Search Results (109)

Event-based sensors provide sparse, motion-centric measurements that can reduce data bandwidth and enable always-on perception on resource-constrained edge devices. This paper presents an event-based machine vision framework for smart-home AIoT that couples a Dynamic Vision Sensor (DVS) with compute-efficient algorithms for (i) human/object detection, (ii) 2D human pose estimation, (iii) hand posture recognition for human–machine interfaces. The main methodological contributions are timestamp-based, polarity-agnostic recency encoding that preserves moving-edge structure while suppressing static background, and task-specific network optimizations (architectural reduction and mixed-bit quantization) tailored to sparse event images. With a fixed downstream network, the recency encoding improves action recognition accuracy over temporal accumulation (0.908 vs. 0.896). In a 24 h indoor monitoring experiment (640 × 480), the raw DVS stream is about 30× smaller than conventional CMOS video and remains about 5× smaller after standard compression. For human detection, the optimized event processing reduces computation from 5.8 G to 81 M FLOPs and runtime from 172 ms to 15 ms (more than 11× speed-up). For pose estimation, a pruned HRNet reduces model size from 127 MB to 19 MB and inference time from 70 ms to 6 ms on an NVIDIA Titan X while maintaining a comparable accuracy (mAP from 0.95 to 0.94) on MS COCO 2017 using synthetic event streams generated by an event simulator. For hand posture recognition, a compact CNN achieves 99.19% recall and 0.0926% FAR with 14.31 ms latency on a single i5-4590 CPU core using 10-frame sequence voting. These results indicate that event-based sensing combined with lightweight inference is a practical approach to privacy-friendly, real-time perception under strict edge constraints. Full article

Devices equipped with the Global Positioning System (GPS) generate massive volumes of trajectory data on a daily basis, imposing substantial computational, network, and storage burdens. Online trajectory simplification reduces redundant points in a streaming manner while preserving essential spatial and temporal characteristics. A representative method in this line of research is Directed acyclic graph-based Online Trajectory Simplification (DOTS). However, DOTS does not preserve stay-related information and can incur high computational cost. To address these limitations, we propose Directed acyclic graph-based Online Trajectory Simplification with Stay Areas (DOTSSA), a fast online simplification method that integrates DOTS with an online stay area detection algorithm (SA). In DOTSSA, SA continuously monitors movement patterns to detect stay areas and segments the incoming trajectory accordingly, after which DOTS is applied to the extracted segments. This approach ensures the preservation of stay areas while reducing computational overhead through localized DAG construction. Experimental evaluations on a real-world dataset show that, compared with DOTS, DOTSSA can reduce compression time, while achieving comparable compression ratios and preserving key trajectory features. Full article

Whey and its permeates constitute highly organic, low-alkalinity dairy streams whose management remains suboptimal in many processing facilities. This narrative review integrates recent evidence on the anaerobic digestion (AD) of whey, linking substrate composition and biodegradability with microbial pathways, inhibition mechanisms, biogas quality, and techno-economic and environmental feasibility in industrial settings. Data for sweet whey, acid whey, and their permeates are synthesized, with emphasis on operational windows, micronutrient requirements, and co-digestion or C/N/P/S balancing strategies that sustain resilient methanogenic communities. Options for biogas conditioning and upgrading towards combined heat and power, boiler applications, and compressed or liquefied biomethane are examined, and selection criteria are proposed based on impurity profiles, thermal integration, and methane-recovery performance. Finally, critical R&D gaps are identified, including mechanistic monitoring, bioavailable micronutrition, modular upgrading architectures, and the valorization of digestate as a recovered fertilizer. This review provides an integrated framework to guide the design and operation of technically stable, environmentally verifiable, and economically viable whey-to-biomethane schemes for the dairy industry. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

