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This paper investigates the critical role of Direct Current to Direct Current (DC-DC) converters in satellite power systems, emphasizing the need for efficient and reliable energy conversion under dynamic space conditions. A novel Fuzzy Model Control (FMC) strategy is proposed for a quasi-Trans-Z-Source DC-DC boost converter, addressing the inherent challenges of nonlinear power dynamics and multiport energy flow management in space applications. The proposed approach enhances mode transitions, improves solar energy extraction from photovoltaic (PV) panels, and ensures stable voltage regulation under fluctuating load conditions. A comprehensive theoretical analysis of the circuit topology highlights its advantages over conventional boost converters, including continuous input current, higher voltage gain, and reduced passive component stress. Simulation and experimental results demonstrate that the proposed FMC achieves ± 1.5% voltage regulation accuracy, reduces current ripple by 28%, and improves transient response time by 35% compared to a conventional Proportional-Integral (PI) controller. The overall system efficiency reaches 94.7% under nominal conditions. Furthermore, the control strategy effectively manages constraints such as duty cycle limits and dynamic disturbances, confirming its real-time applicability for spaceborne platforms such as nanosatellites and CubeSats.

Background A brain tumor is an abnormal tissue growth in the skull that can damage healthy brain areas by exerting pressure. While early detection is vital for prevention, accurate diagnosis with computer-aided design (CAD) systems remains challenging due to variations in tumor shape and location. Aim This paper provided a structured literature survey (SLS) of various machine learning (ML) and deep learning (DL) techniques that were utilized in detection, classification, segmentation, and fusion-based diagnosis involving multiple diagnostic systems and a newly designed convolution neural network (CNN) architecture. Method The SLS was based on reliable papers in the Web of Science (WoS) database and was organized into three phases. The first evaluated recent review papers, identified the number of methodological studies in each, focused on authenticated publications, and analyzed their diagnostic approaches, ending with a critical assessment of the reviews. The second examined recent methodological works in brain tumor diagnosis that were not covered in those reviews, assessing each by its performance metrics. Across these phases, 320 authenticated studies were analyzed. The final phase introduced the detecting and classifying brain tumors (DCBT) system. Results This system combined transferred EfficientNet-B0 (TR_EffNetB0) with a newly developed dual-path CNN architecture, attaining an accuracy of 98.5%. Conclusion The SLS concluded with intuitive key achievements and lessons learned, which made future research easier.

On fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple -Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and active users equipment’s (UE).

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees’ performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by “KF” with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

As the complexity and scope of VLSI designs continue to grow, fault detection processes in the pre-silicon stage have become crucial to guaranteeing reliability in IC design. Most fault detection algorithms can be solved by transforming them into a satisfiability (SAT) problem decipherable by SAT solvers. However, SAT solvers consume significant computational time, as a result of the search space explosion problem. This ever- increasing amount of data can be handled via machine learning techniques known as deep learning algorithms. In this paper, we propose a new approach utilizing deep learning for fault detection (FD) of combinational and sequential circuits in a type of stuck-at-faults. The goal of the proposed semi-supervised FD model is to avoid the search space explosion problem by taking advantage of unsupervised and supervised learning processes. First, the unsupervised learning process attempts to extract underlying concepts of data using Deep sparse autoencoder. Then, the supervised process tends to describe rules of classification that are applied to the reduced features for detecting different stuck-at faults within circuits. The FD model proposes good performance in terms of running time about 187 × compared to other FD algorithm based on SAT solvers. In addition, it is compared to common classical machine learning models such as Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB) classifiers, in terms of validation accuracy. The results show a maximum validation accuracy of the feature extraction process at 99.93%, using Deep sparse autoencoder for combinational circuits. For sequential circuits, stacked sparse autoencoder presents 99.95% as average validation accuracy. The fault detection process delivers around 99.6% maximum validation accuracy for combinational circuits from ISCAS’85 and 99.8% for sequential circuits from ISCAS’89 benchmarks. Moreover, the proposed FD model has achieved a running time of about 1.7x, compared to DT classifier and around 1.6x, compared to RF classifier and GB machine learning classifiers, in terms of validation accuracy in detecting faults occurred in eight different digital circuits. Furthermore, the proposed model outperforms other FD models, based on Radial Basis Function Network (RBFN), achieving 97.8% maximum validation accuracy.

The tremendous growth of the wireless communications and their applications stimulate the urgent need to keep on the available radio spectrum. As a result, cognitive radio (CR) technologies are proposed and developed to manage the limitation of the available spectrum by methods of sensing and sharing the free channels. Wideband spectrum sensing algorithms have a great impact of detecting the vacant channels of the whole spectrum simultaneously. Cooperative sensing techniques are introduced based on sharing users’ sensing outcomes among other users. Therefore, it represents an efficient method to overcome signal shadowing and fading problems. Recently, artificial intelligence (AI) techniques are considered to improve the quality of service (QoS) parameters in cognitive radio networks. In this paper, an adaptive Neuro-Fuzzy interference system (ANFIS) algorithm is proposed in the process of decision-making to detect the optimal and accurate free channels. ANFIS model is trained with some pertinent features over a Music-like channel power level (P MU (k)), channel identity number (k), and channel repetition number. Consequently, the second stage is introduced by applying ANFIS technique on the adaptive blind cooperative wideband spectrum sensing basis to select the optimum required number of cooperative users with increasing performance based on the detected signal to noise ratio (SNR) level per secondary user. Simulation is based on Simulink of five users with different SNR due to fading and shadowing problems. Simulation results proved that, the proposed technique based on cooperative spectrum sensing algorithm with ANFIS model for detection outperformed other traditional detection techniques.

