Renewable energy sources are an increasingly popular way to generate electrical energy for telecommunications systems. The use and effectiveness of remote monitoring systems in such systems has led to growing interests relatively to it. As with any new technological systems, new problems have arisen in the use of renewable energy sources in telecommunications systems and the use of IoT-based remote monitoring systems in them. In particular, one of these problems is the decentralization of remote monitoring systems for renewable power plants of telecommunication systems. The paper details the stages of development and modelling of open-source centralized monitoring systems for solar power station of telecommunication systems. In this paper, a real-time remote monitoring system of solar power sources was modelled and investigated by wireless sensor networks of telecommunications devices. Proteus software environment was obtained for modelling. Before modelling the system, a system structure and a block diagram were developed, each of the elements that are part of the system is described. Software for the system was created. The developed structure and software were tested by modelling. The results of the modelling were presented in a virtual terminal, an oscillography, and a local web browser. ABSTRAK: Sumber tenaga boleh baharu semakin meningkat popular dalam menghasilkan tenaga elektrik bagi sistem telekomunikasi. Penggunaan dan keberkesanan sistem pemonitoran jarak jauh dalam jaringan ini telah membawa kepada peningkatan minat kepada tenaga boleh baharu. Walau bagaimanapun, melalui sistem teknologi baru ini, masalah baru timbul dalam penggunaan punca tenaga boleh baharu dalam sistem telekomunikasi dan penggunaan bersama sistem pemonitoran berdasarkan IoT. Khususnya dalam desentralisasi sistem pemonitoran jarak jauh bagi loji kuasa sistem telekomunikasi. Kajian ini merincikan peringkat pembinaan dan model sumber terbuka berpusat sistem pemonitoran bagi sistem janakuasa solar sistem telekomunikasi. Kajian ini turut memodelkan sistem pemonitoran jarak jauh secara langsung bagi sumber kuasa solar dan dikaji dengan rangkaian sensor tanpa jaringan bagi alatan telekomunikasi. Perisian Proteus telah dihasilkan bagi model ini. Sebelum model sistem dibina, struktur sistem dan gambar rajah blok dibina. Setiap unsur dalam sistem ini diperihalkan. Perisian bagi sistem ini turut dibina. Struktur yang dibina dan perisian ini diuji melalui model. Hasil dapatan model dibentangkan dalam pangkalan maya, oscilograf dan pelayar web tempatan.

Background/Objectives: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. Methods: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. Our technique balances heterogeneous feature representation with suppression of individual model biases. Our model was trained and evaluated on a hand-collected set of 1932 labeled images representing eight classes, consisting of common bites such as mosquito, flea, and tick bites, and unaffected skin. Our domain-specific augmentation pipeline imputed practical variability in lighting, occlusion, and skin tone, thereby boosting generalizability. Results: Our model, DeepBiteNet, achieved a training accuracy of 89.7%, validation accuracy of 85.1%, and test accuracy of 84.6%, and surpassed fifteen benchmark CNN architectures on all key indicators, viz., precision (0.880), recall (0.870), and F1-score (0.875). Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. Conclusions: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. Our model, DeepBiteNet, forms a promising foundation for future integration with mobile health (mHealth) solutions and may complement early diagnosis and triage in dermatologically underserved regions.

Proper management of pedestrian flow at urban crosswalks is crucial for alleviating congestion, reducing waiting times, and improving general traffic safety. This paper presents AH-YOLOFlow, a computer vision framework for pedestrian detection, multi-object tracking, and zone-based flow analytics at urban crosswalks. The proposed framework integrates an attention-enhanced YOLO26n detector, Hungarian assignment-based multi-object tracking, polygon-based spatial zone modeling, and key performance indicator aggregation to estimate pedestrian flow rate, waiting behavior, crossing activity, and localized pedestrian density. The framework was evaluated using a 228.74-s CCTV crosswalk video with a fixed split and an augmented training dataset. Experimental results demonstrate that the proposed detector achieved a mAP@50-95 of 0.5794, mAP@50 of 0.8000, precision of 0.9003, and recall of 0.6847, outperforming the evaluated YOLO-family baselines in terms of mAP@50-95 and precision while remaining competitive with RT-DETR. In the end-to-end pedestrian analytics evaluation, AH-YOLOFlow identified 638 zone-entry events and 495 directional movement events, and successfully associated 54.01% of tracked pedestrian identities with predefined spatial regions. Furthermore, a pseudo-MOT evaluation based on spatially propagated identity annotations demonstrated enhanced tracking consistency following data augmentation, achieving a pseudo-MOTA of 0.7671, pseudo-MOTP of 0.9205, pseudo-HOTA of 0.4527, and pseudo-IDF1 of 0.3844. These findings demonstrate that AH-YOLOFlow provides an effective and computationally efficient framework for real-time crosswalk monitoring by integrating pedestrian detection, tracking, and spatial analytics. In future work, we will focus on validating the system across diverse urban environments using larger multi-scene datasets with manually verified identity annotations to further improve tracking robustness and generalization.