The designs of lossless data compression architectures create a natural trade-off between throughput, power consumption, and compression efficiency, making it difficult for designers to identify an optimal configuration that satisfies all three criteria. This paper proposes a Configurable Dual-Path Huffman/Arithmetic Encoder (CDP-HAE), which offers an architecture that supports the use of shared preprocessing, parallel path encoding using Huffman and Arithmetic, as well as selectable output. The CDP-HAE’s design prevents the waste of excess bandwidth by sending only one selected bit stream at a time. This also enables adaptation to the dynamically changing statistical characteristics of the input data. CDP-HAE’s architecture underwent ASIC synthesis in 90 nm CMOS technology and is implemented on an Artix-7 (A7-100T) using the Vivado EDA tool, confirming the scalability of the architecture to both devices. Synthesis results show that CDP-HAE improves operating frequency by 28.6% and reduces critical path delay by 27.2% compared to reference designs. Additionally, the dual-path design has a slight increase in area; the area utilization remains within reasonable limits. Power analysis indicates that optimizing logic sharing and minimizing switching activity reduces total power consumption by 34.4%. Compression tests show that the CDP-HAE delivers performance comparable to that of a conventional Huffman Encoder using application-specific datasets. Furthermore, the proposed CDP-HAE achieves performance comparable to conventional Huffman encoders on application-specific datasets, while providing up to 10% improvement in compression ratio over Huffman-only encoding. Full article

Compressing a mixture of soil, water, and stabilizer forms compressed stabilized earth blocks (CSEBs), a modernized earthen construction material capable of performance similar to that of engineered masonry with added sustainability achieved by usage of raw materials on-site, reduction in transportation costs of bulk materials to the build site, and improved thermal performance of built CSEB structures. CSEBs have a wide range of potential physical properties due to variations in base soil, mix composition, stabilizer, admixtures, and initial compression achieved in CSEB creation. While CSEB construction offers several opportunities to improve the sustainability of construction practices, assuring codifiable, standardized mix design for a target strength or durability remains a challenge as the mechanical character of the primary base soil varies from site to site. Quality control may be achieved through creation and testing of CSEB samples, but this adds time to a construction schedule. Such delays may be reduced through development of predictive CSEB compressive strength estimation models. This study experimentally determined CSEB compressive strength for six different mix compositions. Compressive strength predictive models were developed for 7-day and 28-day CSEB samples through multiple numerical models (i.e., linear regression, back-propagation neural network) designed and implemented to relate design inputs to 7-day and 28-day compressive strength. Model results provide insight into the predictive performance of linear regression and back-propagation neural networks operating on designed data streams. Performance, robustness, and significance of changes to the model dataset and feature set are characterized, revealing that linear regression outperformed neural networks on 28-day data and that inclusion of downstream data (i.e., cylinder compressive strength) did not significantly impact model performance. Full article

Intelligent personnel safety management is a critical component of smart manufacturing infrastructure. This paper presents an integrated framework combining a structurally optimized neural network (enhanced with spatial and channel feature fusion mechanisms for multi-scale detection) with an agent-based large language model (LLM) enhanced with retrieval-augmented generation (RAG) capabilities for factory safety monitoring. The visual detection component employs the Similarity-Aware Channel Pruning (SACP) method for automated, performance-preserving compression by identifying and suppressing redundant channels based on similarity and norm regularization, while the agent-based LLM with RAG capabilities dynamically integrates real-time violation data with established safety management protocols to generate precise diagnostic reports and operational recommendations. The optimized network achieves real-time violation detection in parallel video streams, and the LLM-powered assistant facilitates intelligent decision-making through natural language querying. Extensive evaluations on multiple benchmark datasets and a real-world safety helmet detection dataset demonstrate the scheme’s superior performance in both accuracy and practical applicability for industrial deployment. Full article

