Md Baharul Islam

Vehicular ad hoc networks (VANETs) are becoming popular due to autonomy and intelligent transportation systems (ITSs), and they play a key role in enabling the broader concept of the Internet of Vehicles (IoV). Moreover, VANETs will have cutting-edge improvements with the emergence of 5G and 6G technologies. Recent research has concentrated on reducing delay and increasing throughput. To improve the performance of VANETs, a novel MIMO-OFDM-based cooperative vehicular safety message communication mechanism is proposed in this work. Optimal relay selection in cooperative communication is essential in maximizing system performance. This article presents an optimal relay selection mechanism based on the signal-to-noise ratio (SNR) and vehicle velocity for VANETs. First, an SNR-based relay selection mechanism is analyzed separately, and then an optimal relay selection mechanism is suggested based on vehicle velocity. To maximize the benefits of multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM), both are combined, and a novel access mechanism is suggested to accommodate MIMO with OFDM. A Markov chain-based mathematical model is derived. Nakagami-m fading channels are considered in the analysis. The performance is evaluated in terms of successful transmission probability, outage probability, throughput, and delay terms. Simulation of urban mobility (SUMO) generates a microscopic mobility model for practical application. For the sake of comparison and to highlight the performance achievable gains, comparisons with existing mechanisms are presented, which reveal significant improvement in performance. The simulation results reveal that significant improvements can be obtained with the proposed system relative to other existing schemes.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects about 1 in 100 children worldwide. Early detection supports timely intervention, but many families still face long waits and uneven access to specialists. In low-resource settings, diagnostic delays can reach around 24 months. In this paper, we synthesize technology-enabled approaches for ASD detection across four data modalities. These include facial expression and gaze analysis, voice and audio assessment, sensor and brain-imaging signals, and multimodal integration. We summarize the main methods and report performance ranges from 68.5% to 98.9% accuracy across studies. We find that deep learning approaches often perform best, especially when they use transfer learning and multimodal fusion. Compared with prior reviews that focus on a single modality or emphasize algorithms alone, we connect performance evidence to clinical readiness factors such as cost, accessibility, scalability, validation, and real-world deployment constraints. At the same time, key barriers remain. Common issues include limited dataset diversity, weak cross-cultural generalizability, a lack of clinical validation, and ethical risks in real-world deployment. We close by outlining practical directions for building detection tools that are clinically useful and globally accessible.

Corporate sustainability reports are often difficult for non-expert audiences to understand because they are typically presented as static, number-dense PDF documents. This paper presents an interactive Augmented Reality (AR) system that transforms Herc Rentals' Corporate Citizenship Report data into spatially anchored 3D visualizations on a commodity Android smartphone. Developed using Unity and AR Foundation, the system detects the corporate logo and dynamically renders three scaled emission spheres representing Scope 1, Scope 2, and Scope 3 greenhouse gas emissions, totaling 358,841 MT CO 2 e in 2024. By tapping a sphere, users can access contextual panels that provide category-level breakdowns and carbon offset equivalencies derived from U.S. Environmental Protection Agency (EPA) greenhouse gas equivalency factors. A preliminary user evaluation with six participants (N = 6), using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX), produced a mean SUS score of 77.9, surpassing the target threshold of 75, along with generally low workload ratings across NASA-TLX dimensions. These findings suggest that AR-based sustainability visualization can improve comprehension, engagement, and environmental awareness in corporate reporting contexts.

This paper presents a feasibility analysis of saliency detection algorithms for stereo video retargeting in assistive augmented reality (AR) systems. Targeting the Magic Leap 2 headset, we benchmark a diverse set of classical and lightweight deep learning models, including AIM, SUN, IKN, BMS, U 2 -Net, and SAM-Net, evaluating their performance in terms of segmentation accuracy, inference speed, and deployment efficiency. Unlike prior approaches that rely on computationally intensive deep neural networks, our goal is to identify models compatible with the limited hardware acceleration available in wearable AR devices. A personalized visual mask, calibrated through user-specific visual profiles, guides the adaptive relocation of salient regions into areas of preserved vision, enabling dynamic field-of-view compensation for individuals with peripheral vision impairments. Experimental results demonstrate that U 2 -Net Lite and SAM-Net offer a practical balance between accuracy and computational cost, while classical methods like BMS remain viable for real-time AR integration. This work contributes to the development of low-latency, vision-aware AR systems, advancing stereo-retargeting pipelines for accessibility-focused applications. All benchmarking code, evaluation scripts, and model wrappers are available upon request.

