Journal of Sensors

This paper reports the deposition of Zn on glass substrates using the physical vapor deposition (PVD) method, followed by an annealing process to grow ZnO for gas-sensing applications. Surface morphologies were characterized using scanning electron microscopy, which revealed nanowire shape. The diameter of the wire was about 35 nm. In addition, X-ray diffraction analysis demonstrated that the ZnO nanowire possessed a wurtzite structure with an orientation of (002). Three types of resistive gas sensors with a spiral-square and two-comb electrode geometries were designed, fabricated, and tested for their ethanol vapor-sensing properties. The experimental results show that the sensor with square-spiral electrode has the sensitivity of 43% for 2,000 ppm of ethanol vapor at 200°C, while the sensor with a comb electrode shows the sensitivity of 32% at the same conditions. Also, two sensors with different dimensions of comb-shaped electrodes showed the same sensitivity, as both the width and the distance between the fingers change simultaneously in the larger comb-shaped electrode. The response time for the comb electrode is shorter than the square-spiral type, and the recovery time is almost independent of the electrode geometry. Therefore, the optimal structure should be selected based on the application.

In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.

With the rapid development, extensive knowledge, and diverse application scenarios of target perception and positioning technology in sensor networks, a passive target perception and localization platform based on Wireless Sensor Networks (WSN) has been designed. The platform is mainly applied for the teaching of electronic information, communication, and Internet of Things (IoT) engineering. The platform follows the teaching concept of “developing students’ ability to solve complex engineering problems” in the emerging engineering discipline and combines experimental simulation with real-world testing, as well as integrating scientific research and teaching. It encompasses technical elements such as perception and localization theory, sparse representation classification modeling, solving underdetermined equations, data analysis, and sparse coding, as well as nontechnical aspects such as team collaboration and cost budgeting. The platform boasts high fidelity and scalability, providing students with the opportunity to develop comprehensive practical and innovative skills in solving complex engineering problems.

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

Background. The mainly lethal ability of ammunition fragments on creatures is achieved by hitting the effective organs or key parts of the biological body with high-speed projectiles. How to efficiently and accurately obtain the projectile speed and hitting position coordinates when the fragment hits the creature after the ammunition blast is the key to the scientific evaluation of ammunition power. Materials and Methods. For the measurement of fragment velocity and hitting coordinates, a series of flexible film circuit sensors can be generated by printing comb-like circuits on polyethylene terephthalate substrates using silver paste printing technology. These sensors are cheap, flexible, and easy to fold and can be printed into different shapes according to the characteristics of the test target to simulate the biological key organs or lethal parts. At the same time, the software and hardware design of the high-speed data signal reading and processing module can realize the data rapidly recording and processing and quickly give the ammunition fragment parameter test results. Results. The test accuracy of the fragment velocity of the laser light screen target and the flexible circuit sensor is compared through the live-fire test. It is proved that the test accuracy of the flexible sensor based on the polymer substrate can meet the accuracy requirements. The flexible sensor based on the organ simulation can quickly give the accurate hit position of the fragment. Conclusion. The newly polymer substrate printed circuit sensor system is a new type of sensor used to replace the laser screen target, and the copper comb printed circuit in the ammunition power test, which can improve the parameter test accuracy, reduce the test consumption, and improve the test quality.

Identifying objects in surveillance and reconnaissance systems with the human eye can be challenging, underscoring the growing importance of employing deep learning models for the recognition of enemy weapon systems. These systems, leveraging deep neural networks known for their strong performance in image recognition and classification, are currently under extensive research. However, it is crucial to acknowledge that surveillance and reconnaissance systems utilizing deep neural networks are susceptible to vulnerabilities posed by adversarial examples. While prior adversarial example research has mainly utilized publicly available internet data, there has been a significant absence of studies concerning adversarial attacks on data and models specific to real military scenarios. In this paper, we introduce an adversarial example designed for a binary classifier tasked with recognizing helicopters. Our approach generates an adversarial example that is misclassified by the model, despite appearing unproblematic to the human eye. To conduct our experiments, we gathered real attack and transport helicopters and employed TensorFlow as the machine learning library of choice. Our experimental findings demonstrate that the average attack success rate of the proposed method is 81.9%. Additionally, when epsilon is 0.4, the attack success rate is 90.1%. Before epsilon reaches 0.4, the attack success rate increases rapidly, and then we can see that epsilon increases little by little thereafter.