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We report the effects of gold nanoparticles (GNP) on the enhancement of the dose effect and double-strand break (DSB) induction for X-ray monoenergies (10 keV–2 MeV). The overall relative biological effectiveness (RBE) was defined as the product of dose and DSB enhancement. The cellular doses and energy spectra were computed using the PENELOPE (PENetration and Energy LOss of Positrons and Electrons) code. The DNA damage yields were estimated using the MCDS (Monte Carlo damage simulation) code. Considering the production of Auger electrons induced by 7mg/ml GNP, we found that dose enhancement at the 30 keV was ~4.0 for cells with radius $4~\mu \text{m}$ . The DSB induction increased as GNP concentrations increased and was dependent on X-ray energy. These findings demonstrated that the maximum RBE was attained at 30–40 keV and therefore this energy range would be more efficient in cancer cell killing.

This paper proposes an adaptive fuzzy K-Means that uses time and travel as calculation parameters, in order to predicts operating points of medical beds with multi-axial actuators. It is found through the simulation that when a data far from the current result is added, the outcome would has a larger mean shift, thus making the forecast worse than expected. I in order to let the model prediction results be closer to the operator's habits, an adaptive fuzzy K-means algorithm combined with multiple sets of rules are designed with the membership functions of trapezoidal, triangular, Gaussian, and generalized bell. Since the users’ habits change dynamically, this paper makes the fuzzy rules adaptive and strengthens the model through design changes of the fuzzy rules. The numerical result of the adaptive fuzzy K-means algorithm is satisfactory since the use of four different membership functions of trapezoidal, triangular, Gaussian and generalized bell can result in the similar output results after the proper parameter adjustments. Finally, with the Arduino Mega 2560 development board and the commercially available actuator modules, and the Hall sensor signal used as the position feedback, a medical bed with three multi-axial actuators is built and then adopted to verify the applicability of the proposed algorithm.

This paper presents a new learning control structure using output recurrent broad learning system (ORBLS) for adaptive nonlinear PID control of unknown digital nonlinear dynamic systems with time delays. The proposed control method, abbreviated as ORBLS-ANPID, is novel in combining ORBLS and model predictive control to develop a new Nonlinear PID control law for high-performance setpoint tracking control and disturbance rejection. Comparative simulations on two renowned nonlinear digital time-delay systems are well used to show the effectiveness and superiority of the proposed method by comparing to six existing methods.

This paper presents a new learning control structure using fuzzy broad learning system (FBLS) for adaptive model predictive control (AMPC) of nonlinear digital time-delay dynamic systems. The proposed control method, abbreviated as FBLS-ANMPC, is novel in combining FBLS and model predictive control to develop a FBLS-ANMPC control law for high-performance setpoint tracking control and disturbance rejection. Comparative simulations are well used to demonstrate the effectiveness and merits of the proposed method in comparison with five existing methods. Experimental results on temperature control of a heating barrel in a plastic injection molding machine are conducted to show the practicability of the proposed method.

