This paper proposes an implantable antenna with ultra-wide bandwidth operating in the Medical Device Radio communications Service band (MedRadio) (401-406 MHz) for wireless capsule endoscopy (WCE). Simulation and experimental results show the proposed antenna has good performance in terms of the return loss and hence the bandwidth from 284 MHz to 825 MHz. The the maximum realized gain of this antenna is -31.5 dBi at 403 MHz. The maximum simulated input power should be <1.7 mW to satisfy SAR regulations in the IEEE standard. The tolerance of the antenna owing to bendability and different WCE shell thicknesses is investigated. These indicate that the proposed antenna is a good candidate for WCE.

This article presents an innovative belt antenna with an electromagnetic band-gap (EBG) ground plane made of textile materials. The antenna can be applied in a smart belt system to set up a communication link with other electronic devices and/or host a variety of sensors to track human motions. The proposed belt antenna works at 2.45 GHz in the industrial, scientific, and medical radio band for Bluetooth low energy communications. Considering the effect the human body would have on the performance of a belt antenna, a textile ground plane is designed to be integrated into the trouser fabric behind the belt to provide isolation from the body and simultaneously improve antenna radiation characteristics. Through the application of the ground plane, the belt antenna achieves a maximum realized gain of 7.94 dBi and a minimum specific absorption rate of 0.04 W/kg at 0.5 W input power. During the design process, characteristic mode analysis is used to explore the underlining principle and further optimize the antenna performance. Two typical EBG structures are analyzed in detail for this application scenario. The suspended transmission line method is used to evaluate EBG performance variations when the textile ground plane is bent. A prototype of such a system is fabricated and tested. Experimental results shows that the belt antenna, together with the textile EBG ground plane, is an excellent candidate for a smart belt system with desirable radiation pattern, efficiency, and safety limit.

In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets ofQandRvalues (KF’s parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tuneQandRparameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.

A near-field wireless power transfer (WPT) system for implantable medical devices, such as pacemakers, is proposed. Operating at 403 MHz within the medical implants communication service (MICS) band, the WPT link constitutes a primary loop to be based outside the body as a transmitter and an implantable loop with a single-turn as a receiver. Simulation and experimental results show that the proposed link offers good power transfer efficiency (PTE) performance. The maximum measured PTE of the proposed link is 57.9% at a transfer distance of 6 mm through 1 mm of air and 5 mm of body tissue. The maximum input power that can be supplied to stay within specific absorption rate safety guidelines is 159 mW. A rectifying circuit is designed to convert 403 MHz RF signals to dc for a 1.5 $\text{k}\Omega $ load with a measured conversion efficiency of 73.2%. The measured end-to-end PTE of the proposed WPT system is 42.4%.

Indirect holographic techniques offer the potential of using simple and inexpensive near-field intensity-pattern measurements for the determination of the radiation characteristics of microwave antennas, and for the reconstruction of complex aperture fields. This work describes a practical method of applying indirect holographic techniques to microwave antennas. It describes how a technique originally developed at optical frequencies can be adapted to enable measurements to be taken on microwave antennas. The major difference is the replacement of a radiated reference signal by an electronically generated reference signal. This has enabled previous practical limitations to be overcome. Computer simulations and practical results are included for a large dish antenna at a sample spacing similar to those employed in direct holographic techniques. This work also describes how reducing the sample spacing significantly below a half wavelength enables the radiation characteristics of smaller antennas to be determined

Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) applications in skin lesion research, with the goal of encouraging the development of automated systems for skin disease diagnosis. To assist dermatologists in their clinical diagnosis, several skin image datasets have been developed and published online. Such efforts have motivated researchers and medical staff to develop automatic skin diagnosis systems using image segmentation and classification processes. This paper summarizes the fundamental steps in skin lesion diagnosis based on papers mainly published since 2013. The applications of ML methods (including traditional ML and deep learning (DL)) in skin disease recognition are reviewed based on their contributions, methods, and achieved results. Such technical analysis is beneficial to the continuing development of reliable and effective computer-aided skin disease diagnosis systems. We believe that more research efforts will lead to the current automatic skin diagnosis studies being used in real clinical settings in the near future.