An 8-tap feed-forward equalizer (FFE) and 10-tap decision feedback equalizer (DFE) were designed for the IEEE 802.3u 100Base-TX specification. The weights of these adaptive filters are updated with a sign–sign least-mean-square (SSLMS) algorithm. To ensure the flexibility of the circuits for various environments channel lengths, the equalizers are designed such that the number of taps can be adjusted to between 1 and 8 for the FFE and 1 and 10 for the DFE. Results indicated that the equalizer can compensate for channel delays of more than 20 dB, and it achieved a bit error rate of 2 × 10−3. Both FFE and DFE not only mitigate signal distortion but also significantly eliminate inter-symbol interference (ISI) in high-speed serializer/deserializer (SerDes) communication systems. The application-specific integrated circuit (ASIC) was designed using Verilog Hardware Description Language (HDL) and implemented with the TSMC 90-nm CMOS 1P9M standard cell process. In simulations, the chip area, delay cycle, and logic gate counts were 845.445 × 845.445 μm2, 12 cycles, and 75 187 gates, respectively. The supplied voltage and frequency of the proposed adaptive DFE were 1.2 V and 250 MHz, respectively.

This study proposes an Internet of Things (IoT)-based smart home system that sends and receives packets through the RS232 protocol and processes them using deep learning. A Field-Programmable Gate Array (FPGA) development board serves as a transceiver, operating a universal serial bus (USB) interface and a Wi-Fi module. The proposed system comprises a built-in wireless transceiver, a set of sensors, a development board running a deep learning algorithm, an MQTT communication protocol, and a terminal device controller. The objective is to implement an IoT -based smart home system with wireless data transmission. Node-RED is used to develop a comprehensive smart home system on the server side for IoT applications, facilitating data access, data processing, and terminal device control. The aim is to achieve automatic regulation and ensure comfortable indoor temperatures. In experiments, the root mean squared error difference between actual and predicted temperatures was approximately 0.4426 °C. After evaluation experiments with the FPGA development board, an application-specific integrated circuit (ASIC) based on the TSMC 0.18-μm CMOS process was used. Simulation results indicate that the chip area is approximately 1.186 × 1.188 mm2, and the dynamic power consumption is approximately 8.1674 mW at a power supply of 1.8 V and operating frequencies of 50 and 5 MHz.

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of chronic diseases and deaths worldwide. When acute exacerbation of COPD (AECOPD) occurs, the frequency and severity of malignant attacks are highly correlated with the mortality rate. The purpose of this study is to use wearable devices to collect the physiological parameters of patients for early warning and prevention of complications of possible AECOPD attacks in the future. The subjects used wearable devices to measure Heart Rate Variability (HRV) at home. Physiological data and health assessment scales of 13 COPD patients were collected during the 6-month study period. According to the scale responses, the severity of the condition was classified into mild AE and no AE. If the subject needed emergency medical treatment due to COPD, it was classified as AE. With the scale classification method, a machine-learning Random Forest (RF) algorithm is used to predict the occurrence of AECOPD in the next 7 days, so as to prevent the deterioration of the disease in advance. The results of the study show that the accuracy of the model is more than 92% according to different classification methods, and using the mixed-parameter model as a feature for the prediction can improve the sensitivity of the original warning mechanism. In order to provide predictive results to the nursing staff at any time, the user interface of our system would transmit a warning message to remind the nursing staff to ensure early medical intervention for patients to avoid the occurrence of AECOPD.

In this study, we propose a modified predictive direct torque control (PDTC) application-specific integrated circuit (ASIC), comprising a neural network (NN) proportional integral derivative (PID) controller, speed-sensorless control, fuzzy error controller, and seven-stage hysteresis controller, to alleviate the ripple problem induced by limited vector voltages and slow speed response in conventional direct torque control. Both flux and torque errors pass through the modified discrete multiple vector voltage switch table to obtain the required vector voltages, and the proposed NN PID controller is used to convert the speed error into a torque command. Notably, the motor speed is evaluated from the magnetic flux, which is calculated using two-phase currents and voltages. The speed-sensorless control not only accelerates the feedback control but also rotates more stably. The NN PID controller generates a torque command according to the speed error, which is obtained by subtracting the estimated predictive speed from the actual speed. The advantages of the proposed system are that it reduces the flux and torque ripples and increases the control stability by filtering out the external interferences. The Verilog hardware description language is used to implement the proposed PDTC ASIC system, and a field-programmable gate array development board is used to verify the designed functions.