The next generation of $5\mathrm{G}$ mobile phone cooling requires many ultra-thin vapor chambers. This paper discusses how to measure the performance characterization of the vapor chamber. All the experiment is processing under the horizontal orientation so that the gravity effect could reduce to minimum. The thermal diffusivity of the two ultra-thin vapor chambers VC-A (180*80*1mm3) and VC-B (90*90*0.4mm3) developed from laboratory are $\alpha_{\mathrm{V}\mathrm{C}-\mathrm{A}}=1.9(\mathrm{cm}^{2}/\mathrm{s})$ and $\alpha_{\mathrm{VC}-\mathrm{B}}=1.44(\mathrm{cm}^{2}/\mathrm{s})$ . Converted to equivalent effective thermal conductivity are $\mathrm{K}_{\mathrm{A}}=4833.3(\mathrm{W}/\mathrm{m}.\mathrm{K})$ and $\mathrm{K}_{\mathrm{B}}=3069(\mathrm{W}/\mathrm{m}.\mathrm{K})$ , which is about ten times of that of pure copper. The maximum heat transfer of VC-B is Q VC $\max,\mathrm{B}=210(\mathrm{W})$ , the axial thermal resistance $\mathrm{R}_{\mathrm{th},\mathrm{VC},Z,\mathrm{B}}=0.04$ (K/W), and the thermal resistance of vapor chamber $\mathrm{R}_{\mathrm{th},\mathrm{VC},\mathrm{B}}=0.42$ (K/W). Using the same method to test the thicker vapor chamber of C brand (90*90*3mm3), the Qvc, $\displaystyle \max,\mathrm{C}^{=551(\mathrm{W})}$ , the axial thermal resistance $\mathrm{R}_{\mathrm{th},\mathrm{V}\mathrm{C},Z,\mathrm{C}}=0.08$ (K/W), and the average thermal resistance of the vapor chamber is $\mathrm{R}_{\mathrm{th},\mathrm{V}\mathrm{C},\mathrm{C}}=0.177(\mathrm{K}/\mathrm{W})$ . The highest thermal conductivity K value vapor chamber with 90×60×0.4 mm3 designed in ACL currently is vapor chamber VC-D, the equivalent thermal conductivity is 33519 (W/m-k).

The paper presents a novel sliding-mode control method augmented with broad learning system (BLS), or abbreviated as BLS-SMC, for trajectory tracking and station keeping of an uncertain Inverse-Atlas ball-riding robot (IASBRR) driven by three omnidirectional wheels. After brief description of the dynamic model of the robot with frictions and gravity, a BLS-SMC controller is proposed to accomplish robust self-balancing and trajectory tracking of the IASBRR in the presence of unknown frictions, mass variations and model uncertainties. The proposed BLS-SMC controller is proven asymptotically stable using Lyapunov stability theory and Barbalta’s lemma. Three comparative simulations and two experiments are conducted to show the effectiveness and merits of the proposed control method. The comparative results also indicate that the proposed controller is superior more efficient by comparing to an existing method.

Current medical surgical navigation allows surgeons to use the surgeon as a more accurate aid. However, most Dicom images are projected onto another screen, and it is quite inconvenient for the doctor to perform surgery on the patient while watching another screen. This paper takes brain surgery as an example, using augmented reality technology based on image target positioning system, combined with smart phone, can instantly display the image of Dicom superimposed on the patient's head to provide surgical assistance for doctors. The surgical vision is more intuitive and focused. This paper is also implemented on Android smart phones to prove the feasibility of this technology.

This paper presents an etherCAT-based impedance control method for a 6-degrees-of-freedom (DOF) industrial robotic manipulator. After describing the overall system structure of this impedance controller using EtherCAT communication network, the forward kinematics of the manipulator is then derived using its Denavit-Hartenberg (DH) parameters. Based on the forward kinematics model and geometrical configurations of the robotic arm, an analytically inverse kinematics method is proposed to find the six joint angles of the manipulator moving from one point to another. For impedance control, a PI impedance controller along with a 6-DOF force/torque sensor and six independent PID torque controllers is proposed to achieve impedance control with satisfactory performance. The effectiveness and merit of the proposed inverse kinematics method and impedance controller are well exemplified by conducting two simulations one experiment on a really 6-DOF industrial robotic manipulator from HIWIN.

Deep learning is an increasingly important technique that can solve complex problems. Due to the growth of data and model complexity, large-scale deep learning has became an important issue. Distributed deep learning is an efficient way to address these complexity issues in training a huge model. However, in a distributed environment network bandwidth becomes a performance bottleneck for deep learning. We propose various optimizations in reducing network usage by scheduling network request events properly, so as to reduce the total training time. These scheduling optimization only requires software innovation and without the need to upgrade physical network bandwidth, thus is economically competitive. The experiments indicate that our scheduler achieves up to 25 % speedup over traditional schedulers.

