This brief proposes a 3-stage dual-branch charge pump (CP) with an advanced dynamic gate-biasing technique (DGB) enabling ultra-low-voltage (0.1 V) energy harvesting. Specifically, we reduce the forward conduction loss and the reverse current leakage loss with the combination of an advanced DGB and an NMOS-PMOS dual-switch transistor pair. Also, we investigate the relationship between the pumping capacitance and the power conversion efficiency (PCE) of the CP, thus guiding the PCE improvement with minimal capacitance. The prototype fabricated in 65-nm CMOS achieves a 43.4% PCE at a 0.1-V input voltage.

This article describes a fully integrated CMOS radio frequency energy-harvesting (RFEH) front end. It features an on-chip stacked step-up transformer integrated with a cross-coupled differential drive (CCDD) rectifier to enhance the input sensitivity. The transformer also serves as an on-chip balun for the CCDD rectifier. The CCDD rectifier innovates a gate-biasing technique and realizes coupling capacitors at the end of each stage to increase the subsequent stage biasing. Here, our RFEH front end operating at 900 MHz achieves an improved sensitivity of −20 and −19.2 dBm at the 1-V output for no-load and a 1-

Small vessels detection is a known issue due to its low radar cross section (RCS). An existing shore-based vessel tracking radar is for long-distance commercial vessels detection. Meanwhile, a vessel-mounted radar system known for its reliability has a limitation due to its single radar coverage. The paper presented a co-located frequency modulated continuous waveform (FMCW) maritime radar for small vessel detection utilising a multiple-input multiple-output (MIMO) configuration. The radar behaviour is numerically simulated for detecting a Swerling 1 target which resembles small maritime’s vessels. The simulated MIMO configuration comprised two transmitting and receiving nodes. The proposal is to utilize a multi-frequency FMCW MIMO configuration in a maritime environment by applying the spectrum averaging (SA) to fuse MIMO received signals for range and velocity estimation. The analysis was summarised and displayed in terms of estimation error performance, probability of error and average error. The simulation outcomes an improvement of 2.2 dB for a static target, and 0.1 dB for a moving target, in resulting the 20% probability of range error with the MIMO setup. A moving vessel's effect was observed to degrade the range error estimation performance between 0.6 to 2.7 dB. Meanwhile, the proposed method was proven to improve the 20% probability of velocity error by 1.75 dB. The impact of multi-frequency MIMO was also observed to produce better average error performance.

This paper evaluated the performance of range detection of a MIMO FMCW radar system detecting small vessels in maritime environments, through numerical simulations. The targets were modeled as Swerling1 targets, which resemble target with slow-changing radar cross sections (RCS). A MIMO FMCW system utilizing two transmitting antennas emitting two FMCW signals in different frequency bands, and two receiving antennas for MIMO processing - is proposed. In the MIMO scheme, a waveform design comprises of two sub-bands with an interval band, is suggested to avoid co-band interference between the two signals emitted from two transmitting antennas. At the receiver, the receiving signals were combined by means of spectrum averaging, before implementation of peak detection to estimate the target range. The performance of the proposed system is observed in terms of probability of range error, simulated over a range of signal-to-noise-ratio (SNR). Simulation results indicated that MIMO processing yields approximately 3 dB improvements of probability of range error against SNR, compared to SISO case observed at 20% range error probability. In addition, the effects of interval band deployment on the quality of beat frequency was also discussed.

This paper proposes a MIMO FM-CW radar system using beat signal averaging method for maritime surveillance application. The system operates by transmitting multiple waveform signals and receiving multiple waveform signals, before averaging the beat signals at each receiver. A numerical simulation was conducted to validate the performance of the proposed method in terms of ranging error probability vs SNR. It is found that the radar shows high accuracy in range estimation even in low SNR values, compared to conventional FM-CW radar using single antenna system.

In this Covid -19 pandemic era, where the negative impact of the virus is felt worldwide, it is crucial to reduce the rate of infection. This paper presents the development of a vehicle to minimize the spread of the disease. The Coronavirus-Autonomous Ground Vehicle (COR-AGV) uses Ultraviolet Light (UV) rather than liquid sanitiser. The use of UV light as a disinfectant is proven in the medical field, where many medical appliances were disinfected using UV light without the need for alcohol substances. Previous research showed thatsimilarCoronavirus such as SARS and MERS lost their ability to infect when exposed to the UV for a particular durationasthe exposure causeddamageto the virus's DNA sequence. The COR-AGV presented herewith uses UV Light to carry out the disinfection task autonomously. The COR-AGV, equipped with a far-UVC light array, navigates autonomously within the targeted areas to sweep systematically, scan and disinfect. The COR-AGV is equipped with camera and sonar sensors to enable path planning and obstacle avoidance in confined spaces and outdoor environments. A GPS is included to assist the COR-AGV navigation system. Simulation test runs were performed to check the adequacy of the rover in performing disinfection. The COR-AGV perform disinfection on the wall surface with more than 50% of the wall surface were covered for disinfection in various floor layouts, while being able to perform obstacle avoidance within the test area. Thus, COR-AGV can be adapted prior to the actual pandemic event to enhance its level of performance. The performance of COR-AGV can be improved with additional sensors such as computer Vision or LiDAR sensor.

Unmanned Aerial Vehicle (UAV), especially quadcopter is widely used in search and rescue, mapping, surveillance and infrastructure inspection. The quadcopter's ability to fly outdoor and return-to-home is important as it simplifies the quadcopter operation and reduces the possibilities of the quadcopter suffering damages and loss. This work proposes a system for a quadcopter performing landing operation on a moving landing platform. This system is based on coordinates of both quadcopter and the moving landing platform using Global Positioning System (GPS) and feedback control using radio frequency communication. From the GPS inputs, the system identifies the distance and yaw angle between these two points and lands on the moving platform without pilot assistance during the landing sequences. The results show that the accuracy of landing on a moving platform based on the performance of the GPS used was within the radius of 2 metres from its target landing point. This paper contributes to the advancement of quadcopter precision landing through developing methods, which use low-cost equipment without compromising the accuracy of the process. Another contribution in this paper shows the ability and accuracy of the quadcopter to execute precision landing on a moving platform without using complex image processing techniques or a high capability computer on the quadcopter, to achieve a real-time UAV navigation.

Virtual reality (VR) technology has catalyzed the development of flexible sensors to interact with objects in a virtual environment. Current solutions for smart glove sensors are limited by rigid factors. In this article, a novel smart glove is developed that utilizes a flexible sensor made from a polyethylene-carbon composite (Velostat) to manipulate a hand model in a 3-dimensional (3D) virtual reality (VR) environment. The sensors are specifically designed to measure the angle of finger joint flexion, and an interface circuit has been developed to process the joint angle data. The strain sensors attached to the smart glove show a high sensitivity of 59.8 % rad−1, with a response time of 15.8 ms when the joint angle increases from 0° to 30° under the dynamic response test. The fabricated smart glove successfully controls a 3D hand in a VR environment. This proof-of-concept experiment explores the potential application of the smart glove for VR telerehabilitation.