Search Results (66)

Acoustic gait recognition is an emerging passive biometric modality that identifies individuals by unique walking sound patterns. This work presents a reproducible benchmarking framework for convolutional neural network (CNN)-based acoustic gait recognition, providing a systematic evaluation methodology across varying identity pool sizes. Raw footstep recordings from the AFPILD dataset were converted into 128-bin mel-spectrograms and used to train a compact CNN across identity pool sizes from 10 to 40 subjects. To ensure statistical reliability, a three-times-repeated five-fold stratified cross-validation protocol was implemented. Experimental results demonstrate strong discriminative capability, with validation accuracy reaching 94.92% and Equal Error Rate (EER) of 1.31% for the 40-subject configuration. A multi-seed subset validation experiment across five independent random subject draws per pool size confirmed that the observed scaling trend is consistent across subset compositions rather than an artifact of a single subject selection. Additional analysis confirmed the framework’s resilience to moderate environmental noise and its superiority over classical Mel-Frequency Cepstral Coefficients paired with a Support Vector Machine (MFCC-SVM) and Convolutional Recurrent Neural Network (CRNN) baselines, supporting the feasibility of acoustic gait recognition as a passive biometric modality. Full article

This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management. Full article

Condiment sauces such as soy sauce, fish sauce, oyster sauce, and Worcestershire sauce play a vital role in culinary practices and cultural identity, particularly in the Philippines. These sauces are distinguished by their unique volatile organic compound profiles, which define their aroma and flavor. With the growing demand for these condiment products, there is an increasing need for accurate and efficient methods to classify them, ensuring product authenticity and strengthening quality control. However, conventional approaches such as sensory evaluation and laboratory-based chemical analysis are often expensive, time-consuming, and subjective. To address this limitation, we used an electronic nose (e-nose) system integrated with a Support Vector Machine (SVM) classifier for the classification of dark condiment sauces. The system consists of an array of MQ-series gas sensors connected to an Arduino Mega 2560 for analog-to-digital conversion, with Raspberry Pi 5 serving as the primary processing unit. Sensor data undergo preprocessing steps, including standardization and dimensionality reduction through principal component analysis, before being classified using SVM. A total of 120 samples, consisting of 40 readings per condiment type, were used for training and testing, while 60 additional samples—15 per class—were reserved for validation. The e-nose system achieved a 95% classification performance, as evaluated using a confusion matrix and overall accuracy metrics. These results demonstrate the potential of the e-nose combined with SVM as a reliable tool for condiment classification. The system offers practical applications in quality control and product authentication. Future work may extend its capabilities toward spoilage detection, the integration of different gas sensors, and the classification of a wider variety of condiment sauces. Full article

Background: The co-existence of hemophilia and cancer presents one of the most complex clinical scenarios, demanding individualised therapeutic planning to balance oncologic efficacy and hemostatic safety. This study evaluated the ability of two Large Language Models (LLMs)—ChatGPT (GPT-4) and Microsoft Copilot (GPT-4–based)—to generate clinically appropriate recommendations for real cases of hemophilia with concurrent malignancy. Methods: Six consecutive adult cases of hemophilia and cancer, managed at the Hemophilia Centre of Padua, Italy, were selected for evaluation. Identical structured prompts were submitted to both LLMs. Two independent expert clinicians rated the model outputs across five domains (Decision/Rationale, Strategy, Selected Drug, Regimen, and Assessment) using a four-level ordinal scale. Results: LLMs demonstrated uneven performances. Outputs were consistently rated as highly reliable in domains involving high-level synthesis, such as Assessment and Strategy. However, substantial limitations were observed in the clinically demanding domains of Selected Drug and Regimen. Critically, in the Selected Drug domain, there was complete agreement between the two expert raters for neither system. This severe lack of concordance signifies that clinicians assigned different adequacy ratings to the same output in every case, reflecting ambiguity, lack of specificity, and inconsistent clinical interpretability of the drug-related information provided by LLMs. Conclusions: While LLMs possess the capacity for high-level reasoning and strategic planning, their inability to translate principles into precise, consistent, and clinically interpretable therapeutic plans—particularly regarding drug selection and treatment regimens—is a significant constraint. These deficiencies, highlighted by the minimal expert concordance in critical domains, necessitate rigorous clinical validation before the responsible integration of LLMs into the management of this uniquely vulnerable patient population. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

Understanding how high-level meanings emerge from low-level microstate dynamics is a central challenge in both artificial intelligence and consciousness studies. Complex networks can exhibit rich behaviors, yet reliably mapping every microstate onto a semantic label to date seems intractable. To explore these limits, a minimal 4-bit model consisting of only a ring of binary cells updated by a parity-flip rule, coupled with a finite lookup table that assigns conceptual tags to selected microstates, is presented. Two core failure modes are noted. First, noise is found to push the system into out-of-training-set states that a semantic decoder cannot label (“missing-label” errors). Second, distinct microstates collapse into the same semantic tag (“many-to-one” grouping), obscuring their unique identities. These findings demonstrate inherent opacity in semantic mapping and suggest fundamental barriers to reverse-engineering high-level content in artificial or biological networks. Future work includes scaling N and examining partial-observability effects. Full article

