Journal of Applied Research and Technology

Enhanced beam attachment recognition for massive MIMO systems in dense distributed renewable energy networks

Mohammad Anwar Assaad — 2025-08-31

Incorporating large-scale multiple-input multiple-output (MIMO) systems in densely deployed renewable energy systems (RES) represents a significant challenge in developing next-generation wireless networks. This field combines cutting-edge communication technologies with sustainable energy systems to enhance network communication and energy management in smart grid applications. Furthermore, varying energy availability in RES-based environments and dynamic load profiles make it difficult to achieve optimal beam attachment in mmWave massive MIMO systems. Conventional beam attachment techniques perform poorly in such dynamic conditions, resulting in poor network performance and high latency. This has created the need for better and more versatile approaches to beam attachment that can address this inherent variability of RES while at the same time providing highly accurate and low-complexity solutions. This paper presents an improved beam attachment recognition system explicitly designed to operate in RES conditions. Thus, the innovative strategy presented in this work is based on ensemble learning, which includes Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, making the prediction more accurate and the system more stable. The proposed method integrates RES-specific signal strength, interference, traffic load, and renewable energy availability into the choice of the preferred beam. Cohesive simulations support our approach in this case. The Random Forest (RF) classifier test accuracy was 97.56%, and the XGBoost classifier was 97.84% – both of which are higher than conventional methods. Analyzing the feature importance of the problem, it was found that distance, angle, and signal strength were the most significant factors in beam assignment. The performance of the system was also very impressive in terms of scalability, with accuracy rates barely flinching even as the number of samples reached 50,000. Also, the energy efficiency analysis showed that the proposed beam attachment approach could lead to more energy-efficient network operations.

A novel miniaturized frequency reconfigurable microstrip patch antenna for modern wireless applications

D. O. Sakle — 2025-08-31

In this paper a novel miniaturized frequency reconfigurable microstrip patch antenna for wireless communication is presented. The antenna consists of a square patch and for achieving miniaturization the ground plane of the antenna is modified. The antenna incorporates two switches for achieving frequency reconfigurability. By controlling the states of the switches, the antenna exhibits three distinct operating modes, two single band (i.e. 2.42 GHz and 5.55 GHz) and a dual band (i.e. 3.77 GHz, 6.21 GHz). For the proposed antenna the reflection coefficient (S11) is well below -10 dB. The proposed antenna having geometric configuration of 16 × 17 × 0.8 mm3 is designed and simulated using CST Microwave studio. A prototype of the antenna is fabricated using low cost FR-4 glass epoxy substrate and the reflection coefficient is measured using Vector Network Analyzer. The simulated reflection coefficient for all the modes are presented and compared with the experimental data validating its usefulness for modern wireless system applications.

Continuous improvement of concrete properties using recycled cardboard ash: A sustainable alternative to cement

C. J. Carrasco-Ahen — 2025-08-31

The use of alternative materials in construction is a growing trend aimed at reducing the environmental impact of cement production. This study evaluated the effect of cardboard ash as a partial cement substitute on the properties of concrete. An experimental methodology was used with 2.5%, 5% and 7.5% additions of cardboard ash. The results indicated that concrete with 5% cardboard ash reduced the absorption speed to 0.229 mm at 831.38 s^½ compared to the reference concrete. In addition, an improvement in mechanical strength was observed, with increases of 15.10%, 16.99% and 18.41% in compressive strength, and 13.76%, 19.63% and 27.33% in flexural strength, highlighting the potential of cardboard ash to optimize concrete properties. These results were evaluated according to ASTM standards. In conclusion, the use of cardboard ash in concrete production contributes significantly to sustainability by reducing CO(2) emissions, making this material a greener and more environmentally efficient option.

Portable mini antenna for detecting and distinguishing between types of hemorrhagic stroke

J. N. Shehab — 2025-08-31

Stroke is a major cause of disability and death worldwide, and it constitutes a considerable burden on global health. Thus, prompt detection of strokes reduces the possible risk of death. In this work, an antenna in the form of a Moroccan decoration was proposed for early detection of hemorrhagic stroke. This includes testing the five main types of hemorrhagic stroke, which are subdural, epidural, subarachnoid, intracerebral, and intraventricular, and this method provides the possibility of distinguishing between them, thus reducing the rate of death and disability by a large percentage. The proposed antenna is characterized by its small size with dimensions of 30×35×0.62 mm3, a resonance frequency of 2.806 GHz, a 3.196 GHz bandwidth, and a 2.8 dB gain. The simulation results of the proposed antenna show high efficiency in detecting and distinguishing hemorrhagic strokes, as indicated by clear changes in resonance frequency and return losses S11 caused by brain percentage change due to hemorrhagic stroke.

Tuning a PID controller using genetic algorithms

D. López-Reyna — 2025-06-30

This paper details the development of PID controller tuning, based on the implementation of advanced optimization techniques in MATLAB to find the optimal gains for control actions. The methodology used to create the solutions was that of genetic algorithms, an artificial intelligence technique developed in the 1970s and inspired by Darwin's natural selection, within the field of evolutionary computing. Its implementation is based on selection, crossover, and mutation processes, which allow the solutions to iteratively converge towards increasingly optimal results. Two different genetic algorithms were programmed and designed. The first focused exclusively on a single objective, which was the settling time; while the second was based on a multi-objective technique that additionally considered the maximum overshoot, rise time, and delay time. Different fitness functions were developed to create these neural models; subsequently, the gain results obtained from these genetic methods were compared with those proposed by analytical and experimental methods, both in the field of simulation and in physical implementation. The analysis of the responses validated the
efficiency and effectiveness of the proposed algorithms for controller tuning, showing better performance with the gains obtained through genetic algorithms.

