Multi-sensor Controller Research Articles

VLSI Implementation of a Cost-Efficient Micro Control Unit With an Asymmetric Encryption for Wireless Body Sensor Networks

This paper presents a very large-scale integration (VLSI) circuit design of a micro control unit (MCU) for wireless body sensor networks (WBSNs) in cost-intention. The proposed MCU design consists of an asynchronous interface, a multisensor controller, a register bank, a hardware-shared filter, a lossless compressor, an encryption encoder, an error correct coding (ECC) circuit, a universal asynchronous receiver/transmitter interface, a power management, and a QRS complex detector. A hardware-sharing technique was added to reduce the silicon area of a hardware-shared filter and provided functions in terms of high-pass, low-pass, and band-pass filters according to the uses of various body signals. The QRS complex detector was designed for calculating QRS information of the ECG signals. In addition, the QRS information is helpful to obtain the heart beats. The lossless compressor consists of an adaptive trending predictor and an extensible hybrid entropy encoder, which provides various methods to compress the different characteristics of body signals adaptively. Furthermore, an encryption encoder based on an asymmetric cryptography technique was designed to protect the private physical information during wireless transmission. The proposed MCU design in this paper contained 7.61k gate counts and consumed 1.33 mW when operating at 200 MHz by using a 90-nm CMOS process. Compared with previous designs, this paper has the benefits of increasing the average compression rate by over 12% in ECG signal, providing body signals analysis, and enhancing security of the WBSNs.

An Efficient Micro Control Unit with a Reconfigurable Filter Design for Wireless Body Sensor Networks (WBSNs)

In this paper, a low-cost, low-power and high performance micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of an asynchronous interface, a register bank, a reconfigurable filter, a slop-feature forecast, a lossless data encoder, an error correct coding (ECC) encoder, a UART interface, a power management (PWM), and a multi-sensor controller. To improve the system performance and expansion abilities, the asynchronous interface is added for handling signal exchanges between different clock domains. To eliminate the noise of various bio-signals, the reconfigurable filter is created to provide the functions of average, binomial and sharpen filters. The slop-feature forecast and the lossless data encoder is proposed to reduce the data of various biomedical signals for transmission. Furthermore, the ECC encoder is added to improve the reliability for the wireless transmission and the UART interface is employed the proposed design to be compatible with wireless devices. For long-term healthcare monitoring application, a power management technique is developed for reducing the power consumption of the WBSN system. In addition, the proposed design can be operated with four different bio-sensors simultaneously. The proposed design was successfully tested with a FPGA verification board. The VLSI architecture of this work contains 7.67-K gate counts and consumes the power of 5.8 mW or 1.9 mW at 100 MHz or 133 MHz processing rate using a TSMC 0.18 μm or 0.13 μm CMOS process. Compared with previous techniques, this design achieves higher performance, more functions, more flexibility and higher compatibility than other micro controller designs.

An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs)

In this work, an asynchronous multi-sensor micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE), and an error correct coder (ECC). To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs.

A Neural Network Ensemble-Based Approach to Sensor Fault Detection

This paper focuses on the problem of detecting sensor faults in feedback control systems with multistage RBF neural network ensemble-based estimators. The sensor fault detection framework is introduced. The modeling process of the estimator is presented. Fault detection is accomplished by evaluating residuals, which are the differences between the actual values of sensor outputs and the estimated values. The particular feature of the fault detection approach is using the data sequences of multi-sensor readings and controller outputs to establish the bank of estimators and fault-sensitive detectors. A detectability study has also been done with the additive type of sensor faults. The effectiveness of the proposed approach is demonstrated by means of three tank system experiment results.