This article presents a novel framework for encrypting color images to enhance digital data security using deep learning and artificial intelligence techniques. The system employs a two-model neural architecture: the first, a Convolutional Neural Network (CNN), verifies sender authenticity during user authentication, while the second extracts unique fingerprint features. These features are converted into high-entropy encryption keys using Particle Swarm Optimization (PSO), minimizing key similarity and ensuring that no key is reused or transmitted. Keys are generated in real time simultaneously at both the sender and receiver ends, preventing interception or leakage and providing maximum confidentiality. Encrypted images are secured using the Advanced Encryption Standard (AES-256) with keys uniquely bound to each user’s biometric identity, ensuring personalized privacy. Evaluation using security and encryption metrics yielded strong results: entropy of 7.9991, correlation coefficient below 0.00001, NPCR of 99.66%, UACI of 33.9069%, and key space of 2²⁵⁶. Although the final encryption employs an AES-256 key (key space of 2²⁵⁶), this key is derived from a much larger deep-key space of 2⁸¹⁹² generated by multi-layer neural feature extraction and optimized via PSO, thereby significantly enhancing the overall cryptographic strength. The system also demonstrated robustness against common attacks, including noise and cropping, while maintaining recoverable original content. Furthermore, the neural models achieved classification accuracy exceeding 99.83% with an error rate below 0.05%, confirming the framework’s reliability and practical applicability. This approach provides a secure, dynamic, and efficient image encryption paradigm, combining biometric authentication and AI-based feature extraction for advanced cybersecurity applications. Full article

Sweet cherry (Prunus avium L.) exhibits relatively low genetic diversity because of the self-compatibility of some varieties and repeated crossings of the same genotypes. High-quality markers are therefore needed for their reliable discrimination. However, the most currently used simple sequence repeat (SSR) markers offer only limited resolution for genotyping purposes. Here, thirty new highly polymorphic SSR markers were extracted from whole-genome sequences of 299 sweet cherry genotypes. Then, 16 highly polymorphic SSR markers were selected, multiplexed into one PCR, and successfully verified on a collection containing 294 unique genotypes. Compared with the set of 16 SSR markers recommended by the European Cooperative Programme for Plant Genetic Resources (ECPGR) for sweet cherry genotyping, our newly developed system has a seven orders of magnitude lower probability of the random identity of two genetically distinct samples than the ECPGR set (10⁻¹⁹ vs. 10⁻¹²). This higher resolution not only enables more precise genotyping but can also be successfully used for parentage or population analyses. This new and unique one-tube approach for sweet cherry genotyping will substantially simplify genotyping workflows, minimize errors, and save labor, time, and cost. Full article

With the rise of smart cities, technology has enabled more efficient urban management. A key part of this is the Internet of Vehicles (IoVs), which connects vehicles to smart city systems to improve transportation safety and efficiency. This integrated system enables wireless connection between vehicles, allowing for the sharing of essential traffic information. However, with all this connectivity, there are growing concerns about IoV security and privacy. This paper presents a new privacy-preserving authentication scheme for Autonomous Vehicles (AVs) in the IoV field using physical unclonable functions (PUFs). This scheme employs a bilinear pairing-based encryption technique that supports search over encrypted data. The primary aim of this scheme is to authenticate AVs inside the IoV architecture. A novel PUF design generates random keys for our authentication technique, hence boosting security. This dual-layer security strategy safeguards against a range of cyber threats, including identity fraud, man-in-the-middle attacks, and unauthorized access to personal user data. The PUF design will guarantee the true randomness of the AVs’ users’ secret keys. To handle the large amount of data involved, we use hardware acceleration with different Field-Programmable Gate Arrays (FPGAs). Our examination of privacy and security demonstrates the achievement of the defined design goals. The proposed authentication framework was fully implemented and validated on FPGA platforms to demonstrate its hardware feasibility and efficiency. The integrated heterogeneous PUF achieves an average reliability exceeding $98.5\%$ across a wide temperature range, while maintaining near-ideal randomness with an average Hamming weight of $49.7\%$ over multiple challenge sets. Furthermore, the uniqueness metric approaches $49.9\%$, confirming strong inter-device distinguishability among different PUF instances. The complete authentication architecture was synthesized on Nexys-100T, Zynq-104, and Kintex-116 devices, where the design utilizes less than $80\%$ of slice Look-Up Tables (LUTs), under $27\%$ of on-chip memory resources, and below $16\%$ of DSP blocks, demonstrating low hardware overhead. Full article

