Wei Jiang 1 Junrui Li 2 , Jiwei Xu 2 , 1 National Police University for Criminal Justice, Baoding, China, 2Beijing University of Chemical Technology, Beijing, China.
Achieving high-efficiency video compression while preserving perceptual quality is a persistent challenge, especially with the growing demand for high-resolution and high-frame-rate content. In this paper, we propose a novel compression framework that focuses on region-of-interest awareness and per-ceptual optimization to enhance both compression efficiency and visual fidelity. The core of our method lies in identifying foreground regions—treated as regions of interest—using an object detection algorithm prior to encoding. These regions receive prioritized treatment during compression, with finer quantization control guided by a deep learning model. This model, based on a convolutional neural network, dynamically predicts quantization levels for each coding block by incorporating both spatial features and perceptual quality metrics. To further improve encoding performance, we reformulate traditional rate-distortion op-timization by introducing data-driven models that relate bitrate and visual quality to quantization levels. These models serve to generate high-quality training labels and guide quantization decisions during in-ference. Additionally, region-aware encoding control adapts quantization granularity based on the size and significance of detected objects. Experimental results demonstrate that the proposed approach signif-icantly reduces bitrate—achieving an maximum saving of around 19%—while maintaining stable and high perceptual video quality, outperforming conventional video coding techniques and recent learning-based methods.
Stephen Bossmart, Marymount University, United States of America
With hybrid work models and multi-cloud environments, perimeter-less networks are making traditional perimeter-based security models obsolete against more complex cyber threats. Ransomware attacks increased by 50% in 2025, and the number of data breaches reached a record high with losses surpassing $20.8 billion. To overcome these challenges, Zero Trust Architecture (ZTA) relies on continuous verification and least-privilege access, assisted by the use of innovative secure protocols such as TLS 1.3, QUIC, and post-quantum cryptography. The study explores the theoretical principles, deployment strategies, and prospects of Zero Trust, highlighting the essence of continuous authentication, micro-segmentation, and AI-driven threat detection. ZTA, combined with next generation secure protocols, is a significant paradigm shift from trust but verify to never trust, always verify, and heightened protection against a growing cyber threat.
Zero Trust Architecture, Secure Protocols, Network Security, Post-Quantum Cryptography, multi factor authentication
Ryder Wei 1 Douglas Winegarden 2 , 1 Cate School, Carpinteria, CA 93013, 2University of Washington Bothell, 17927 113th Ave NE, Bothell, WA 98011
Autistic and neurodiverse learners frequently experience sensory overload in noisy, multi-speaker environments such as classrooms, where an inability to suppress irrelevant auditory input impedes comprehension and participation. This paper presents Voxent, a cross-platform mobile system that translates the selective permeability of a biological membrane into a computational pipeline admitting only the most relevant spoken content. Voxent captures or imports audio, transcribes it with a large-scale weakly supervised speech-recognition model, and reshapes the raw transcript into concise, structure-appropriate notes through a context-conditioned large language model. Five interaction modes — Classroom, Group Collaboration, Family Conversation, Task Sequence, and Public Setting — tailor the output to distinct real-world scenarios. The system is a Flutter client backed by a serverless Firebase architecture in which all model inference runs inside authenticated Cloud Functions, keeping credentials off the device. In a pilot study with twenty-four participants, Voxent earned a System Usability Scale score of 82.4, and 92% of participants preferred mode-formatted notes over raw verbatim transcripts..
assistive technology, automatic speech recognition, neurodiversity, selective attention, large language models, mobile computing, accessibility, cognitive load, serverless architecture, human-computer interaction
Henry Yang 1 Jonathan Sahagun 2 , 1 Sage Hill School, 20402 Newport Coast Dr, Newport Coast, CA 92657, USA , 2 California State University, Los Angeles, 5151 State University Dr, Los Angeles, CA 90032
Inconsistent tooth-brushing contributes to oral diseases that affect roughly half of the global population, yet the behaviour itself is private, repetitive, and unmeasured, which blunts the effect of conventional reminders and verbal instruction. This paper presents Brush Sense, a gamified Internet-of-Things system that couples an accelerometer- equipped smart brush with a mobile application and a cloud backend to measure, score, and socially reinforce daily brushing. An ESP32-S3 device carrying an MPU6050 inertial sensor classifies motion with dual acceleration thresholds, scores vigorous strokes in real time through a combo-and-zone game loop, and streams a self-contained state protocol over Bluetooth Low Energy to a Flutter application backed by Firebase authentication and a Realtime Database holding statistics, streaks, achievements, friends, and leaderboards. Evaluation on the physical prototype shows 97.5% precision and 98.8% recall for session-start detection, a monotonic 2.2× score response across four controlled brushing intensities, and state delivery above a 95% reliability target through five metres.
Smart toothbrush, Internet of Things, gamification, Bluetooth Low Energy, accelerometer, inertial sensing, mobile health, habit formation, embedded systems, Flutter, Firebase, behaviour change
Copyright © BDIoT 2026
