A data glove enables fine motion control in virtual reality (VR). This work presents a cost-effective data glove made of a commercial nylon-based glove with integrated polyethylene-carbon composites (Velostat). The resistance of the Velostat varies when a finger is bent. This piezoresistive behavior was explored by designing the Velostat as the strain sensor. The strain sensor was characterized for its flexure sensing by connecting it to a data acquisition circuit. The circuit is designed to process the output of the joint angles and feed it to the computer. A linear resistance output was measured with a sensitivity/gauge factor of 1.45 % per degree with a response time of 0.0158 seconds when the strain sensor bends from 0° to 30°. To control the 3D virtual hand's movement, the data glove was coupled with two inertial measurement unit sensors at the forearm and upper arm to identify its coordinates. The fabricated data glove successfully performs a proof-of-concept by picking-and-placing multiple objects in a VR environment.

This paper presents a 35.0 × 35.0 × 2.7 mm3 compact, low-profile, and lightweight wearable antenna for on-body wireless power transfer. The proposed antenna can be easily printed on a piece of flexible tattoo paper and transformed onto a PDMS substrate, making the entire antenna structure conform to the human body for achieving a better user experience. Here, a layer of frequency selective surface (FSS) is inserted in between the antenna and human tissue, which has successfully reduced the loading effects of the tissue, with 13.8 dB improvement on the antenna gain. Also, the operating frequency of the rectenna is not affected much by deformation. To maximize the RF-DC conversion efficiency, a matching loop, a matching stub, and two coupled lines are integrated with the antenna for tuning the rectenna so that a wide bandwidth (~ 24%) can be achieved without the use of any external matching networks. Measurement results show that the proposed rectenna can achieve a maximum conversion efficiency of 59.0% with an input power of 5.75 μW/cm2 and can even exceed 40% for a low input power of 1.0 μW/cm2 with a 20 kΩ resistive load, while many other reported rectennas can only achieve a high PCE at a high power density level, which is not always practical for a wearable antenna.

During operation, machines or objects generate vibration and there are unwanted vibrations that will disrupt the overall system, which results in faults such as imbalance, crack, wear, and misalignment. Thus, collecting and analyzing the vibration data has become an effective method in monitoring the condition of an object. There are many instruments to acquire vibration data and techniques to analyse the collected data. In this study, two types of vibration sensors are applied to collect and visualizes the vibration data in real-time, which are SW420 vibration sensors and ADXL345 accelerometers. The sensors are mounted on the rod, which is attached to the motor to determine the effect of the mounting position on the vibration. Then, a threshold value is set to see the reliability of these vibration sensors in detecting faults. Based on the results, mounting the vibration sensor closer to the source of vibration will generate higher vibration and both sensors can successfully detect a fault, set by the threshold values.

The introduction of LoRa changes the way Internet of Things (IoT) technologies communicate by offering up to a 15 km range of wireless data transfer. However, LoRa does not reach its maturity yet, and its reliability in transmitting data under different circumstances remains unclear. Thus, this paper aims to evaluate the performance of the Cytron LoRa RFM shield when deployed with an unmanned aerial vehicle (UAV) in terms of communication range and received signal strength indicator (RSSI). Different experimental conditions were used to evaluate the reliability of UAV-based LoRa, and the findings show that the data transmission is successful at the 1 km range under the direct line-of-sight condition. The RSSI recorded were in the range of −91 to −103 dBm. However, the communication was unsuccessful at a distance of 400 m and beyond when the LoRa pathway was blocked by obstacles.

Pilots, mostly the beginner ones, often overlook the maintenance of the unmanned aerial vehicle (UAV). A lack of proper maintenance or condition monitoring before flying the UAV might lead to a crash due to failures in certain UAV components. Besides, the drone inspection based on the naked eyes is unreliable as some faults might be missed. In this study, the real-time data collection technique for propeller’s inspection is introduced. An ADXL345 accelerometer was used to determine the acceleration experienced by the UAV and classify between a healthy and faulty propeller in real-time. Currently, this approach does not require further signal processing techniques as the faulty propeller can be detected from raw acceleration data. The results show that the ADXL345 accelerometer can classify between four types of faulty propellers simulated in this study and is preferable to be implemented compared to using the infrared sensor. Based on the experimental results, an improvement of 15.64% was determined in terms of the minimum difference between healthy and faulty data.

The topic Internet of Things (IoT) has been growing rapidly, parallel with the advancement of Industry 4.0. In the wireless communication network, which is a subbranch of IoT, LoRa devices provide compelling features for IoT applications such as greater range compared to cellular networks, secure data transmission, and low power consumption. Using the two vital indicators, namely received signal strength indicator (RSSI) and packet reception rate (PRR), this study evaluated the reliability of the LoRa communication network in a multistorey building. Two LoRa devices are deployed as a receiver and transmitter, and the LoRa performances are evaluated on each floor. A packet reception rate of 60% was observed when the LoRa transmitter was placed at placement O and N. Findings also show that the dynamic environment has more effect on the RSSI compared to PRR.

Drones have been widely applied in the precision agriculture sector in the past few years. Incorporating artificial intelligence (AI), sensors, microcontrollers, and the Internet of Things (IoT) into the drones can help overcome the challenges faced by the farmers, such as livestock monitoring, wide land area, crop spraying, and in-depth crop health analysis. In this paper, several drone applications in precision agriculture are discussed, including the hardware and techniques involved. In addition, commercial agricultural drones available in the market to date are presented. The publications trend regarding drone application in precision agriculture is also included and based on reviewing more than 50 articles, a quadcopter-type drone is the most used drone in this sector, and seed plantin

Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases in which, due to accident, the arm of the multirotor crack or loosen. This is normally unnoticeable without disassembly, and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash. Different types of AI methods are incorporated in this study, namely, fuzzy logic, neuro-fuzzy, and neural network (NN). Their results are compared to determine the best method in predicting the safety of the multirotor. Fuzzy logic and neuro-fuzzy methods provided acceptable decision-making, but the performance of the neuro-fuzzy approach depend on the dataset used because overfit model might give incorrect decision-making. This also applies to the NN technique. Because the vibration data are collected in the laboratory environment without consideration of wind effect, this framework is more suitable for early prediction before flying the multirotor in the outdoor environment.