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.     

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.     

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.

     

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.     

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     

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.     

A phase reconstruction algorithm from bispectrum [seismicreflection data]

A computationally efficient procedure for the reconstruction of the impulse response of a (minimum- or nonminimum-phase) linear time-invariant system from its bispectrum is presented. This method is based on computing cepstrum of the impulse response sequence from the ω₁=ω₂ slice of the bispectrum. The algorithm can be implemented by using only the one-dimensional fast Fourier transform algorithm     

Compressive sensing applied to radar systems: an overview

Modern radar systems tend to utilize high bandwidth, which requires high sampling rate, and in many cases, these systems involve phased array configurations with a large number of transmit–receive elements. In contrast, the ultimate goal of a radar system is often to estimate only a limited number of target parameters. Thus, there is a pursuit to find better means to perform the radar signal acquisition as well as processing with much reduced amount of data and power requirement. Recently, there has been a great interest to consider compressive sensing (CS) for radar system design; CS is a novel technique which offers the framework for sparse signal detection and estimation for optimized data handling. In radars, CS enables the achievement of better range-Doppler resolution in comparison with the traditional techniques. However, CS requires the selection of suitable (sparse) signal model, the design of measurement system as well as the implementation of appropriate signal recovery method. This work attempts to present an overview of these CS aspects, particularly when CS is applied in monostatic pulse-Doppler and MIMO type of radars. Some of the associated challenges, e.g., grid mismatch and detector design issues, are also discussed.     

Complexity reduction of equalization/pre-emphasis using set membership filtering for NG LR-PON

Coherent receivers, with advanced and low-complexity digital signal processing (DSP), have the advantage of increasing the loss/power budget of next generation-long-reach passive optical networks (NG-LRPONs). This reduces the network capital expenditures by eliminating or reducing the number of amplifiers to be installed between the optical line terminal (OLT) and the optical network units (ONUs). In this paper, we investigate the complexity and convergence speed of two adaptive equalization and/or pre-emphasis strategies for mitigating chromatic and polarization mode dispersions (CD and PMD) in NG-LRPON. We first identify two potential deployment strategies of equalization and/or pre-emphasis. The first equally splits the signal processing in the OLT and ONU; however, the second concentrates most of DSP in the OLT trying to reduce the cost and alleviate the complexity of ONUs. Our investigation shows that the second strategy achieves 50 % faster convergence rate in terms of number of symbols for 16QAM/5 Gbaud. Moreover, we apply the enhanced set membership filtering (SMF) technique, recently introduced for next generation wireless communications, to our LR-PON in order to reduce the update rate of equalizers’ taps, hence reduce the calculation complexity of the OLT and ONUs. Our results show that by employing SMF technique a substantial reduction in the number of mathematical operations needed to attain convergence is achieved. Simulation results reveal that our proposed SMF can reduce the equalizers’ update rate, hence calculation complexity, by 55 % for 16QAM and 75 % for QPSK with marginal degradation of the BER.