Detection of vehicles on the road can contribute to the establishment of an intelligent transportation management system to allow smooth transportation and the reduction of road accidents. Thus far, an efficient and low-cost polymer flexible pressure sensor for vehicle detection is lacking. This paper presents a flexible sensor for vehicle sensing and demonstrates a wireless system for monitoring vehicles on the road. A vehicle sensor was fabricated by sandwiching a polyurethane material between aluminum top/bottom electrodes. The sensing mechanism was based on changes in capacitance due to variation in the distance between the two electrodes at an applied external pressure. A clear response against a pressure load of 0.65 Mpa was observed, which is the same pressure as that of the car tire area in contact with the road. Significantly, the sensor was easy to embed on the road line due to its mechanical flexibility and large size. A field test was carried out by embedding the sensor on the road and crossing the sensor with a car. Moreover, the signal displayed on the tablet indicated that the sensing system can be used for wireless detection of the axle, speed, or weight of the vehicle on the road. The findings suggest that the flexible pressure sensor is a promising tool for use as a low-cost vehicle detector in future intelligent transportation management.

Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation.

We have demonstrated a method for using proper models of pentacene P-channel and fullerene N-channel thin-film transistors (TFTs) in order to design and simulate organic integrated circuits. Initially, the transistors were fabricated, and we measured their main physical and electrical parameters. Then, these organic TFTs (OTFTs) were modeled with support of an organic process design kit (OPDK) added in Cadence. The key specifications of the modeled elements were extracted from measured data, whereas the fitting ones were elected to replicate experimental curves. The simulating process proves that frequency responses of the TFTs cover all biosignal frequency ranges; hence, it is reasonable to deploy the elements to design integrated circuits used in biomedical applications. Complying with complementary rules, the organic circuits work properly, including logic gates, flip-flops, comparators, and analog-to-digital converters (ADCs) as well. The proposed successive-approximation-register (SAR) ADC consumes a power of 883.7 µW and achieves an ENOB of 5.05 bits, a SNR of 32.17 dB at a supply voltage of 10 V, and a sampling frequency of about 2 KHz.

This paper presents the development of a system for simulating and visualizing autonomous vehicle navigation using a single-beam LiDAR sensor integrated with an artificial neural network, implemented entirely in JavaScript. The single-beam LiDAR sensor in the simulation was configured to closely resemble real-world counterparts, such as the YDLIDAR TG30. The proposed neural network architecture, consisting of four output neurons, processes data from ten distinct angular measurements of the LiDAR sensor to predict necessary steering and speed adjustments for autonomous navigation. Furthermore, we introduce an enhanced collision detection algorithm that improves upon traditional methods by providing more precise identification of line segment intersections. Implementing the entire system in JavaScript demonstrates the viability of utilizing web technologies for complex machine learning tasks, representing a significant advancement in autonomous vehicle research. Experimental results validate the effectiveness of this approach across various scenarios, emphasizing its potential for broader applications in cost-sensitive autonomous systems. This work may also serve as a valuable resource for researchers new to machine learning, contributing to greater accessibility in the development of autonomous vehicles.

In this paper, a technique to develop a secure messaging service utilizing a new post-quantum cryptosystem, termed CryptoMess, is proposed. Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) is utilized to secure key exchange paired with the AES algorithm to protect message content in communication. At the same time, the Rainbow post-quantum digital signature technology is incorporated to assure the integrity and authenticity of communications between the sender and the recipient. As a consequence, the messaging program is able to exchange messages between users, assuring safety, security, integrity, and authenticity. The performance of the program has a transmitting rate of approximately 0.26 s and a receiving rate of approximately 0.22 s. The message signing time is approximately 0.027 s, the message verification speed is approximately 0.22 s, and the key exchange time is approximately 0.0017s.