In this paper, an integral terminal sliding-mode (ITSM) consensus control method is presented for a team of uncertain, networked, heterogeneous Mecanum-wheeled omnidirectional robots (MWORs) moving together in formation. After the description of the dynamic model of each kind of MWOR by a set of unified multivariable vector state equations, the interconnection structure of the multi-robots is modeled by a directed and strongly connected graph. An ITSM formation control is proposed to achieve finite-time formation control and trajectory tracking in presence of robot uncertainties. The usefulness and superiority of the proposed method are well exemplified by conducting three computer simulations on cooperative formation of four heterogeneous MWORs with a virtual leader.

This paper presents a more efficient control algorithm for selecting suitable firing nodes and removing unimportant input variables automatically to improve the computational efficiency, reduce the number of firing rules and achieve good performance for the nonlinear control systems. A novel function-link fuzzy cerebellar model articulation controller (FLFC) is designed for uncertain nonlinear systems based on a multiple attribute decision-marking method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed control system is comprised a TOPSIS FLFC (TFLFC) and a robust compensator. The parameters of the proposed TFLFC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so the system's stability is guaranteed. Finally, the proposed control system is used to control an inverted pendulum system and a master-slave Duffing-Holmes chaotic system to illustrate its favorable control performance.

Comparative study of bifacial and monofacial c-Si module performance under specific sequential tests were demonstrated in this article. The tests in each sequence include preconditioning, UV exposure, dynamic mechanical load test, thermal cycling, humidity freeze and damp heat according to qualification protocol IEC 61215, IEC TS 62782 and IEC 62892 (draft). In addition, the effect of long term temperature cycling and extended damp heat were used to assess the material failure induced power degradation in both bifacial and monofacial c-Si modules.

In this paper, a novel but simple control method using command feedforward and feedback PID control, or called two degrees-of-freedom (Two-DOF) control, is presented for temperature control of a stretch polyethylene terephthalate (PET) blow molding machine. To achieve required temperature control of PET bottle performs passing through both heating ovens, our proposed controller consists of a digital command-feedforward controller designed to improve the transient performance and track quickly temperature set-points, and a digital feedback proportional-integral-derivative (PID) controller proposed to eliminate temperature errors and accomplish disturbances rejection. Such a controller not only carries out precise temperature setpoint tracking under any arbitrary ambient environment, but also uses the practical expertise of the control practitioners working for PET blow molding machines. Excellent set-point tracking and disturbance rejection of the proposed control method are well illustrated by computer simulations and experimental results. The results also clearly show applicability and superiority of the proposed method in comparison with other existing controllers.

Although cloud storage integration services have been developed in recent years that providing a single interface to manage multiple cloud storage accounts across different providers, these cloud storage integration services still remain several issues that do not address. First, because these services do not support arrangement mechanisms for users, they need to keep track of which account they want to upload files to and arrange their storage spaces manually. Second, since each account is divided into different sections, it is impossible to provide a virtual folder that contains files from different cloud storage providers. In addition, when all accounts are nearly full, integration services could not chunk a file to store it across multiple accounts. Third, uploading and moving files across accounts are always done through the integration service's server, which might become a bottleneck when there are a lot of users uploading files at the same time. In this paper, we propose a new cloud storage integration service - Unified Cloud Storage (UCS) for solving the above-mentioned issues, and evaluate its performance by experiments.

This paper presents an adaptive nonsingular terminal sliding mode formation control by means of output recurrent fuzzy wavelet neural networks for a group of networked heterogeneous Mecanum-wheeled omnidirectional robots with uncertainties. The dynamic behavior of each uncertain heterogeneous omnidirectional robot is modelled by a reduced three-input-three-output second-order state equation with uncertainties and the multi-robot system is modeled by graph theory. Through the Lyapunov stability theory and online learning of the system uncertainties via output recurrent fuzzy wavelet neural networks, an adaptive nonsingular terminal sliding mode control approach is offered to attain finite-time formation control in presence of uncertainties. Conducting three simulations are to show the effectiveness and merit of the proposed method.