Cybersickness remains a major barrier to comfortable virtual reality (VR) use, particularly during rapid changes in motion. We present a jerk-triggered bone-conducted vibration (BCV) system that delivers brief BCV stimuli shortly after the peak of linear jerk, targeting the transition where linear acceleration begins to decrease. This timing is designed to induce an inverse coupling effect by introducing BCV as acceleration decays. We develop a low-cost hardware platform and a software pipeline that enables real-time triggering and seamless compatibility with existing SteamVR applications. To support practical deployment, we introduce a lightweight earhook mounting concept that clips directly onto a VR headset and maintains consistent contact with the mastoid without headbands, calibration, or user-specific fitting. We report engineering validation of system operation, including end-to-end triggering behavior and integration reliability, and outline a future controlled human-subject study to evaluate effectiveness for cybersickness mitigation.

Thermal surveillance systems deployed over months must remain accurate despite environmental thermal drift induced by seasonal weather variation, ambient temperature changes, and sensor recalibration. We present results on the Long-Term Thermal Detection (LTDv2) benchmark using a YOLOv11m detector adapted for single-channel long-wave infrared (LWIR) imagery via a thermally consistent training pipeline: we disable RGB-centric color augmentations (e.g., HSV hue/saturation) that are invalid for grayscale LWIR and emphasize intensity-domain perturbations that better reflect long-term drift, retaining controlled brightness variation to emulate sensor aging and scene temperature shifts while keeping the remainder of the YOLOv11 training recipe unchanged for stable optimization. On the 8-month LTDv2 benchmark, our approach achieves a weighted score of 0.448, improving over the official baseline score of 0.429 (by 4.4%), with an overall mAP@50 of 0.505. A configuration study on the validation split further characterizes the impact of key training choices. Temporal robustness analysis reveals pronounced seasonality, with peak performance in February (mAP@50 = 0.610) under cooler conditions and degradation in April (mAP@50 = 0.438) during seasonal transition. Our findings suggest that drift-aware, grayscale-consistent augmentation is a straightforward and effective lever for improving long-term LWIR detection, while temporal diagnostics are essential for understanding failure modes in real deployments.

Despite progress in sign recognition, real-time systems that can robustly handle partial or uncertain visual cues remain limited. This challenge is especially pronounced when only a small set of keywords can be reliably detected, yet the system must still generate fluent, contextually appropriate natural-language output. In this paper, we present SignGPT, a real-time American Sign Language (ASL) keyword spotting and LLM-guided natural language generation system. We extract hand and upper-body landmarks and image crops using MediaPipe. We then employ a compact visual encoder with a BiLSTM/GRU temporal head trained with Connectionist Temporal Classification (CTC) to recognize fifteen ASL keywords without frame-level alignments. The detected keyword sequence (typically 2-5 keywords) conditions a large language model (LLM) to perform contextual sentence completion, returning the top three English sentence candidates consistent with the recognized keywords and a task prompt (e.g., domain constraints, style, or register). We outline an evaluation protocol that combines keyword-level sequence accuracy with sentence-level quality assessed via human preference studies. On a held-out test set, SignGPT achieves 97.0% sentence-level accuracy with LLM guidance versus 73.5% without, demonstrating that LLM-guided contextual completion effectively bridges real-time keyword spotting and coherent ASL-to-English sentence generation.

