A Stretchable Strain Sensor Based on CNTs/GR for Human Motion Monitoring

Flexible/stretchable strain sensors have attracted much attention due to their advantages for human-computer interaction, smart wearable and human monitoring. However, there are still great challenges on gaining super durability, quick response, and wide sensing range. This paper provides a simple process to obtain a sensor which is based on graphene (GR)/carbon nanotubes (CNTs) and Ecoflex hybrid, which demonstrates superb endurance (over 1000 cycles at 100% strain), remarkable sensitivity (strain over 125% sensitivity up to 20) and wide sensing range (175%). All results indicate that it is capable for human movement monitoring, such as finger and knee bending and pulse beat. Most importantly, it can be used as a warning function for the night cyclist’s ride. This research provides the feasibility of using this sensor for practical applications.

Artificial Intelligence Technology in Electric Automation Monitoring System of Power Plant

With the development of large-capacity and high-parameter units, the degree of electrical automation in power plants is becoming more and more important. Due to the relatively backward technology of traditional electrical monitoring, it is more human monitoring, and the level of automation is low. Nowadays, communication network technology has been rapidly developed, and machinery and equipment have become more intelligent. These have laid a good technical foundation for the reform and innovation of electrical automation monitoring systems in power plants. This article first studies the development status of the electrical automation monitoring system, and then uses the fieldbus and Ethernet technology to carry out the configuration mode and construction of the electrical automation system. Then, this article designs the basic functions, advanced application functions and software parts of the system. Finally, this paper tests the running time and update function of the system dynamic module. The test result shows that the running time of the dynamic module meets the requirements of the system, and the update function is relatively fast.

Preparation of Bamboo-Like Carbon Nanotube Loaded Piezoresistive Polyurethane-Silicone Rubber Composite

In this study, a novel technology is reported to prepare a piezoresistive polyurethane-silicone rubber nanocomposite. Polyurethane (PU) foam was loaded with a nitrogen-doped bamboo-shaped carbon nanotube (N-BCNT) by using dip-coating, and then, impregnated with silicone rubber. PU was used as a supporting substrate for N-BCNT, while silicone rubber was applied to fill the pores of the foam to improve recoverability, compressive strength, and durability. The composite displays good electrical conductivity, short response time, and excellent repeatability. The resistance was reduced when the amount of N-BCNT (0.43 wt %) was increased due to the expanded conductive path for electron transport. The piezoresistive composite has been successfully tested in many applications, such as human monitoring and finger touch detection.

Tomato Leaf Diseases Detection Using Deep Learning Technique

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

Implementing a Real Time Human Detection and Monitoring Social Distancing for Covid-19 Using V-J algorithm and OpenCV

Recently, the outbreak of Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing has becomeone of mandatory preventive measures to avoid physical contact. COVID-19 is a disease caused by a severe respiratory syndrome coronavirus. The continuous development of technology of IT enabled computers to see and learn. There are many viable applications for computer learning and vision to solve new tasks. In this paper, we propose a framework, able of automatically detect no. of human bodies present in a single image, acquired by a traditional low-cost camera. In this paper Viola jones algorithm is usedto detect human monitoring social distancing norm. System is divided into two parts, the first part is about person detection whereas second part is about monitoring whether people are following social distancing or not, it is applicable if image containsmore than one human. This paper is going to study and understand the Viola-Jones algorithm by implementing the whole detection framework and based on the implementation, conduct experiment to hopefully further improve the performance.

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.

Model Assessment on Cooling Effect of Mangrove and Proboscis Monkey Conservation Area in Tarakan City

<p><em>A conservation area for mangrove forests and proboscis monkeys locates within a dense urban centre in Tarakan city. Its surrounding area has rapidly transformed into condensing urban artificial structures with minimal vegetation cover. The study aims to determine the cooling effect of the conservation area toward its surrounding. The study applies model assessment, which comprises ENVI-MET for simulation of air temperature distribution, and Rayman for PMV (Predicted Mean Vote) calculation to investigate the level of heat stress. The result shows that the mangrove cover has a cooling effect effectively after midnight to 11:00 the subsequent day. Specifically, for the surrounding area, it is confined within an 8-hour window (from 0:00 to 8:00). Within this window, inside the mangrove area, ENVI-MET generates significantly lower air temperature, when compared to its surrounding. The mangrove presence correlates to reducing air temperature up to 1.8</em><em>°</em><em> C. The cooling effect also extends to its surrounding with a limited radius (approximately 10 m). However, PMV calculation implies that there is no significant improvement in thermal comfort as a result of the mangrove presence. The results may not adequately reflect the reality in the field. Further research to calibrate PMV measurement that involves human monitoring is required to validate the findings.</em></p>

TSINC: A Plagiarism-resistant Online Testing Method

TSINC is a new online testing method. The acronym TSINC represents its five characteristics: Time-pressed, Sequential, Individualized, Not searchable, and Calibration-bound. Its primary feature is that it can be used to organize plagiarism-resistant online testing without human monitoring. In this paper, the logical basis of this method, its implementation details, applicable areas and limitations, test cases, and so on are reported.

Skema Handover pada Multi-kamera dengan Logika Fuzzy untuk Sistem Pemantauan Orang

ABSTRAKAdanya berbagai peristiwa yang membahayakan di tempat keramaian menyebabkan diperlukannya sebuah sistem pemantauan aktifitas manusia di sekitarnya untuk pengawasan keamanan. Sistem multi-kamera sangat cocok digunakan untuk pemantauan target pada lingkungan area yang luas. Disaat target meninggalkan jangkauan area kamera menuju lainnya, proses pemantauan target harus tetap bekerja dan diserahkan ke kamera lainnya. Protokol serah terima target dapat berjalan jika terdapat komunikasi antar kamera yang tersedia. Penelitian ini menyajikan skema handover pada sistem multi-kamera dengan menerapkan pengambilan keputusan handover berbasis logika fuzzy. Dengan begitu, target akan selalu ditangani oleh kamera meskipun target bergerak menjauhinya. Berdasarkan hasil simulasi, skema handover ini mampu mereduksi total number of handover sebesar 20% dibandingkan dengan metode AHCS (Active Handover Control Scheme). Selain itu, handover delay pada metode usulan memperoleh waktu 123.72μs dan masih lebih lama dari AHCS.Kata kunci: handover, multi-kamera, pemantauan orang, fuzzy logic ABSTRACTThe existence of various dangerous events in a crowded place causes the need of surveillance system to monitor the human activity continuously in a certain area. Multi-camera systems are used to monitor targets in large areas. When the target leaves the camera’s range for another, the target monitoring process should continue to work and be left to other cameras. The target handover protocol may work if there is communication between the available cameras. This document presents a handover scheme in a multi-camera system by applying a fuzzy logic handover decision. Thus, the target will always be processed by the camera, even if the target is moving away from it. Based on the simulation results, this handover scheme is able to reduce the total number of handovers by 20% compared to the AHCS (Active Handover Control Scheme) method. In addition, the handover delay in the proposed method obtains 123.72 μs and is still longer than the AHCS.Keywords: handover, multi-camera, human monitoring, fuzzy logic