This paper presents a leader-follower consensus formation control method using backstepping sliding-mode control for a group of uncertain, networked heterogeneous nonholonomic wheeled mobile robots (NWMRs), in order to achieve formation keeping and trajectory tracking, respectively. The NWMRs are composed of two kind of wheeled mobile robots, including nonholonomic self-balancing two-wheeled mobile robots (NSBTWMRs) and nonholonomic wheeled differential-driving mobile Robots (NWDDMRs). The dynamic behavior of each NWMR is governed by its second-order dynamic model, and the networked multi-NWMR system is modeled by a directed graph. By using the Lyapunov stability and sliding-mode control theories, one intelligent adaptive, distributed consensus control approach is respectively presented to carry out formation keeping and trajectory tracking in the presence of uncertainties, in order to not only keep all the robots in formation, but also let them track their desired trajectories and maintain them in formation, respectively. Simulations are conducted to show the effectiveness and merits of the proposed methods.

On the aged society coming soon, many studies have explored homecare technologies. In this work, the activities at home are captured by a panoramic camera located at the center of a living room, and then analyzed and classified into standing, walking, sitting, falling, and watching television. First, the background subtraction scheme accompanied with shadow removal, and morphological operators of erosion and dilation is used to find out moving subjects, and TV on/off switching. Second, the parameters associated with the subject height, the distance between the camera and subject, and the distance map between subject contour points and centroid are computed to derive features for recognizing activities of standing, walking, sitting, and falling where the Support Vector Machine (SVM) classifier is employed. Additionally, the moving object similar to a parallelogram shape is identified as a TV set. Third, the majority voting in a picture segment of 10 pictures is adopted to determine the final recognition result. When subject sitting and TV on occur simultaneously, this situation is treated as an activity of watching TV. The experimental results reveal that our activity recognition system can achieve the accuracy rate up to 91.1%. The error rates associated with subject height and distance estimations are around 5.5% and 8.2%, respectively. The accuracies of TV on and off detection are close to 95.9% and 100.0%, respectively. Therefore, the proposed activity recognition system exhibits a superior performance for homecare applications.

This paper presents an intelligent predictive PI temperature control using particle swarm optimization - real coded genetic algorithm (PSO-RGA) for transfer molding modules in semiconductor die packaging machines. The system parameters of the transfer molding process are obtained by using the well-known reaction curve method. The best control parameters of the PI controller are offline tuned by using PSO-RGA algorithm. The set-point tracking, disturbance rejection, and robustness capabilities of the proposed method are well exemplified by conducting simulations and experiments on a real transfer mold process.

This paper presents an intelligent sliding mode formation control using recurrent fuzzy wavelet neural networks (RFWNN) for a group of uncertain, networked heterogeneous Mecanum-wheeled omnidirectional platforms (MWOPs). The dynamic behavior of each uncertain MWOP is modelled by a reduced three-input-three-output second-order state equation and the multi-MWOP system is modeled by a directed graph. By using the Lyapunov stability theory and online learning the system uncertainties via RFWNN, an intelligent adaptive, sliding mode control approach is presented to carry out formation control in presence of uncertainties. Two simulations are conducted to show the effectiveness and merit of the proposed method with existing collision-free and obstacle-avoidance approaches.

This paper presents an distributed consensus formation control using recurrent fuzzy wavelet neural networks (RFWNN) for a group of heterogeneous mobile three-Swedish-wheeled omnidirectional robots (TSWORs) with uncertainties. The dynamic behavior of each TSWOR is modelled by a three-input-three-output second-order state equation and the multirobot system is modeled by a directed graph. By online learning the system uncertainties using RFWNN and using the Lyapunov stability theory, an intelligent adaptive distributed consensus formation control approach is presented to carry out formation control in the presence of heterogeneity and uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

This paper presents two exponential leader-follower consensus formation control approaches with collision and obstacle avoidance for a networked, homogenous quadrotors cooperatively flying at a fixed altitude. The dynamic behavior of each quadrotor is modelled by a fourth-order system model and the multi-quadrotor system is modeled by a directed graph which has a spanning tree with the root being the leader. By using the Lyapunov and sliding-mode control techniques, two exponential, distributed consensus formation control approaches are presented to accomplish consensus formation control. Simulations are conducted to show the effectiveness and merits of the proposed methods with Collision - free and obstacle-avoidance methods.

