are retained. Principle component analysis followed by a whitening method are then applied to this spectrally transformed data in order to reduce the dimensionality of problem, thereby reducing the dictionary size and most importantly fulfilling the required condition of dictionary incoherence for better CS-based recovery. Two different block-structured dictionary formations are tested. The performance of the recovery of spatially close targets, in both FM and GSM PBR setups, are reported.     

A GACS modeling approach for MPEG broadcast video

Accurate MPEG source models are needed to support high speed networks such as ATM and Internet. In this paper, we propose a video model called Gaussian auto-regressive and chi-square processes (GACS) for MPEG coded video traffic. The GACS models the sizes of MPEG I, P, and B frames according to the MPEG syntax I-frame>P-frame>B-frame. This is done by decomposing the process of each frame size into a weighted sum of a number of chi-square sequences. Each chi-square sequence is then obtained by squaring a Gaussian process, which is efficiently generated by using an auto-regressive (AR) model whose parameters are determined from an estimated covariance matrix. We evaluate the effectiveness of our model by conducting a series of experiments using a wide variety of long empirical video sequences. The results show that the proposed model leads to excellent data fit and accurate prediction of queuing performance.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Multiple access interference cancelation technique in optical CDMA systems

This paper presents a simple and efficient multiple access interference (MAI) cancelation technique in optical code division multiple access (OCDMA) system. The proposed technique is based on hybrid frequency shift keying (FSK) with an enhanced modified prime code as a signature sequence for coding techniques. Coherent FSK modulation along with incoherent demodulation using Arrayed-Waveguide Grating has been examined in the transceiver structure. In the proposed technique, a reference signal is constructed by using one of the addressed spreading sequences, and MAI cancelation is performed by subtracting the reference signal from the received signal of the desired user. The performance of the proposed FSK-OCDMA system is compared with the performance of the existing pulse position modulation (PPM)–OCDMA system. The simulation results reveal that the bit-error rate performance of the proposed technique is superior to the performance of the pulse position modulation (PPM) technique. Also, the results indicate that the proposed technique is very power efficient, and when the bit rate is constant, the network capacity can be expanded to accommodate a large number of simultaneous active users with low error rate. Moreover, the proposed technique simplifies the hardware of the receiver design.     

On the Performance of FFT/DWT/DCT-based OFDM Systems with Chaotic Interleaving and Channel Estimation Algorithms

The efficiency of data transmission over fading channels in orthogonal frequency division multiplexing (OFDM) systems depends on the employed interleaving method. In this study, we propose an improved chaotic interleaving scheme which aims to improve the performance of OFDM system under fading channel. In the proposed scheme, the binary data is interleaved with chaotic Baker map prior to the modulation process. In the sequel, significant degree of encryption is being added during data transmission. The performance of the proposed approach is tested on the conventional fast Fourier transform OFDM, discrete wavelet transform OFDM, and discrete cosine transform OFDM with and without chaotic interleaving. Furthermore, an expectation–maximization (EM) algorithm is proposed for improving channel impulse response (CIR) estimation based on a maximum likelihood principle. The proposed scheme makes use of EM algorithm to update the channel estimates until convergence is reached. The simulation results show the efficiency of the proposed algorithms under Rayleigh fading environments where the symbol error rate essentially coincides with that of the perfect channel case after the fifth EM iteration.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Design of a printed log‐periodic dipole array antenna with high gain for millimeter‐wave applications

In this article, a V‐band printed log‐periodic dipole array (PLPDA) antenna with high gain is proposed. The antenna prototype is designed, simulated, fabricated, and tested. Simulation results show that this antenna can operate from 42 to 82 GHz with a fractional impedance bandwidth of 64.5% covering the whole V‐band (50–75 GHz). The antenna has a measured impedance matching bandwidth that starts from 42 to beyond 65 GHz with good agreement between the experimental and simulated results. At 50 and 65 GHz, the antenna has a measured gain of 10.45 and 10.28 dBi, respectively, with a gain variation of 2.6 dBi across the measured frequency range. The antenna prototype exhibits also stable radiation patterns over the operating band. It achieves side‐lobe suppression better than 17.26 dB in the H‐plane and better than 8.95 dB in the E‐plane, respectively. In addition, the cross‐polarization component is 18.5 dB lower than the copolarization with front‐to‐back ratio lower than 24.1 dB in both E‐ and H‐planes across the desired frequency range. Based on a comparison of performance among the reported work in the literature, we can say that the proposed PLPDA antenna is a proper candidate to be used in many applications at V‐band frequency. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:185–193, 2015.     

A novel hardware/software embedded system based on automatic censored target detection for radar systems

This paper presents a practical design exploration for a new application related to real-time, high-resolution target detection for radar systems. In this paper, an embedded architecture that combines the hardware and software components in a single platform is experienced using a field programmable gate array FPGA-based PC-board. The detection process utilises three techniques: namely, automatic censored ordered statistics detection (ACOSD), cell averaging (CA) and ordered statistics (OS) CFAR techniques, all of which operate in parallel to increase the accuracy of the detection and to reduce the false-alarm rate for both homogeneous and non-homogeneous environments. A prototype of the embedded system detector has been implemented for homogeneous and non-homogeneous environments on Stratix IV FPGA Board. The prototype operates at 200 MHz and performs real-time target detection with an execution delay of 0.27 μs, which is less than the critical time (0.5 μs) for high-resolution detection.