Multimodal biosensing is a promising route to reliable upper-limb intent detection for rehabilitation and humanrobot interaction. In the ABC 2026 Multimodal Upper-limb Movement Intent Detection Challenge (MUMIDC), synchronized EEG, EMG, and IMU recordings are provided together with trigger timestamps that delineate task stages. We propose a lightweight, trigger-aligned pipeline that (i) robustly parses raw MindRove CSV files (including corrupted/header-only trials), (ii) reconciles timestamp timebases and aligns multimodal streams to trigger events, (iii) extracts fixed-length statistical descriptors from cue and action windows, and (iv) compares unimodal and fusion strategies under two official protocols: Experiment 1 (within-subject) and Experiment 2 (leave-one-subject-out, LOSO). Across LOSO action decoding, EMG+IMU achieved 0.695 ± 0.208 accuracy and 0.694 ± 0.208 macro-F1, while lightweight late fusion with weight w=0.85 achieved 𝟎. 𝟔 𝟗 𝟔 ± 𝟎. 𝟐 𝟎 𝟖 accuracy and 0.694 ± 0.208 macro-F1. LOSO cue decoding was substantially more challenging; the best model (late fusion) reached 0.375 ± 0.101 accuracy and 0.362 ± 0.097 macro-F1. We analyze practical data issues encountered in the raw recordings and show that within-subject feature normalization and late fusion improve robustness, while cross-subject generalization remains the primary limitation for early-stage intention decoding.

Indoor location recognition in nursing care facilities is critical for activity monitoring and safety management. Bluetooth Low Energy (BLE) beacon signals collected in real-world environments are noisy and highly imbalanced across locations. But these noises and imbalances make reliable localization challenging. This study aims to develop and evaluate a robust BLE-based indoor location recognition framework that effectively captures short-term temporal signal patterns under realistic care-facility conditions. An established Temporal Convolutional Network (TCN) architecture is applied as part of a TCN-based framework. BLE RSSI signals are provided by the ABC 2026 Activity and Location Recognition Challenge (ALRC). The approach integrates RSSI aggregation, normalization, and masking of missing beacon observations. Then, a fixed-length temporal windowing is used to model temporal dependencies in BLE sequences. Model performance is assessed using a stratified training–validation split, with Macro F1-score adopted as the primary metric to address severe class imbalance. Experimental results demonstrate that the proposed framework achieves a validation accuracy of 0.66 and a Macro F1-score of 0.56 across 22 indoor location classes. Comparative evaluation against five baseline classifiers shows more balanced performance. Particularly, our study has improved recognition of underrepresented locations. The main contribution of this work lies in the adaptation and evaluation of temporal convolutional modeling for BLE-based indoor localization in nursing care environments. Overall, the results indicate that temporal modeling significantly enhances robustness and reliability of BLE-based indoor location recognition in care-facility scenarios.

Indoor localization in nursing homes, addressed through the ABC 2026 'Decode the Invisible' Challenge, faces significant obstacles because of a strong class imbalance, where minority rooms are underrepresented in the training data and high-traffic areas predominate, leading to inadequate location coverage and poor recognition performance. The study suggests an Ultimate inference framework that combines ensemble voting, minority boosting, and Random Forest classification to achieve 100\% prediction coverage while preserving accuracy across all room classes. The technique processes 4,107 CSV files into 40-second intervals using 33-dimensional feature vectors using Bluetooth Low Energy (BLE) beacon signals from 25 stationary transmitters. The scarcity of minority classes is addressed by augmenting data using Gaussian noise injection ($\sigma = 1.0$ dB). The suggested method successfully recovers (1) rooms 503 and 510 from zero recall to 0.800 and 1.000 F1-scores, respectively; (2) detects 20 out of 22 room classes compared to 18 in baseline methods; and (3) achieves a weighted F1-score of 0.7492 and Macro F1-score of 0.6241 with full coverage. While conventional approaches sacrifice coverage for accuracy, our ensemble-based minority recovery approach maintains macro-level fairness without compromising majority class performance. For healthcare settings with limited resources, this provides a consistent solution.