This paper presents a design methodology of EMI/RFI suppression by SI/PI simulation at smartphone applications. In smartphones, the high speed transmission lines of display serial interface (DSI) at LCD panel are designed on a 2-layer flexible printed circuit (FPC) board. Serious simultaneously switching noise and interference are appeared to distort the GSM communication channels. In this paper, we design the FPC with optimized current return paths and decoupling capacitors by SI/PI simulation. This solution achieves 4 dB noise suppression at GSM-1900 band without cost increasing on the FPC design.

This paper presents a distributed consensus formation control using recurrent interval type-2 fuzzy neural networks (RIT2FNN) for a team of uncertain ballbots. The dynamic equations of each ballbot in sagittal and coronal planes can be decomposed into two identical second-order underactuated dynamic system models, and the multirobot system is modeled by graph theory. By online learning the system uncertainties using RIT2FNN and the Lyapunov stability theory, an intelligent distributed consensus formation control law is presented to carry out formation control in the presence of uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

This paper presents a decentralized cooperative transportation control method with obstacle avoidance using fuzzy wavelet neural networks (FWNN) and consensus algorithm for a group of mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties, in order to get together to move a large payload. The dynamic behavior of each uncertain MWOR is modelled by a reduced three-input-three-output second-order system model and the uncertain multi-MWOR system is modeled by graph theory. By online learning the system uncertainties using FWNN and using the Lyapunov stability theory, an intelligent adaptive, cooperative consensus-based control approach is presented to carry out transportation control with uncertainties. An obstacle avoidance method is proposed to modify the generating trajectory of the virtual leader, in order to avoid any collisions between the robots and the environment. Two simulations are conducted to show the effectiveness of the proposed method.

A comprehensive analysis of fin profile effect on bulk FinFET device characteristics is described in this paper. Optimal fin profile and anti-punch-through (APT) implant profile are important to DC performance and multiple-Vt offering capability, which are essential for system-on-chip (SoC) applications. This study provides practical device design guidelines for bulk FinFET technology.

This paper presents a decentralized consensus formation control using fuzzy wavelet neural networks (FWNN) for a group of homogenous, mobile four-Mecanum-wheeled omnidirectional vehicles (MWOVs) with uncertainties. The dynamic behavior of each MWOV is remodeled by a three-input-three-output second-order system model and the multi-MWOV system is modeled by graph theory. By online learning the system uncertainties using FWNN and using backstepping techniques, an intelligent adaptive, cooperative consensus-based control approach is presented to carry out formation control in the presence of uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

This paper presents a direct adaptive motion controller using fuzzy wavelet neural networks (FWNN) for speed control of an electric two-wheeled robotic vehicle (ETWRV) with unknown parameters and uncertainties. With the decomposition of the overall system into two subsystems: yaw motion control and mobile inverted pendulum, two direct adaptive FWNN motion controllers are respectively proposed to achieve station keeping, speed following and yaw motion control. Asymptotic stabilities of the two controllers with their FWNN weighting updating rules are derived via the Lyapunov stability theory. Simulation results indicate that the proposed controllers are capable of providing satisfactory control actions to steer the vehicle.

This paper presents an adaptive robust motion control using fuzzy wavelet neural networks (FWNN) for a electric two-wheeled robotic vehicles (ETWRV). A mechatronic system structure driven by two DC motors is briefly described, and its nonlinear mathematical modeling incorporating the friction between the wheels and the motion surface is derived. With the decomposition of the overall system into two subsystems: yaw control and inverted pendulum, two intelligent adaptive FWNN controllers are proposed to achieve self-balancing, speed tracking and yaw motion control. Simulation results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.