This paper presents an Ethernet packet transformation and transmission architecture between Modbus transmission control protocol (Modbus/TCP) and universal serial bus (USB) 3.0 developed with a field-programmable gate array (FPGA) development board. The proposed architecture is used to complete packet transformation and transmission between Ethernet and USB 3.0 for application in plant automation. The Ethernet receiver receives and analyzes Modbus/TCP packets and sends the source address, destination address, IP header, and Modbus/TCP header to the register to verify the correctness of the packet. The Modbus/TCP packet is stored in static random access memory and awaits access by a USB 3.0 module. An FPGA development board (Intel DE10-Standard) is used for functional verification. The measured results show that the latency, throughput, and dynamic power are 18.845 ×s, 747.45 Mbps, and 142.17 mW, respectively, at a voltage of 1.8 V and operating frequency of 125 MHz.

A mobile node localization algorithm based on the Extended Kalman Filter (EKF) along with Radio Signal Strength Index (RSSI) information is proposed in this paper for mobile sensor networks. The localization process is two-fold: the initialization phase and subsequent localization phase. If a node receives the information broadcast from three anchor (or beacon) nodes, it will be localized initially and then enter the subsequent phase. Different from the localization methods using EKF with RSSI requiring three anchor nodes or more, the proposed method estimates the position of a localized node whether or not any anchor node is received. The simulation results indicate that the node localization rate of the proposed method outperforms. Also, more anchor nodes received, more accuracy localization results observed.

Video telephone systems are convenient media for people to communicate remotely in life. If the video can be analyzed in the process of video chat to extract the relevant physiological indicators of the communicators such as emotions or stress, it can help the communicators to make proper conversations. In this paper, we propose (1) Similar Brightness Area Selection Method (2) and Difference-driven Weight-Setting Method to measure the heart rate in the non-contact image mode that can be adapted to the video signal in the videophone. After the image is compressed, it is transmitted through the network. After being transmitted to the video receiver for image decompression, we also propose that the luminance (Y) instead of Green channel (G) of the image should be utilized based on Photoplethysmography (PPG) to infer the heart rate. In our experiment, we have adjusted the weight range of our Difference-driven Weight-Setting Method for four commonly used video compression codecs including H.264, H.265, VP8, and VP9 together with five different compression bit rates and the results have shown to be effective in improving the accuracy of the heart rate measurement. The root mean square error (RMSE) of the heart rate measurement has also been calculated as evaluation metrics in the experiment. The mean and standard deviation of the overall RMSE is 2.14±0.52 bpm (beats per minute) that have shown the feasibility of our proposed methods to effectively and accurately measure the heart rate in the video telephony system.

With the Chatbot API, this paper proposes an IoT-based home automation system. The Chatbot simplifies the user interface and allows users being able to control each device at home by way of chatting with Chatbot. To build up the system, the Line messaging API is adopted to conduct smart home applications. After the user interacts with Chatbot, Line Server transmits the conversation message to the back-end analysis platform through HTTPS secure transmission. Once the platform analyzes the messages and makes relevant decisions, control messages will be transmitted to the central control board through MQTT communication to control various appliances at home. To demonstrate the efficacy, a prototype automation system is implemented. The experimental results show that the proposed system with Chatbot achieves applications such as mart adjustment, emotion regulation, and security monitoring in a natural human-like interaction way.

This study proposes a piezoelectric energy-harvesting module (PEHM) for sensor nodes, which can provide energy by using motor vibration signals. Moreover, a prototype PEHM is developed in this study, which is used by sensor nodes in an environmental monitoring system. The monitoring system consists of a front-end sensing platform, a back-end server, and a cloud database. The monitoring data are collected by the sensor nodes and then transmitted to the back-end server using a layered multi-hop scheme. The goals of the monitoring system are to detect motor in the factory. The information of motor faults are displayed on a webpage in real time, after the monitoring data are processed and analyzed to find motor faults. In addition, all monitoring data and analysis results are uploaded to the cloud database, so remote monitoring can be achieved. Also, the proposed PEHM is field tested where motors operate continuously. In the test, the nodes transmit a heavy-load packets for 10 minutes and then enter the sleep mode. The PEHM operating at the rated motor speed of 1800rpm, charging for 24.3 hours is observed to reach the node power self-sufficiency. The test results show that the proposed PEHM is capably of stably suppling energy to sensor nodes and building up a cloud factory monitoring system by using the wireless sensor network with PEHM.

