Electrical and Electronics

This paper presents a Compressive Sensing (CS) greedy iterative algorithm, based on OMP: Sparsity-Independent-OMP (SI-OMP), for varying sparsity spectral conditions. As CS is one of the most essential techniques used by a Cognitive Radio (CR) for efficient usage of spectrum, it is required to be optimally simple, and, still, swift in working. The complexity here refers to the Number of computations a CR is required to make while using such algorithms and, this also, will in turn affect the effective requirement of hardware and power consumption. The proposed algorithm introduces negligible additional complexity, but enables a significant performance improvement in the reconstruction accuracy for arbitrarily varying spectral conditions. The spectrum here is a function of time and frequency both, exhibiting varying sparsity in both the domains.

The aim of this study was to optimise the biogas generation from anaerobic digestion process and to investigate the biogas as secondary fuel for the CI engine. The parameters studied were total solid concentration (TS), initial pH and co digestion of slurry. The effectiveness of animal and industrial waste was investigated using 1 m3 bio digester working in a continuous process. Anaerobic digestion seemed feasible with loading rate of 8%,10% and 12% ( TS)and biogas yield reported as 82.3m3,110.9m3 and 74.4 m3 respectively. The data obtained establish the importance of pre-treatment and total solids for achieving higher cumulative biogas yield. The volume of generated biogas was effectively used as secondary fuel for an engine by modifying the engine inlet manifold to operate with dual fuel mode.

This paper presents a new topology for a seven-level cascaded H-bridge multilevel inverter. The proposed topology uses reduced number of switches and requires only one DC source. The new topology results in reduced cost and can be implemented for any number of levels. The proposed seven level inverter is simulated in MATLAB-Simulink. The switching angles are generated using a new technique of selective harmonic elimination technique. The simulated waveforms of output voltage have reduced total harmonic distortion.

The complexity and size of circuits have been rapidly rising, placing a stressing demand on industry for faster and more competent for VLSI design. If partitioning is not done in valuable manner, ignoring the parameters like firmness, time delay and robustness it may corrupt the overall performance of a design. Optimization is used to make a design particularly efficient, finding the maximum of a function. In the partitioning main objective is to minimize the number of cuts. Investigating the application of the Memetic Algorithm (MA) for solving the codesign partitioning problem can be done.

The low voltage profile of the Nigeria grid results in poor voltage regulation in the network. The objective of this paper is to improve the voltage profile of the network using Static Var Compensator. The condition of the network was obtained through load flow technique. After performing load flow analysis, it was observed that some buses were violated that is, they were operating below the standard limit of 0.95 - 1.05 PU (313.5kV - 346.5kV). The Static Var Compensator is an important FACTS device which contains majorly a combined combination of the capacitor, inductor and the thyristor. The installation of SVC helped to improve the voltage profile of the affected buses to 1PU.

Monitoring technology has advanced dramatically in recent years in various locations, both urban and rural. The Internet of Things (IoT) enables the remote control and collecting of data from sensors for their subsequence analysis. Thus, LiFi was proposed as an enabling technology for IoT in indoor environments. This enables the supermarket automation through the use of IoT topology. However, the absence of mutual trust can create a barrier to implementation. To conduct cryptocurrency transactions, blockchain technology has been widely employed. It has recently shown to be effective in establishing confidence in the Internet of Things (IoT) domain. This paper offered a method for integrating IoT features into supply chains. While strengthening the security of IoT-based supply chain management, our suggested architecture streamlines data sharing and decreases computational, storage, and latency needs.

Since Superconducting Magnetic Energy Storage (SMES) unit with a self-commutated converter is capable of controlling both the active and reactive power simultaneously and quickly, increasing attention has been focused recently on power system stabilization by SMES control. In this paper investigates the self tuning control scheme for SMES is proposed and applied to Automatic Generation Control (AGC) in power system. The system is assumed to be consisting of two areas. The proposed self-tuning control scheme is used to implement the automatic generation control for load frequency control application adding to conventional control configuration. The effects of the self tuning configuration with Fuzzy Proportional Integral Controller (FPIC) in AGC on SMES control for the improvement of Load Frequency Control (LFC) is compared with that of PI controlled AGC. The effectiveness of the SMES control technique is investigated when Area Control Error (ACE) is used as the control input to SMES. The computer simulation of the two-area interconnected power system shows that the self tuning FPIC control scheme of AGC is very effective in damping out of the oscillations caused by load disturbances in one or both of the areas and it is also seen that the FPIC controlled SMES performs primary frequency control more effectively compared to PI controlled SMES in AGC control

This paper presents change detection approach for synthetic aperture radar images based on log and mean ratio operators, image fusion and a novel fuzzy local information c means Clustering algorithm. The two multi-temporal SAR images are subjected to ratio operators. The log ratio operator is used to produce the difference image with enhanced low intensity pixels. The mean ratio operator suppresses the unchanged region and improves the homogeneity of the changed region. The difference image is then subjected to image fusion. To improve the intensity of the background pixels Wavelet based image fusion technique is used. The fused image is then subjected to a novel fuzzy algorithm which is Reformulated Fuzzy Local Information C Means Clustering Algorithm. Finally the total number of pixels varied from the difference image is calculated. The kappa and Percentage of correct classification values are more than other algorithms.

Hiding the information is a vital issue in the 21st century in the field of Data Communication security .It is an important issue because the virtual and digital information transmission faces critical setbacks due to hacking and hackers threats. The transmission of information via the Internet may expose it to detect and theft. So data embedding technologies are developed to provide personal privacy, commercial and national security interests. In this work we consider the problem of extracting blindly data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video).We develop a novel multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multicarrier spread-spectrum embedding. Here the original host and the embedding carriers both are assumed as not available. Experimental results shows that the proposed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.

The automatic identification of the modulation type of a signal at the receiver is a major task of an intelligent receiver, such as software defined radio (SDR), which is used in different communication systems. With no knowledge of the transmitted data, recognition of the modulation type is a difficult task. A communication system, in which receiver is designed based on Machine learning is trained to detect the message using statistical parameters. In this work, machine learning algorithm is developed for identifying 6 different modulated signal types at the receiving end. MATLAB tool is used to generate different modulated signals. Identification of modulation type is done by analyzing different statistical features such as normalized PSD, kurtosis and sum-fft which are calculated using the sampled version of received signal. The algorithm is tested for different frequency and amplitude signals at the receiving end and results are tested for correctness.