In the paper, an adaptive decoupling predictive temperature control using neural networks (NN) is presented for extrusion barrels in plastic injection molding machines. Due to weakly coupling effects, the extrusion barrels are approximated by decoupling linear system models together with independent NN models. These decoupling system parameters and NN models are experimentally determined using the recursive least-squares estimation (RLSE) approach with forgetting factor. The adaptive decoupling predictive PI control laws together with NN compensating terms are developed by minimizing a generalized predictive cost function. A real-time control algorithm is then proposed to achieve temperature control of extrusion barrels. Experimental results on a laboratory-built extrusion barrel are conducted to illustrate the usefulness and applicability of the proposed method are well exemplified by conducting.

This paper presents an interval type-2 fuzzy maximum power point tracking (IT2FMPPT) control method for a solar photovoltaic (PV) power generation systems with a DC/DC buck converters and a battery charging system. After the brief model descriptions of the solar PV power charging system, the maximum power and its optimal voltage of the system at different illuminations are numerically solved using a Newton-Raphson method. Via the DC/DC buck converter and the current feedback control technique, the proposed MPPT controller is synthesized using PI control and IT2 fuzzy control logics. The asymptotical stability of the overall closed-loop system with the proposed IT2FMPPT controller is investigated in some details. The effectiveness and merit of the proposed IT2FMPPT controller are exemplified by conducting simulations via Matlab/Simulink. Through comparative simulations, the proposed IT2FMPPT controller is shown superior in comparison with a conventional PI MPPT controller and a conventional fuzzy MPPT controller.

This paper presents an intelligent consensus-based cooperative formation control using recurrent fuzzy wavelet cerebellar-model-articulation-controller (RFWCMAC) for a team of uncertain multiple ballbots. The dynamic model of each ballbot is formulated as one multivariable second-order underactuated dynamic system model, and the multirobot system is modeled by graph theory. By online learning the system uncertainties using RFWCMAC, an intelligent consensus-based cooperative formation control approach is presented using the Lyapunov stability theory and sliding-mode control approach, in order to carry out formation control in the presence of uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

Currently, people obtained housing information from relatives, friends, sale representatives from model home, Realty networks, or government. It is difficult to get the housing-related information from these resources and hard to compare. Therefore, we proposed a public inquiry website called Housing Price Analysis using information from public websites to do statistical analyses based on the total ratio of "average value" and "standard deviation". People can use this website to get better understanding of price trend in certain area. This website will compare all records on public realty websites and government system based on same area with similar size of houses, number of rooms, age of house, etc. By using this system, users can get better understanding the trend of housing prices.

The paper proposes a novel method for enhanced fusion adaptive fuzzy control in the two-wheeled balancing six degrees of freedom robotic arm. The two-wheeled mobile robot system and six degrees of freedom robotic arm are integrated into the mobile robot system. Due to the motion of the robot arm, the stability issue becomes more complex. This study employs the adaptive fuzzy control to provide suitable controller for the mobile robot system. In addition, a dynamic learning rate can effectively improve the learning performance. In order to show the superiority of the proposed controller, this paper compares our proposed method with other control methods as proportional-derivative (PD) controller, fuzzy controller, adaptive fuzzy controller, PD fuzzy controller, and adaptive PD controller. The experiment results clearly demonstrate that the proposed control method has much faster convergent speed and is very well performed.

Besides of normal face-to-face learning, learners can use electrical resources to perform digital learning in educational area. Learners can use communication tools such as real-time communication tools, online conferencing tools, and collaborative learning web-sites to perform synchronous learning. This paper proposed a browser-based plugin free online tutoring system. By applying browser-based technologies to perform media data transmission, users could use the proposed system without the need of installing client applications. The system integrates services for account management, digital whiteboard, collaborative editing for online document and data synchronization to make the server-side more effective. Users are able to use the system to find online class participants and perform synchronous online learning.

