This study introduces a new method to enhance ADAS's safety and error prevention capabilities in intelligent vehicles. We address the significant computational and memory demands required for real-time video processing by leveraging BDD100 K, KITTI, CityScape, and Waymo datasets. Our proposed hardware-software co-design integrates an MPSoC-FPGA accelerator for real-time multi-learning models. Our experimental results exhibit that, despite an increase in ADAS tasks and model parameters compared to the state-of-the-art studies, our model achieves 24,715 GOP performance with 4% lower power consumption (6.920 W) and 18.86% less logic resource consumption. The model processes highway scenes at 22.45 FPS and attains 50.06% mAP for object detection, 57.05% mIoU for segmentation, 43.76% mIoU for lane detection, 81.63% IoU for drivable area segmentation, and 9.78% SILog error for depth estimation. These findings confirm the system's effectiveness, reliability, and adaptability for ADAS applications and represent a significant advancement in intelligent vehicle technology, with the potential for further improvements in accuracy and memory efficiency.

Random number generators are cryptographic components that generate unpredictable, independent and identically distributed bits. We studied 1D chaotic logistic map in the context of random number generation. Nonlinear dynamics of the logistic map, its statistical and spectral features are studied using numerical simulations. Logistic map based random number generator is implemented on a discrete time operating field programmable analog array chip. We evaluated the random number generation performance of our design using NIST 800.22 statistical tests.

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research.

Flow sensing is essential in biomedical engineering, industrial process control, and environmental monitoring. Conventional sensors, while accurate, are often constrained by high fabrication costs, complex processes, and limited design flexibility, restricting their use in disposable or rapidly customizable applications. This paper presents a novel ultra-low-cost airflow sensor fabricated entirely through fused deposition modeling 3D printing. The device employs a cantilever-based structure printed with PETg filament, followed by the deposition of a conductive ABS piezoresistive layer in a two-step process requiring no curing or post-processing. Experimental characterization reveals that the sensor operates in an ultra-low pressure range of 0.88–26.68 Pa, corresponding to flow velocities of 1.2–6.6 m/s. The sensor achieves a sensitivity of 967 Ω/Pa, a resolution of 9.27 Pa, and a detection limit of 83.27 Pa, with a total resistance change of approximately 51.5 kΩ. This kilo-ohm-scale response enables direct readout via a digital multimeter without requiring Wheatstone bridges or instrumentation amplifiers. The minimalist design, combined with sub-5 min fabrication time and material cost below $0.05, positions this sensor as an accessible platform for disposable, scalable, and resource-constrained flow monitoring applications in both biomedical and industrial contexts.