Enhanced secure path selection model for underwater acoustic sensor networks using advanced machine learning and optimization techniques

S. Palanivel Rajan — 2025-06-30

The underwater acoustic sensor network is a large network consisting of many operating sensor nodes that surround a transmitting node. The communication process faces substantial disturbances caused by the everchanging nature of the underwater acoustic channel, which is characterized by fluctuating properties in both time and location. Therefore, the underwater acoustic communication system has difficulties in reducing interference and improving communication efficiency and quality by using adaptive modulation. This work presents a model that aims to tackle these difficulties by suggesting an optimum route selection and safe data transmission
approach in UASN using sophisticated technology. The suggested approach for transferring safe data in UASN via optimum route selection consists of two main stages. Nodes are first chosen based on restrictions such as energy, distance, and connection quality, which are quantified in terms of throughput. Moreover, the process of forecasting energy is made easier by using sophisticated machine learning methods like transformer models. The ideal route is generated using a hybrid optimization technique called enhanced swarm optimization, which combines ideas from particle swarm optimization and genetic algorithms. Afterward, data is safely transported via the most efficient route by using fully homomorphic encryption. Finally, the ESO+ transformer model that was created is tested against established benchmark models, showcasing its strong and reliable performance. The proposed model demonstrates remarkable performance with an accuracy of 95.12%, precision of 94.83%, specificity of 93.65%, sensitivity of 95.28%, false positive rate of 4.72%, F1 score of 94.95%, Matthews correlation coefficient of 94.85%, false negative rate of 4.72%, negative predictive value of 95.15%, and false discovery rate of 5.15% when trained on a learning percentage of 70%.

BT-GANformer: A generative ensemble transformer mechanism for brain tumor segmentation and classification

P. Mishra — 2025-08-31

The segmentation task for brain tumors from Magnetic Resonance Imaging (MRI) has been challenging and crucial to radiologists in their decision-making process. The recent developments in the attention mechanism in Natural Language Processing tasks have gained wide popularity and potential applications in Computer Vision and related problems. This article proposes a Generative ensembled Vision Transformer that achieves a State-of-the-Art (SOTA) performance in segmenting Brain tumors from multiple modalities of MRI scans. The proposed method includes an encoder and decoder block with CNN and Transformer, which forms the Generative architecture. The discriminator distinguishes the predictions of the Generator from the ground truth and consists of convolution layers along with a softmax for the classification tasks. The model was trained using the BraTS 2021 Task 1 dataset for the segmentation, and the Task 2 dataset was applied to evaluate the classification task. The proposed model scores a DICE average of 91% with a phenomenal score in tumor-core (TC), enhancing-tumor (ET), and whole-tumor (WT) categories. The model scores 99% ROC AUC score in the methylguanine‐methyltransferase (MGMT) classification task.

Kinematic and dynamic analysis through the robotic formulation of the clavicle-shoulder joint of a human upper extremity

L. A. Mejía — 2025-06-30

This paper presents the kinematic and dynamic modeling of the shoulder-clavicle assembly constituting a four–degrees-of-freedom mechanical system. The models are obtained through robotic concepts and formulations, applied to a specific case of arm abduction with movement in the acceleration and deceleration phase, and compared with its equivalent static model. The influence of a suspended mass at the arm´s end is also analyzed. Subsequently, a biomechanical model considering the muscular action of the deltoid muscle is created based on the dynamic model obtained, allowing estimation of the force exerted by the moving muscle.

Designing and optimizing RIS unit cell with CST for mm-waves

H. TH. S. ALRikabi — 2025-08-31

Wireless communication is being modernized with Reconfigurable Intelligent Surface (RIS) antennas, which enhance signal coverage, capacity, and energy efficiency. This project depicts the Rogers RT duroid 5880 substrate design and simulation for a 28 GHz RIS antenna. Phase stability with varactor integration was without warning detected in initial probations using a 'P' shaped unit cell. The design was polished to a 'R' shape and a stripline was added to boost phase response and delegate for dynamic phase modification. For electromagnetic simulations, CST software was utilized; however, MATLAB conceivable accurate phase visualization, hence rout CST's downsides. The resulting design shows amplified system efficiency, beamforming, and variability. However, because of analytical limitations, simulating a 32 by 32 RIS array caused difficulties. In the face of this, the discoveries highlight how RIS antennas can luxuriously improve wireless communication performance, especially in knotted settings.

Adaptive Archimedes optimization algorithm trained deep learning for polycystic ovary syndrome detection using ultrasound image

K. R. Shelke — 2025-08-31

Polycystic ovary syndrome (PCOS) disorder is caused by a protracted menstruation cycle that frequently elevated the androgen levels of women in their reproductive age. Insulin resistance affects 50% to 70% of all women with PCOS, and hormone difference contributes the high levels of testosterone that causes the symptoms and signs of PCOS. This work develops a deep learning (DL)-based PCOS diagnosis to address these issues. At the initial stage, the ultra sound image is preprocessed by means of adaptive Wiener filter for noise removal process. The Polycystic ovary (PCO) follicles segmentation process is performed using the Fuzz Local C-Means Clustering (FLICM). Feature extraction is the neat stage, where the Speeded-Up Robust Feature (SURF), Shape index histogram as well as the statistical features includes variance, mean, kurtosis, entropy and standard deviation are extracted. Furthermore, the PCOS detection is done in the next stage, where a deep Q Net (DQN) is utilized and the parameters of DQN is optimized by the adaptive Archimedes optimization algorithm (AOA). Moreover, the system performance is evaluated using accuracy, sensitivity and specificity parameters with the corresponding values like 0.906, 0.918 and 0.928.