Sound classification is a multifaceted task that necessitates the gathering and processing of vast quantities of data, as well as the construction of machine learning models that can accurately distinguish between various sounds. In our project, we implemented a novel methodology for classifying both musical instruments and environmental sounds, utilizing convolutional and recurrent neural networks. We used the Mel Frequency Cepstral Coefficient (MFCC) method to extract features from audio, which emulates the human auditory system and produces highly distinct features. Knowing how important data processing is, we implemented distinctive approaches, including a range of data augmentation and cleaning techniques, to achieve an optimized solution. The outcomes were noteworthy, as both the convolutional and recurrent neural network models achieved a commendable level of accuracy. As machine learning and deep learning continue to revolutionize image classification, it is high time to explore the development of adaptable models for audio classification. Despite the challenges associated with a small dataset, we successfully crafted our models using convolutional and recurrent neural networks. Overall, our strategy for sound classification bears significant implications for diverse domains, encompassing speech recognition, music production, and healthcare. We hold the belief that with further research and progress, our work can pave the way for breakthroughs in audio data classification and analysis.

Collisions with fallen pedestrians pose a lethal challenge to current advanced driver-assistance systems. This paper introduces and quantitatively validates the Advanced Falling Object Detection System (AFODS), a novel safety framework designed to mitigate this risk. AFODS architecturally integrates long-wave infrared, near-infrared stereo and ultrasonic sensors, processed through a novel artificial intelligence pipeline that combines YOLOv7-Tiny for object detection with a recurrent neural network for proactive threat assessment, thereby enabling the system to predict falls before they are complete. In a rigorous controlled study using simulated adverse conditions, AFODS achieved a 98.2% detection rate at night, a condition where standard systems fail. This paper details the system’s ISO 26262-aligned architecture and validation results, proposing a framework for a new benchmark in active vehicle safety, demonstrated under controlled test conditions.

Pedestrian–vehicle collisions produce a rich kinematic record that is entirely lost by the time a forensic investigation begins. Recovering this record constitutes a state-estimation problem. This paper presents a Phase 1 design for a multimodal sensor fusion and signal-processing framework utilising 128-channel LiDAR, 1080p NIR stereo cameras, and a 2 kHz IMU, all fused via Kalman filtering and Savitzky–Golay polynomial differentiation. The framework is evaluated through Monte Carlo uncertainty propagation and sensitivity analysis applied to a constructed simulation scenario; no real clinical or forensic data are used in this Phase 1 report. Under simulated conditions with throw-distance measurement uncertainty of ±0.5 m, velocity reconstruction shows an estimated propagated uncertainty of ±2.03 km/h under expanded simulation conditions with vehicle-coefficient variance activated. Sensitivity analysis indicates that a 10% noise spike in acceleration would theoretically amplify injury metrics by 26.9%, providing quantitative justification for noise-optimal pre-filtering. The bimodal kinematic–acoustic architecture is proposed as a physically interpretable foundation for collision reconstruction; its experimental performance awaits Phase 2–4 validation. A five-phase validation roadmap is presented, progressing from FEA simulation to independent multi-site replication before any forensic deployment is proposed.

Battery state-of-health (SOH) benchmarks often mix cycles from the same cell across training and test sets. We examined whether predictor structure changes the gap between within-cell interpolation and cross-cell transfer. Seven regressors and five predictor sets were evaluated in four NASA cells (636 cycles) using ten random 80:20 splits and leave-one-cell-out (LOCO) testing repeated over ten model seeds. Across-model median random split RMSE fell from 5.00 SOH points with age alone to 1.52 with diagnostics plus age, whereas median LOCO RMSE fell only from 5.83 to 4.61; the absolute protocol gap therefore increased from 0.83 to 3.09 points, and the median cell-paired LOCO/random ratio increased from 1.24 to 4.53. With all diagnostics, model-level ratios ranged from 1.65 to 5.16, and random-versus-LOCO ranks were uncorrelated (descriptive Spearman ρ = 0.00). Holding the number of training cycles constant, increasing the number of source cells from one to three reduced median LOCO RMSE from 5.25 to 4.73 points, but 32.1% of cell–model pairs did not improve. Standardised sliced Wasserstein shift was associated with the validation ratio (descriptive ρ = 0.73) but not with absolute LOCO RMSE (ρ = −0.01). Rated capacity normalisation preserved the predictor-dependent gradient. An eight-cell Oxford analysis was limited to an age-only boundary check and was not a feature-matched replication. These four-cell laboratory results show that richer discharge diagnostics can improve represented cell interpolation much more than unseen cell transfer; claims should therefore report absolute and relative errors with complete cell-held-out validation.

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios.

Modern automotive safety systems rely heavily on upright-pedestrian detection, leaving individuals in compromised postures—due to medical emergencies, intoxication, or prior impacts—largely "invisible" to standard sensors. This mini review argues for an integrated safety paradigm that synthesizes ISO 26262 functional safety protocols with multi-modal artificial intelligence. By deploying the Advanced Falling Object Detection System (AFODS), which combines YOLOv7 Tiny spatial recognition, thermal imaging, and acoustic signal processing, we demonstrate a 98.2% detection rate for prone individuals—a significant improvement over the 21.4% true positive rate in current low-light scenarios. This technological shift is supported by a deontological ethical framework that mandates the protection of the most vulnerable participants in the transit ecosystem. The system architecture has been filed under Japanese patent application 2025-167440.