This paper proposes an intelligent motor rotary fault diagnosis system using the dynamic structural neural network. In order to improve the convergence efficiency, the terminal attractors integrated with the bat algorithm is applied to the motor rotary fault detection. The optimization parameter, learning rate, is set as the positions of bats is optimized by the dynamic adjustment mechanism. Also, the MATLAB R2010b software is used to establish the bat algorithm based terminal attractor learning method. By conducting numerical simulations of training neural networks, the efficiency of the proposed algorithm and other methods are compared and analyzed. Finally, this work uses the method to fulfill the module of motor fault diagnosis. From the results of motor fault simulation and analysis, the proposed method increases the learning speed and reduce the learning error of fault diagnosis.

This study aims to design an adaptive output recurrent cerebellar model articulation controller (AORCMAC), which is embedded into the direct torque control (DTC) system of an induction motor as the speed controller. Similar to the conventional cerebellar model articulation controller (CMAC), the designed AORCMAC also has the advantages of rapid learning, simple architecture, online training, and nonlinear learning abilities. In addition, by incorporating the Gaussian function and recursion, the AORCMAC provides satisfactory dynamic response. This study compares the AORCMAC with the adaptive fuzzy CMAC (AFCMAC) and uses the root mean square error as the indicator for performance assessment. The experiment results verify that the proposed AORCMAC has rapid speed response, and its performance is superior to that of the AFCMAC. The AORCMAC maintains excellent robustness despite changes to the motor parameters and the addition of external load disturbances.

Localization is a very important issue to wireless sensor networks. The sensor node requires accurate location information in order to achieve the purpose of real-time monitoring and transmission of information. The more the sensor nodes and GPS modules are used in localization, the higher the localization accuracy can achieve, but employing more sensor nodes also leads to high costs. Thus, how to improve the localization accuracy while solving the problem of high costs has garnered attention. In this paper, an area-restriction based localization (ARBL) algorithm is proposed. The ARBL algorithm uses both range-based and range-free schemes, which combines area restriction setting and the sampling points from Monte Carlo localization (MCL) to improve the localization accuracy. The simulation results show that the ARBL algorithm proposed in this paper outperforms DuRT, MMRL and RL algorithms that employ a mobile anchor node, regardless of the number of the sensor nodes and the moving speed of the anchor node.

This paper investigates the type-2 T-S fuzzy multiple feedback-loop guaranteed cost controller design for uncertain singular systems with transmission and state delays. Based on the type-2 T-S fuzzy theory, this work constructs a mathematical model for a class of nonlinear uncertain singular time-delay systems. Using the parallel distributed compensation concept with Lyapunov theory and linear matrix inequality technique, a robust guaranteed cost fuzzy controller is designed with the multiple feedback-loop structure to stabilize the considered system with guaranteed cost performance. Finally, an optimization procedure is presented to reduce the upper bound of guaranteed cost. According to the simulation results, when parametric uncertainties and transmission and state delays exist simultaneously, it indicates that the proposed guaranteed cost controller demonstrates better design flexibility. Also, the closed-loop system is robustly stable and the upper bound of guaranteed cost is decreased.

The robust H-infinity controller design is investigated in this paper for a class of nonlinear singular systems with state and transmission delays. The proposed controller is configured in multiple feedback loops. Based on the interval type-2 T-S fuzzy modelling and parallel distributed compensation techniques, a fuzzy multiple feedback-loop controller with memoryless state and delayed states is proposed to stabilize the system with required H-infinity performance by using the Lyapunov stability theory. The controller gain matrices are able to be calculated by solving the linear matrix inequalities. To check the feasibility of the proposed method, a numerical example is given and the simulation results indicate that the proposed multiple-feedback-loop controller is effective.

This paper presents an adaptive sliding self-organizing fuzzy controller (ASSOFC) designed using fuzzy theory and a self-organizing algorithm. Composed of a conventional fuzzy controller (FC) and self-organizing algorithm, the ASSOFC adopts the sliding surface signal as an input, uses the algorithm to adjust the central position of the output consequent membership function of the FC, and, by fuzzy control, regulates the learning rate and fuzzy rules in real time to improve control performance. The ASSOFC is embedded into the direct torque control system of a switched reluctance motor (SRM) as a speed controller, and the performance and feasibility of the controller were validated. The experimental results indicate that the root mean square error values for the ASSOFC at various speed ranges are lower than those for a conventional FC, indicating that the proposed controller provides a superior speed response for SRMs.

