arXiv subjects

Md Hasibul Hasan

Publications and source records attributed to Md Hasibul Hasan.

3 recordsLinked to original sources

arXiv2026

A Non-Invasive Cloud-Based Migration Strategy for Post-Quantum Cybersecurity in Smart HVAC Systems: Architecture, Implementation, and Empirical Evaluation

Legacy smart HVAC controllers rely on vendor-cloud TLS secured by ECDH and RSA, both broken by Shor's algorithm, and typical 10-15 year lifespans mean today's devices remain in service through the quantum-threat era. Direct on-device post-quantum cryptography is infeasible: an ESP32-S3, representative of capable HVAC hardware, has only 339 KB free heap against the 900 KB ML-KEM-768 requires, and even classical ECDH-P256 keygen (111.93 ms) dwarfs hardware AES-128 (0.032 ms). We propose a non-invasive PQC proxy, requiring no device, firmware, or vendor-cloud changes, performing ML-KEM-768 encapsulation and ML-DSA-65 authentication (NIST FIPS 203/204) with AES-256-GCM session keys via HKDF, implemented with Open Quantum Safe liboqs on a Raspberry Pi 4B gateway. Over 500 runs, the post-quantum handshake (Steps 1-6) completes in 2.48 ms, 0.38 ms slower than classical baseline, with PQC computation around 8% of handshake time at 20 ms simulated round-trip network latency. The gateway sustains 443 sessions/second, 100% success under 32 concurrent connections, extrapolating to 3546 sessions/second on a 32-core cloud instance. Five side-channel tests, including verified in-place session-key zeroization and a fixed-vs-random TVLA timing analysis, found no exploitable timing leakage or susceptibility to man-in-the-middle attacks. The architecture is vendor-agnostic and becomes unnecessary once vendors adopt NIST PQC natively.

cs.CR↗

arXiv2026

MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck. Existing deep learning systems face three compounding failures: end-to-end detectors treat unannotated cells as background, skewing recall by annotation completeness rather than true cell recovery; Non-Maximum Suppression suppresses valid detections in dense smears; and pipelines lack per-cell spatial evidence for clinical audit. We present MalariAI, a two-stage decoupled framework addressing all three. Stage 1 applies an annotation-agnostic watershed algorithm to isolate every cell in a full 1600x1200 image, recovering 75.95% of ground-truth cells without any ground-truth input. End-to-end, the pipeline reaches a binary parasitized AP@0.5 of 29.10% - the clinically relevant metric for flagging any infected cell - while the stricter multi-class mAP@0.5 of 8.67% mainly reflects watershed's organic region boundaries being penalized against axis-aligned ground-truth boxes, not a localisation failure. Stage 2 fine-tunes EfficientNet-B0 with Focal Loss on ground-truth crops, achieving 98.36% classification accuracy - an oracle upper bound once a cell is correctly localised - with 87.5% and 75.0% accuracy on the rare schizont and gametocyte stages, versus 38.45% and 57.27% AP for a modern YOLOv8s detector evaluated end-to-end on the same classes. Grad-CAM++ heatmaps generated per detected cell provide instance-level spatial evidence for clinical audit; a quantitative energy-in-box analysis confirms this activation is concentrated on the annotated cell body significantly above a geometric chance baseline (+0.0485, paired p = 1.4 x 10^-33), letting microscopists verify predictions at the individual parasite level without sacrificing classification performance.

eess.IV↗

arXiv2026

Comprehensive Dataset and Signal Processing Framework for Phonocardiogram-Based Heart Rate and Blood Pressure Estimation

Cardiovascular diseases (CVDs) represent significant global health challenges today, necessitating regular and reliable monitoring to enable early intervention. Phonocardiogram (PCG) signals present a promising non-invasive method for assessing cardiovascular health. While recent studies have focused on estimating heart rate (HR) from PCG signals and blood pressure (BP) through multimodal combinations with other physiological data, reliable and cost-effective systems that can predict both HR and BP using only PCG signals remain largely unexplored. In this study, we proposed and developed a lab-scale cost-effective Phonocardiogram Tracking (PhonoTrack) system that can measure both HR and BP using only the PCG signal. We also introduced a corresponding dataset collected from 15 participants to evaluate the effectiveness of the proposed system. HR was determined using several peak detection methods, such as Hilbert Transform (HT), Shannon Entropy (SE), and WES, achieving notable Pearson correlation coefficients of 0.965, 0.973, and 0.955, respectively. The corresponding root mean square errors (RMSEs) were 2.467 bpm, 1.688 bpm, and 1.992 bpm for HT, SE, and WES, respectively. Additionally, we developed an advanced semi-empirical model based on multiple regression techniques to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). This model demonstrated standard deviations of 2.10 mmHg for SBP and 3.20 mmHg for DBP across all subjects, with Pearson correlation coefficients of 0.89 and 0.70, respectively. These findings pave the way for developing a non-invasive, low-cost, and portable PhonoTrack device, positioning it as a promising solution for continuous cardiovascular monitoring settings.

eess.SP↗