Wireless Mesh Networks (WMNs) offer a promising approach to pervasive communication with efficient network coverage using minimal infrastructure. However, current WMN routing protocols are inefficient due to high energy consumption. This is caused by limited battery life of nodes, uneven distribution of traffic (load imbalance), and long data transmission distances, all of which shorten network lifetime. This paper proposes a new routing protocol for WMNs called Modified Chicken Swarm Optimization-based Efficient Cluster Head Selection (MCSO-ECHS). MCSO-ECHS leverages the network gateway to select optimal Cluster Heads (CHs). An objective function, considering both residual energy and node distance, is used for CH selection. The MCSO algorithm then refines the selection process to ensure balanced energy consumption among these energy-constrained nodes. This approach prolongs the network lifetime and improves overall energy efficiency. Simulation results demonstrate that MCSO-ECHS outperforms existing protocols in terms of energy consumption, network lifetime, throughput, end-to-end delay and packet delivery ratio, significantly enhancing the energy efficiency of WMNs.
The Present CubeSat project success rate may deter nonprofit organizations from beginning new projects, especially for first-time creators. However, since the electronic components of a CubeSat are intended to be very power-efficient and tightly placed, its size and electrical characteristics provide a more difficult limitation. The CubeSat antennas are key parts that will need to be carefully designed since they need to be tiny, light, and deployable for bigger antennas. This study provides an extensive overview of the key characteristics of metasurface-based antennas with an emphasis on their effectiveness in CubeSat communication systems. This research work initially introduces metasurface antennas and examines how well-suited they are geometrically for various frequency bands for CubeSat spacecraft. Furthermore, a detailed analysis of these metasurface antennas’ radiating capabilities is conducted in accordance with the CubeSat configuration, links, and orbits. Additionally, over thirty X-band metasurface-based antennas are fully evaluated in terms of their suitability for CubeSats. The use of specifically designed metasurfaces has resulted in a notable increase in CubeSat antenna performance. This paper offers an emerging approach for researchers to advance the usage of metasurface-based antennas in CubeSat missions such as UM5-Ribat and UM5-EOSAT CubeSats of University Mohammed V in Rabat.
Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. Lévy distributions, with heavy tails and a location parameter (δ), provide a more accurate model for non-Gaussian and non-centered clutter in complex environments. This paper presents a comprehensive performance analysis of three widely used CFAR processors—cell-averaging (CA), greatest-of (GO), and smallest-of (SO) in homogeneous Lévy-distributed clutter with an arbitrary δ. We derive integral-form expressions for the probability of false alarm (PFA) for each processor, explicitly incorporating δ. Furthermore, we provide analytical formulations for the probability density function (PDF) of key statistics involving Lévy random variables, such as sums, minima, and maxima. Monte Carlo simulations validate the theoretical results, showing that the PFA performance improves with increasing δ, highlighting the critical impact of clutter location on CFAR detector performance. These findings offer valuable insights for designing robust CFAR detectors in non-Gaussian, non-centered clutter environments.
Renewable energy sources like wind and solar power experience output voltage fluctuations due to changing weather conditions. To maintain a stable power supply, integrating multiple input sources is essential. A Multi-Input Converter (MIC) provides a more efficient solution by reducing the need for numerous passive components, which in turn minimizes cost, size, and weight compared to separate converters. This study introduces a dual-input boost converter with zero voltage switching (ZVS), utilizing a single auxiliary circuit to enable soft switching for all semiconductor elements. This design not only improves efficiency but also retains the advantages of multi-input converters. Additionally, a voltage multiplier is incorporated to enhance the voltage conversion ratio, achieving higher voltage gains. The theoretical analysis of the proposed converter is validated through experimental results, with efficiency measurements demonstrating a 3% improvement over conventional hard-switching designs.
Optical Character Recognition (OCR), especially for scripts with complex structures like Persian script, faces significant challenges in interpreting nuanced characters and contextual variations. This study provides a straightforward and scalable approach to developing omni-font OCR systems. A synthetic dataset incorporating words, numbers, punctuation marks, mathematic symbols, and whitespace characters is developed to evaluate YOLO's capability in detecting 70 characters across 15 formal, informal, and handwritten-style fonts. The proposed method for detection of regular space and non-breaking space characters achieved high-precision results that may eliminate the need for a separate word detection stage in an OCR system. In another investigation, we attempted to detect characters from an unseen font by training the model on a batch of other fonts. A formal font such as “B Nazanin” is near-completely detectable without being directly included in the model’s training with a batch of fourteen other fonts. For a handwritten-style font such as “MRT_Sayeh-1”, the mean detectability increases from 54%, when training the model with other single fonts, to 80% when a batch of fourteen other fonts is used. Overall, this study demonstrates that object detection-based OCR models have the potential for Omni-Font text recognition through expanded datasets and advancements in deep learning.