Internet of Things deployments face escalating security threats, yet systematic methods for auditing the defensive posture of IoT device networks remain underdeveloped. Current intrusion detection evaluations focus on algorithmic accuracy while neglecting operational requirements—computational efficiency, reproducibility, and interpretable risk assessment—that security audits demand. This paper introduces a reproducible security audit framework for IoT device networks, demonstrated through systematic evaluation of four machine learning models (Random Forest, LightGBM, XGBoost, Logistic Regression) on the TON_IoT dataset containing nine attack categories targeting smart environments. Our audit methodology enforces strict feature hygiene by excluding identity-revealing attributes, benchmarks both threat detection capability and computational cost, and provides complete reproducibility artifacts including preprocessing pipelines and trained models. The framework evaluates security posture through dual lenses: binary classification (distinguishing compromised from legitimate traffic) and multiclass classification (attributing threats to specific attack types). Binary audit results show ensemble models achieve 99.8–99.9% accuracy with perfect ROC-AUC (100%) and sub-15 ms inference latency per 1000 flows, confirming reliable attack detection. Multiclass auditing reveals more nuanced findings: while overall accuracy reaches 99.0% with macro-F1 near 97%, rare attack types expose critical blind spots—man-in-the-middle threats achieve only 78% F1 despite representing serious security risks. LightGBM provides optimal audit performance, balancing 99.93% detection accuracy with 2.76 MB deployment footprint. We translate audit findings into actionable security recommendations (network segmentation, rate-limiting, TLS metadata collection) and compare against twenty published studies, demonstrating that our framework achieves competitive detection rates while uniquely delivering the transparency, efficiency metrics, and reproducibility required for credible security assessment of production IoT networks. Full article

Core germplasm, a strategically selected subset of the original germplasm, aims to maximize the representation of genetic diversity within the entire collection. Establishing a germplasm resource bank is essential for the effective management and sustainable utilization of genetic resources. This study developed a core germplasm repository for Hypsizygus marmoreus, a commercially important mushroom species, to capture the genetic diversity of the original collection with a minimal sample size. Genetic diversity and cluster analyses were conducted on 57 representative strains of H. marmoreus, including both cultivated and wild accessions from different regions, using 15 pairs of simple sequence repeat (SSR) markers. DNA molecular identity cards were generated for all germplasms, and cultivation trials with agronomic trait assessments were performed on 24 core accessions. A total of 115 distinct alleles were identified, with genetic similarity coefficients ranging from 0.70 to 1.00. Clustering at a similarity threshold of 0.76 classified the strains into five groups. The core germplasm panel, comprising 24 accessions (42.11% of the total collection), retained full allelic diversity and preserved the genetic and phenotypic variability of the original population, confirming its suitability for parental selection in breeding programs. unique molecular identity codes were developed for each H. marmoreus germplasm by integrating SSR marker profiles with data on geographical origin, fruiting body color, and cultivation traits. These were converted into DNA molecular ID codes, providing a reliable system for rapid identification and traceability of germplasm resources. The findings offer a valuable reference for breeding improvement and the protection of edible fungal varieties with independent intellectual property rights. Full article

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

The proliferation of biometric technology across various domains including user identification, financial services, healthcare, security, law enforcement, and border control introduces convenience in user identity verification while necessitating robust protection mechanisms for sensitive biometric data. While chaos-based encryption systems offer promising solutions, many existing chaos-based encryption schemes exhibit inherent shortcomings including deterministic randomness and constrained key spaces, often failing to balance security robustness with computational efficiency. To address this, we propose a novel dual-layer cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system for protecting biometric templates and facilitating secure feature matching operations. The framework implements a two-tier encryption mechanism where each layer independently utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring template-specific encryption patterns that enhance resistance against chosen-plaintext attacks. The framework performs dimensional normalization of input biometric templates, followed by image pixel shuffling to permutate pixel positions before applying dual-key encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates remain encrypted in storage, with decryption occurring only during authentication processes, ensuring continuous security while enabling biometric verification. The proposed system’s framework demonstrates exceptional randomness properties, validated through comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of ${10}^{320}$, negligible correlation coefficients (<${10}^{-2}$), and robust differential attack resistance with an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA Face and Iris databases, demonstrates practical computational efficiency, achieving average encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis against other state-of-the-art encryption schemes verifies the effectiveness and reliability of the proposed scheme and demonstrates our framework’s superior performance in both security metrics and computational efficiency. Our findings contribute to the advancement of biometric template protection methodologies, offering a balanced performance between security robustness and operational efficiency required in real-world deployment scenarios. Full article

Magnetocardiography (MCG) provides a non-invasive, contactless technique for evaluating the magnetic fields generated by cardiac electrical activity, offering unique spatial insights into cardiac electrophysiology. However, conventional MCG systems depend on superconducting quantum interference devices that require cryogenic cooling and magnetic shielded environments, posing considerable impediments to widespread clinical adoption. In this study, we present a novel MCG system utilizing a high-sensitivity, wide-dynamic-range magnetoresistive sensor array operating at room temperature. To mitigate environmental interference, identical sensors were deployed as reference channels, enabling adaptive noise cancellation (ANC) without the need for traditional magnetic shielding. MCG recordings were obtained from 40 healthy participants, with signals processed using ANC, R-peak-synchronized averaging, and Bayesian spatial signal separation. This approach enabled the reliable detection of key cardiac components, including P, QRS, and T waves, from the unshielded MCG recordings. Our findings underscore the feasibility of a cost-effective, portable MCG system suitable for clinical settings, presenting new opportunities for noninvasive cardiac diagnostics and monitoring. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article