This paper presents an adaptive intelligent steering controller using backstepping sliding-mode control and recurrent wavelet fuzzy cerebellar model articulation controller (RWFCMAC) and for an uncertain ball-riding human transporter in presence of significant system uncertainties. The proposed controller operates at two independent modes: self-balancing and station keeping. The self-balancing mode is used to balance by following the rider's two-dimensional inclination angles, while the station-keeping mode is aimed to permit the rider to keep the vehicle at a target position. The RWFCMAC is designed to online learn the uncertainties caused by riders' weights and different parameters. The superior performance and merit of the proposed control methods are well exemplified by conducting two simulations.

High voltage I/O FinFET device optimization for a 16nm system-on-a-chip (SoC) technology is presented. After careful optimization through high electric field (E-field) mitigation by junction engineering, I/O FinFET devices with leakage current reduction by 1∼2 orders, hot carrier injection (HCI) lifetime improvement by 2.8×/1.2× for N- and PMOS, respectively, and junction breakdown voltage (V bd ) improvement by more than 0.8V are achieved.

This paper presents an direct adaptive control for an uncertain electric seatless unicycle using wavelet fuzzy cerebella model articulation controller (WFCMAC). The WFCMAC is proposed to approximate the equivalent control part and uncertainties. A nonlinear backstepping sliding-mode controller with on-line adaptive laws is presented to accomplish adaptive robust self-balancing and speed control of the seatless electric unicycle based on the rider's body inclination and the speed of the unicycle. Simulations results indicate that the performance and merit of the proposed method are well illustrated by comparing to two existing controllers.

Exploring an unknown environment for a multi-robot system (MRS) is the problem of controlling a team of robots over all points of a given region in an efficient and safety manner. In this paper a cooperative exploration approach is developed by using a robot-sensor-network (RSN) and communication system and the technique of minimal information entropy. The exploration strategy is able to be divided by two stages: collection and coverage stage. In the first stage, considering the neighbors of the ith robot of the MRS, a grid-based approach incorporating with a potential approach is proposed for covering the maximal area in the sense of balance force. Furthermore, in the second stage, a spiral approach incorporating with the nonholonomic trajectory tracking control design is employed. Theoretical proof shows the stability of the proposed cooperative exploration method. Finally, simulations are also conducted to show the effectiveness of the proposed approach.

A novel circularly polarized (CP) loop tag antenna for a UHF band (902–928 MHz) RFID tag is proposed. The structure of the proposed tag antenna is a square loop type loaded with an open gap, two feeding strips, and a matching stub. Besides achieving satisfactory 3-dB axial ratio bandwidth of 5.45% and 10-dB impedance bandwidths of approximately 5.5%, good antenna gain and reading range of more than 2.8 dBic and 16.3 m, respectively, were also demonstrated.

A novel inverted-Z tag antenna for circularly polarized operation in the UHF band RFID application is proposed. The structure of the proposed tag antenna comprises two inverted-Z strips, one printed on top side and the other on bottom side of the substrate, perpendicularly to each other. The measured 10-dB impedance bandwidth of the tag antenna is 33 MHz (897–930 MHz), and its 3-dB axial-ratio bandwidth is 32 MHz (899–931 MHz). The antenna gain and reading range of about 1.38 dBic and 14 m, respectively, were also demonstrated.

This paper presents indirect adaptive self-balancing and station keeping control methods using recurrent Wavelet Fuzzy CMAC (RWFCMAC) for an omnidirectional ball-driven chair in presence of significant system uncertainties. By backstepping, sliding-mode control and RWFCMAC, the self-balancing controller is synthesized to follow the rider’s inclination angles in both two axes (x-z and y-z axis), and the station-keeping controller is designed to allow the rider to maintain the vehicle at the same place. The RWFCMAC is designed to online learning the uncertainties caused by riders’ weights and different unknown frictions between the ball and terrain surfaces. The superior performance and merit of the proposed control methods are well exemplified by comparing to two existing controllers.