arXiv subjects

Saifur Rahman Sabuj

Publications and source records attributed to Saifur Rahman Sabuj.

5 recordsLinked to original sources

arXiv2026

MoWaveQFormer: A Motion-Conditioned Quality-Gated Transformer for Smartphone-Based PPG Heart Rate Estimation

Photoplethysmography (PPG)-based heart-rate (HR) estimation on smartphones remains unreliable in free-living conditions because motion artifacts vary in spectral structure across activities, and benchmarks such as BUT PPG v2.0 label signal quality only as binary good or bad, discarding partially usable data. This study develops an HR estimation architecture that explicitly conditions motion type and signal quality rather than treating both uniformly. We propose MoWaveQFormer, a three-stage architecture trained under ECG supervision. Stage 1 assigns each window a discrete motion group based on accelerometer-derived spectral energy, without trainable parameters. Stage 2 uses this index to select one of the three learnable FIR filter banks for motion-specific spectral shaping of the PPG signal. Stage 3 embeds the resulting sub-bands into patch tokens, re-weights them via a differentiable soft gate derived from the quality label, and encodes them with a Transformer whose pooled output is regressed to HR, trained jointly with an ECG-supervised loss and a pulse-transit-time consistency term. In a subject-independent split of BUT PPG v2.0 (3,888 recordings, 50 subjects), MoWaveQFormer achieved a mean absolute error of 7.85 bpm, the lowest among five methods, with significant improvements over three baselines (Wilcoxon test, p<0.05). Ablation and Bland-Altman analyses characterize each component's contribution. With 816,445 parameters and sub-3-ms latency, MoWaveQFormer suits real-time deployment, pending validation on smartphone hardware. Replacing motion-agnostic filtering and binary quality discarding with differentiable conditioned processing offers a compact pathway to more reliable free-living PPG-based cardiovascular monitoring.

eess.SP↗

arXiv2025

SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification

Brain-computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by enabling communication between the brain and external devices. However, the non-stationary nature of EEG signals and significant inter-subject variability cause substantial challenges for developing robust cross-subject classification models. This paper introduces a novel Spatial-Spectral-Temporal Attention Fusion (SSTAF) Transformer specifically designed for upper-limb motor imagery classification. Our architecture consists of a spectral transformer and a spatial transformer, followed by a transformer block and a classifier network. Each module is integrated with attention mechanisms that dynamically attend to the most discriminative patterns across multiple domains, such as spectral frequencies, spatial electrode locations, and temporal dynamics. The short-time Fourier transform is incorporated to extract features in the time-frequency domain to make it easier for the model to obtain a better feature distinction. We evaluated our SSTAF Transformer model on two publicly available datasets, the EEGMMIDB dataset, and BCI Competition IV-2a. SSTAF Transformer achieves an accuracy of 76.83% and 68.30% in the data sets, respectively, outperforms traditional CNN-based architectures and a few existing transformer-based approaches.

cs.CV↗

arXiv2024

Adaptive Context-Aware Multi-Path Transmission Control for VR/AR Content: A Deep Reinforcement Learning Approach

This paper introduces the Adaptive Context-Aware Multi-Path Transmission Control Protocol (ACMPTCP), an efficient approach designed to optimize the performance of Multi-Path Transmission Control Protocol (MPTCP) for data-intensive applications such as augmented and virtual reality (AR/VR) streaming. ACMPTCP addresses the limitations of conventional MPTCP by leveraging deep reinforcement learning (DRL) for agile end-to-end path management and optimal bandwidth allocation, facilitating path realignment across diverse network environments.

cs.NI↗

arXiv2024

EcoEdgeTwin: Enhanced 6G Network via Mobile Edge Computing and Digital Twin Integration

In the 6G era, integrating Mobile Edge Computing (MEC) and Digital Twin (DT) technologies presents a transformative approach to enhance network performance through predictive, adaptive control for energy-efficient, low-latency communication. This paper presents the EcoEdgeTwin model, an innovative framework that harnesses the synergy between MEC and DT technologies to ensure efficient network operation. We optimize the utility function within the EcoEdgeTwin model to balance enhancing users' Quality of Experience (QoE) and minimizing latency and energy consumption at edge servers. This approach ensures efficient and adaptable network operations, utilizing DT to synchronize and integrate real-time data seamlessly. Our framework achieves this by implementing robust mechanisms for task offloading, service caching, and cost-effective service migration. Additionally, it manages energy consumption related to task processing, communication, and the influence of DT predictions, all essential for optimizing latency and minimizing energy usage. Through the utility model, we also prioritize QoE, fostering a user-centric approach to network management that balances network efficiency with user satisfaction. A cornerstone of our approach is integrating the advantage actor-critic algorithm, marking a pioneering use of deep reinforcement learning for dynamic network management. This strategy addresses challenges in service mobility and network variability, ensuring optimal network performance matrices. Our extensive simulations demonstrate that compared to benchmark models lacking DT integration, EcoEdgeTwin framework significantly reduces energy usage and latency while enhancing QoE.

cs.NI↗

arXiv2020

Cognitive IoT based Health Monitoring Scheme using Non-Orthogonal Multiple Access

It has become very essential to address the limited spectrum capacity and their efficient utilization to support the increasing number of Internet of Things devices. When it comes to medical infrastructure, it becomes very imperative for medical devices to communicate with the base station. In such situations, communication over the wireless medium must provide optimized throughput (data rate) with effectual energy usage, which will ensure precise medical feedback by the responsible staff. Taking into account, it is necessary to operate wireless communication precisely at a higher frequency with more substantial bandwidth and low latency. Cognitive Radio (CR) is traditionally a viable choice, where it identifies and utilizes the vacant spectrum, thus maximizing the primary user's capacity and achieving spectral efficiency. To ensure such outcomes, the Non-Orthogonal Multiple Access (NOMA) techniques have proven to deliver an effective solution to the increasing number of devices with unimpaired performance, especially when the communication shifts towards a higher frequency band such as the mmWave band. In this chapter, IoT based CR network in uplink communication is proposed alongside employing NOMA techniques for optimal throughput, and energy efficiency for a medical infrastructure. Numerical results show that effectual throughput and energy efficiency for a High Reliable Communication (HRC) device and Moderate Reliable Communication (MRC) device improve over 83.13% and 73.95%, respectively and their corresponding energy efficacy values show vast improvement (83.11% and 73.96% respectively). Likewise, for interference case both the throughput and the energy efficiency improve approximately over 93% for all devices.

eess.SP↗