Image steganography is often employed in information security and confidential communications, yet it typically faces challenges of imperceptibility and robustness during transmission. Meanwhile, insufficient attention has been paid to preserving the quality of the secret image after JPEG compression at the receiver, which limits the effectiveness of steganography. In this study, we propose an anti-compression attention-based diffusion pattern steganography model using GAN (ADPGAN). ADPGAN leverages dense connectivity to fuse shallow and deep image features with secret data, achieving high robustness against JPEG compression. Meanwhile, an enhanced attention module and a discriminator are employed to minimize image distortion caused by data embedding, thereby significantly improving the imperceptibility of the host image. Based on ADPGAN, we propose a novel JPEG-compression-resistant image framework that improves the quality of the recovered image by ensuring that the degradation of the reconstructed image primarily stems from sampling rather than JPEG compression. Unlike direct embedding of full-size secret images, we downsample the secret image into a secret data stream and embed it into the cover image via ADPGAN, demonstrating high distortion resistance and high-fidelity recovery of the secret image. Ablation studies validate the effectiveness of ADPGAN, achieving a 0-bit error rate (BER) under JPEG compression at a quality factor of 20, yielding an average Peak Signal-to-Noise Ratio (PSNR) of 39.70 dB for the recovered images. Full article

This paper introduces a new analytical model of a multi-service system to which a heterogeneous mixture of stream, elastic, and adaptive traffic is offered, corresponding to data streams transmitted in real-world networks. The heterogeneity results from the coexistence of compressed and non-compressed traffic classes, where the compressed traffic is subject to a finite compression mechanism and includes both elastic and adaptive traffic. The model accounts for access constraints on resources for stream traffic, a strategy frequently used in modern ICT networks to manage the impact of high-rate data streams on overall system performance. The proposed model is developed based on an analysis of the service process within the system using multi-dimensional Markov processes. To assess the accuracy of the analytical model, a digital simulation is conducted. A comparative analysis of the results obtained from the analytical model and the simulation experiments confirms the high accuracy and robustness of the proposed approach, making it suitable for the design and optimization of modern ICT networks, including 5G systems. Full article

The growing demand for real-time video streaming in power-constrained embedded systems, such as drone navigation and remote surveillance, requires encoding solutions that prioritize low latency. In these applications, even small delays in video transmission can impair the operator’s ability to react in time, leading to instability in closed-loop control systems. To mitigate this, encoding must be lightweight and designed so that streaming can start as soon as possible, ideally even while frames are still being processed, thereby ensuring continuous and responsive operation. This paper presents the design of a hardware implementation of the Logarithmic Hop Encoding (LHE) algorithm on a Field-Programmable Gate Array (FPGA). The proposed architecture is deeply pipelined and parallelized to achieve sub-frame latency. It employs adaptive compression by dividing frames into regions of interest and uses a quantized differential system to minimize data transmission. Our design achieves an encoding latency of between 1.87 ms and 2.1 ms with a power consumption of only 2.7 W when implemented on an FPGA clocked at 150 MHz. Compared to a parallel GPU implementation of the same algorithm, this represents a 6.6-fold reduction in latency at approximately half the power consumption. These results show that FPGA-based LHE is a highly effective solution for low-latency, real-time video applications and establish a robust foundation for its deployment in embedded systems. Full article

Real-time media streaming over publish–subscribe platforms is increasingly vital in scenarios that demand the scalability of event-driven architectures while ensuring timely media delivery. This is especially true in multi-modal and resource-constrained environments, such as IoT, Physical Activity Recognition and Measure (PARM), and Internet of Video Things (IoVT), where integrating sensor data with media streams often leads to complex hybrid setups that compromise consistency and maintainability. Publish–subscribe (pub/sub) platforms like Kafka and MQTT offer scalability and decoupled communication but fall short in supporting real-time video streaming due to platform-dependent design, rigid optimization, and poor sub-second media handling. This paper presents FrameMQ, a layered, platform-agnostic architecture designed to overcome these limitations by decoupling application logic from platform-specific configurations and enabling dynamic real-time optimization. FrameMQ exposes tunable parameters such as compression and segmentation, allowing integration with external optimizers. Using Particle Swarm Optimization (PSO) as an exemplary optimizer, FrameMQ reduces total latency from over 2300 ms to below 400ms under stable conditions (over an $80\%$ improvement) and maintains up to a $52\%$ reduction under adverse network conditions. These results demonstrate FrameMQ’s ability to meet the demands of latency-sensitive applications, such as real-time streaming, IoT, and surveillance, while offering portability, extensibility, and platform independence without modifying the core application logic. Full article

Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices within distributed network environments, including computational limitations, memory constraints, and energy-efficiency requirements for real-time intelligent inference. We provide comprehensive analysis of soft computing optimization strategies essential for intelligent edge deployment, systematically examining model compression techniques including pruning, quantization methods, knowledge distillation, and low-rank decomposition approaches. The survey explores intelligent MLOps frameworks tailored for network edge environments, addressing continuous model adaptation, monitoring under data drift, and federated learning for distributed intelligence while preserving privacy in next-generation networks. Our work covers practical applications across intelligent smart agriculture, energy management, healthcare, and industrial monitoring within network infrastructures, highlighting domain-specific challenges and emerging solutions. We analyze specialized hardware architectures, cloud offloading strategies, and distributed learning approaches that enable intelligent edge computing in heterogeneous network environments. The survey identifies critical research gaps in multimodal model deployment, streaming learning under concept drift, and integration of soft computing techniques with intelligent edge orchestration frameworks for network applications. These gaps directly manifest as open challenges in balancing computational efficiency with model robustness due to limited multimodal optimization techniques, developing sustainable intelligent edge AI systems arising from inadequate streaming learning adaptation, and creating adaptive network applications for dynamic environments resulting from insufficient soft computing integration. This comprehensive roadmap synthesizes current intelligent edge machine learning solutions with emerging soft computing approaches, providing researchers and practitioners with insights for developing next-generation intelligent edge computing systems that leverage machine learning capabilities in distributed network infrastructures. Full article

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

This paper proposes a method to secure Compressive Sensing (CS) streams. It involves protecting part of the measurements with a secret key and inserting code into the remaining measurements. The secret key is generated via a cryptographically secure pseudorandom number generator (CSPRNG) and XORed with the measurements to be inserted. For insertion, we use a reversible data hiding (RDH) scheme, which is a prediction error expansion algorithm modified to match the statistics of CS measurements. The reconstruction from the embedded stream results in a visibly distorted image. The image distortion is controlled by the number of embedded levels. In our tests, embedding on 10 levels results in ≈18 dB distortion for images of $256\times 256$ pixels reconstructed with the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). A particularity of the presented method is on-the-fly insertion, which makes it appropriate for the sequential acquisition of measurements with a single-pixel camera. On-the-fly insertion avoids the buffering of CS measurements for the subsequent standard encryption and generation of a thumbnail image. Full article

Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single-organ focus, and limited interpretability. We propose MaxViT-XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with MBConv layers to jointly classify soybean leaf and seed diseases while remaining lightweight and explainable. Two benchmark datasets were upscaled through elastic deformation, Gaussian noise, brightness shifts, rotation, and flipping, enlarging ASDID from 10,722 to 16,000 images (eight classes) and the SD set from 5513 to 10,000 images (five classes). Under identical augmentation and hyperparameters, MaxViT-XSLD delivered 99.82% accuracy on ASDID and 99.46% on SD, surpassing competitive ViT, CNN, and lightweight SOTA variants. High PR-AUC and MCC values, confirmed via 10-fold stratified cross-validation and Wilcoxon tests, demonstrate robust generalization across data splits. Explainable AI (XAI) techniques further enhanced interpretability by highlighting biologically relevant features influencing predictions. Its modular design also enables future model compression for edge deployment in resource-constrained settings. Finally, we deploy the model in SoyScan, a real-time web tool that streams predictions and visual explanations to growers and agronomists. These findings establishes a scalable, interpretable system for precision crop health monitoring and lay the groundwork for edge-oriented, multimodal agricultural diagnostics. Full article