This paper investigates the robust H controller design for a class of nonlinear singular time-delay systems. The parametric uncertainties are also considered in this work. To solve the problem, firstly, the nonlinear system is modeled by using the interval type-2 T-S fuzzy theory. To analyze the stability, by choosing a proper Lyapunov functional, the delay dependent characteristics are taken into account. Moreover, free weighting matrices are introduced to reduce the conservativeness. Consequently, using the linear matrix inequality technique, a sufficient condition for the stability of singular delayed control system is obtained. As to the controller design, this work introduces the concept of duality. Along with the stability condition, the desired robust H8 controller design can be derived. Finally, a numerical example is simulated and analyzed by using the Mat lab LMI toolbox. According to the simulation results, the designed controller stabilizes the nonlinear systems and achieves the H8 performance. It verifies the correctness of the proposed controller design method.

This study proposed a fuzzy excitation controller to reduce noise and torque ripples for switched reluctance motors. The design of the controller is simple and can generate appropriate turn-on and turn-off angles according to the speed of torque error in order to improve the torque response. At the motor speed of 300 rpm with one Nm load, the experimental results showed that the implantation of a fuzzy excitation controller in the driver system substantially improved the steady state torque ripples generated when compared to those generated by traditional controllers (at fixed excitation angles), especially at low speeds.

This paper applies the Taguchi method to filter out the number of input neurons and increases the training efficiency of the dynamic structural neural networks. In order to avoid that omitting the harmonics may affect the fault diagnosis result, this work establishes an index for the fault identification which is based on the features of the first and second harmonics. Together with the identification results of dynamic structural neural network, the diagnosis can be done. The experimental results indicate the proposed method can reduce the iterations dramatically.

By referencing the adaptive stator flux estimator (ASFE) framework in the model reference adaptive system (MRAS), this study designed an adaptive rotor speed estimator and a stator resistance estimator, and applied the Takagi-Sugeno-Kang (TSK) fuzzy system and projection algorithms to the estimators to establish an induction motor direct torque controlled system without a speed sensor and possessing stator resistance adjustment abilities. In addition, the adaptive TSK fuzzy controller (ATSKFC) was adopted as the speed controller of the system, and was capable of online learning. The transient response was improved by the integration of a refined compensation controller.

This paper proposes a single-stage high-efficacy fly back power-factor-correction (PFC) converter with optimization efficiency control for driving multi-string light-emitting diodes (LEDs). The LED driver consists of a fly back converter with PFC mechanism and a constant-current drive circuit with functionalities of efficiency optimization and dimming control. The proposed single-stage LED driver topology features benefits of high power factor and low total harmonic distortion (THD) in low-cost outlay. The pulse width modulation (PWM) dimming mechanism cooperating with the constant-current control circuit completes the LED dimming control. In order to reduce the power dissipations of the dimming circuits, a dynamic voltage regulation (DVR) control is presented to regulate the LED supply voltage by means of the sensing of the drain voltage of the MOSFET in the current controller. While maintaining the desired LED brightness, the DVR technique can minimize the voltage drop of the dimming circuit to enhance the LED driver efficiency further. A 30W white LED driver is devised and a corresponding driver prototype is realized. Testing results are shown experimentally to verify the effectiveness and performance improved of the proposed scheme.

Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers. Each of the roles represents a person that uses a mobile device such as a mobile phone to communicate with the server setup in the care center such that he or she can go around without restrictions. For commercial mobile phones with Bluetooth communication capability attached to chronic patients, we have developed physiological signal recognition algorithms that were implemented and built-in in the mobile phone without affecting its original communication functions. It is thus possible to integrate several front-end mobile care devices with Bluetooth communication capability to extract patients' various physiological parameters [such as blood pressure, pulse, saturation of haemoglobin (SpO2), and electrocardiogram (ECG)], to monitor multiple physiological signals without space limit, and to upload important or abnormal physiological information to healthcare center for storage and analysis or transmit the information to physicians and healthcare providers for further processing. Thus, the physiological signal extraction devices only have to deal with signal extraction and wireless transmission. Since they do not have to do signal processing, their form factor can be further reduced to reach the goal of microminiaturization and power saving. An alert management mechanism has been included in back-end healthcare center to initiate various strategies for automatic emergency alerts after receiving emergency messages or after automatically recognizing emergency messages. Within the time intervals in system setting, according to the medical history of a specific patient, our prototype system can inform various healthcare providers in sequence to provide healthcare service with their reply to ensure the accuracy of alert information and the completeness of early warning notification to further improve the healthcare quality. In the end, with the testing results and performance evaluation of our implemented system prototype, we conclude that it is possible to set up a complete intelligent healt care chain with mobile monitoring and healthcare service via the assistance of our system.