In high-mobility operating scenarios orthogonal frequency division multiplexing (OFDM) system lacks its optimality, intercarrier interference (ICI) occurs and the Doppler shifts deteriorate the orthogonality of the subcarriers. However, this problem can be overcome by utilizing complicated equalizers at the receiver. Discrete cosine transform (DCT) and discrete Hartley transform (DHT) have been used instead of discrete Fourier transform (DFT) in the standard OFDM system. The performance results achieved enhancement over dispersive selective channels that cannot be accomplished with a standard OFDM system, even with utilizing complex equalizers. In this paper, the performance of the DCT-based OFDM system and DHT-based OFDM system is compared and analyzed with respect to DFT-based OFDM system performance over doubly dispersive fading channel at various Doppler shifts, the block diagrams of the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results emphasized that the performance of DCT and DHT-based OFDM systems under doubly dispersive fading conditions scenario outperforms DFT-based OFDM system of order 3 dB energy per bit to noise power spectral density ratio (E /N ).

The explanation of infeasibilities formed in Minimal Unsatisfiable Subformulas (MUSes) is a core task in the analysis of over-constrained Boolean formulas. A wide range of applications necessitate MUS detection including knowledge-based validation, software design, verification and error diagnosis in digital VLSI circuits. Consequently, various enhanced algorithms for determining MUS have been utilized for solving Maximum Satisfiability algorithms and Conjunction Normal Form (CNF) redundancies. Three enhancements are proposed in this paper. The first is a CPU-GPU algorithm for computation of Minimal Correction Subsets (MCSes) optimized for NVIDIA General Purpose Graphics Processing Unit paradigm. The proposed algorithm of generating all MCSes from the encoded CNF instance was developed using our parallel SSGPU solver and implemented using CUDA. The second enhancement is an algorithm for MUS computation based on auto-reduction of the enhanced MCSes for faster MUS detection. The third improved algorithm is for computing MUS directly without using MCSes. The two proposed algorithms outperform techniques as they could locate and explain design faults in digital VLSI circuits in earlier stages of IC design flow. The second proposed routine of MUS extraction was performed by avoiding a non-critical step in calling the SAT solver during MUS computation, leading to improving the performance of the MUS extraction algorithm. The third proposed mechanism for direct extraction of MUS was optimized by reducing the required SAT-solver calls using a classification of clauses in the input formula. Comparative analysis of the proposed algorithm against the Compute All Minimal Unsatisfiable Subsets (CAMUS) algorithm determined 1.54 × faster detection of MUS using ISCAS'85, ISCAS'89 and synthetic benchmarks. Also, the third algorithm for direct MUS computation delivers 17.05 × faster than shrinking algorithm used in MUST (minimal unsatisfiable subset) tool using ISCAS'85 and synthetic benchmarks. Moreover, it was observed that the CPU-GPU algorithm for MCSes computation based on the SSGPU solver delivered 1.92 × faster than its conventional counterpart, based on CUD@SAT equivalent on GPU using ISCAS'85, ISCAS'89 and synthetic Benchmarks.

This paper presents a novel framework comprises of a Propositional Satisfiability (SAT) encoder and solver. The framework responsible for generating and proving a simplified SAT-based formula of digital circuits for Automatic Test Pattern Generation (ATPG) proposes. The parallel algorithms introduced in this work are aimed at both combinational and sequential circuits and optimized on NVIDIA General-Purpose Graphics Processing Unit (GPGPU) paradigm. The SAT encoder presents an efficient method to apply the Boolean Constraint Propagation (BCP) on-the-fly while the generation is running on the GPU. The simplified formula is further proved for satisfiability using an improved parallel solver on GPU. The proposed encoder executes 93 times faster compared to the sequential counterpart. The test generation algorithm using the GPU-accelerated framework delivers about 5.86 speedup on an average compared to the state-of-the-art Lingeling solver. Moreover, the SAT encoder reduced the run time for fault detection by 6.53 and 11.42% on an average when applied to the proposed and the conventional CUD@SAT solvers, respectively, offering promising related work for the future research.

In the second technique, the secret message is encrypted first then LSB technique is applied. [...]Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. First one is a cover image and Second is the secret file which will be hidden by a private key to encrypt the secret file. [...]it's almost impossible for the Human Vision System (HVS) to notice by these slight changes so the possible adversary attacks will be decreased. According to results obtained in this paper, it is clear that we can hide the intended data in messages while minimizing its size, enabling us to transfer the data more securely with less overall burden in capacity in comparison to other algorithms.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR.