Design of ispPAC-based Humidity Sensor Signal Processing Circuits

The widely used sensitive elements of humidity sensors can be divided into 3 types,i.e.,resistor,capacitor,and electrolyte.Humidity sensors consisting of these sensitive elements have corresponding signal processing circuit unique to each type of sensitive elements.This paper presents an ispPAC (in-system programmable Programmable Analog Circuit) -based humidity sensor signal processing circuit designed with software method and implemented with in-system programmable simulators.Practical operation shows that humidity sensor signal processing circuits of this kind,exhibit stable and reliable performance.

Design and implementation of a hybrid circuit system for micro sensor signal processing

This paper covers a micro sensor analog signal processing circuit system（MASPS） chip with low power and a digital signal processing circuit board implementation including hardware connection and software design. Attention has been paid to incorporate the MASPS chip into the digital circuit board.The ultimate aim is to form a hybrid circuit used for mixed-signal processing,which can be applied to a micro sensor flow monitoring system.

THE EFFECTS OF CORRELATED SENSOR SIGNAL FLUCTUATION ON THE STATISTICAL PERFORMANCE OF AN AR HIGH RESOLUTION ARRAY PROCESSOR

The statistical performance of AR high resolution array processor in presence of correlated sensor signal fluctuation is studied. Mean square inverse beam pattern and pointing error are examined. Special attention is paid to the effects of reference sensor and correlation between sensors. It is shown that fluctuation causes broadening or even distortion of the mean square inverse beam pattern. Phase fluctuation causes pointing error. Its standard variance is proportional to that of fluctuation and is related to the number of sensors of the array. Correlation between sensors has important effects on pointing error.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

Direction finding of coexisted independent and coherent signals using electromagnetic vector sensor

The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.

A Sensor Failure Detection Method Based on Artificial Neural Network and Signal Processing

This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.

基于PIR Sensor的单目标跟踪系统的设计与实现

针对基于热释电红外传感器的人体跟踪系统在实际环境中,由于受环境噪声和硬件参数影响而误差较高的问题,本文提出了一种基于热释电红外传感器PIR Sensor(Pyroelectric Infrared Sensor)的高精度人体目标跟踪方案。该方案首先对检测区域内运动人体热辐射的红外信号特征进行了提取,然后利用PIR Sensor定位节点自身几何参数和探测数据,得到初步定位结果。最后通过Kalman滤波算法对初步定位结果进行滤波处理并更新目标的状态信息,实现对检测区域内人体目标的定位与跟踪。实验表明,该系统的跟踪误差与同类跟踪系统相比降低了71.96%,证明了该系统具有较高的跟踪精度。

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

Internal Electrical Noises of BioFET Sensors Based on Various Architectures

The results of a comparative literature analysis of internal electrical noises and signal-to-noise ratio for nanoscale BioFET (biological field-effect transistor) and DNA (deoxyribonucleic acid) sensors based on different architectures MIS (metal-insulator-semiconductor), EIS (electrolyte-insulator-semi-conductor) and ISFET (ion-selective field-effect transistor) are presented. Main types, models and mechanisms of internal noises of bio- & chemical field-effect based sensors are analyzed, summarized and presented. For the first time, corresponding detail electrical equivalent circuits were built to calculate the spectral densities of noises generated in the active part of a solid (semiconductor, dielectric) and in an aqueous solution for MIS, EIS and ISFET structures based sensors. Complete expressions are obtained for the rms (root mean square) value of the noise current (or voltage), as well as the noise spectral densities for the architectures under study. The miniaturization of biosensors leads to a decrease in the level of the useful signal-current. For successful operation of the sensor, it is necessary to ensure a high value of the SNR (signal-to-noise ratio). In case of weak useful signals, it is necessary to reduce the level of internal electrical noise. This work is devoted to a detailed study of the types and mechanisms of internal electrical noises in specific biosensor architectures.

A modified phase diversity wavefront sensor with a diffraction grating

The phase diversity wavefront sensor is one of the tools used to estimate wavefront aberration, and it is often used as a wavefront sensor in adaptive optics systems. However, the performance of the traditional phase diversity wavefront sensor is limited by the accuracy and dynamic ranges of the intensity distribution at the focus and defocus positions of the CCD camera. In this paper, a modified phase diversity wavefront sensor based on a diffraction grating is proposed to improve the ability to measure the wavefront aberration with larger amplitude and higher spatial frequency. The basic principle and the optics construction of the proposed method are also described in detail. The noise propagation property of the proposed method is also analysed by using the numerical simulation method, and comparison between the diffraction grating phase diversity wavefront sensor and the traditional phase diversity wavefront sensor is also made. The simulation results show that the diffraction grating phase diversity wavefront sensor can obviously improve the ability to measure the wavefront aberration, especially the wavefront aberration with larger amplitude and higher spatial frequency.

A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks

Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.

Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

针对传感节点的基于signal-slot电源管理方案设计与实现

随着传感网络和3G网络的融合,物联网已经成为新世纪最重要的技术之一,如何延长传感节点的工作时间已成为物联网研究的一个重要课题。传统的电源管理规范如APM(Advanced Power Management)和ACPI(Advanced Configuration and Power Interface)主要针对PC设计,因其复杂性和对BIOS层要求等因素,在无线传感节点中并不适用。为了解决此问题,针对传感节点计算和存储能力有限的特点,我们首先开发了精简的signalslot框架,基于signal-slot框架,并设计了简单有效的电源管理方案SPM(Simple Power Management),并将SPM在流行的传感节点操作系统Contiki中实现。

Robust digital receiver for EPC sensor network

A robust digital receiver based on a matched filter （MF） is proposed for the radio frequency identification （RFID） reader system to enhance the reliability of signal processing in the electronic product code （EPC） sensor network （ESN）. The performance of the proposed receiver is investigated by examining the anti-collision algorithm in the EPC global Class1 Generation2 protocol. The validity and usefulness are demonstrated by both computer simulations and experiments. Based on the verification results, comparing with the conventional zero crossing detector （ZCD） based receiver, the proposed receiver is very robust against strong amplitude distortions and considerable frequency deviations happening on the backscattered signal from a passive tag.

High sensitive and rapid responsive n-type Si: Au sensor for monitoring breath rate

125 μm-breath sensor with high sensitivity and rapid response was prepared by using n-type Si: Au material. Its sensitivity coefficient and time constant were 4 V.sec / L and 38 msec, respec-tively. Its working principle was based on ano- malous resistance effect, which not only increa- sed the sensitivity, but also reduced its time con-stant greatly. Its signal processing system can select the breath signals and work stably. Therefore, the small changes of breath system can be measured and, especially, patient’s breath rate can be monitored at a distance.

New low noise interface circuit for detecting weak current of micro-sensors

A new low noise interface circuit for detecting weak current of micro-sensors is designed.By using the transimpedance amplifier to substitute the charge amplifier,the closed-loop circuit can avoid the phase error of the charge amplifier.Therefore,the phase compensation devices will be cancelled,because there is no phase transformation through the transimpedance amplifier.As well as,by using CCCII devices to implement the high value feedback resistor of the impedance amplifier,the noise of the I-V transformation devices is reduced,comparing with the passive resistor.The floating resistor is easy to be integrated into chips,making the integration of the interface circuit of the intelligent sensors increase.Through the simulation,the phase error of the charge amplifier is almost 9°at 2 kHz and it changes with the working frequency of the micro-sensors making the phase compensation not easy.The value of the floating resistor is 250 kΩ where the bias current is 50 μA.The noise of the active resistor is 0.037 fV2/Hz,comparing with the noise of the passive resistor,which is 4.14 fV2/Hz.

Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm

Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.

Interface Design Of Digital Platform For Bio Signal Processing

Bio-sensor arrays for multi-channel recording have been developed recently and signal processing platforms for those signals have been studied actively.But it’s thereal situation which these technologies are generally developed and studied respectively.So the interface design between recording array and signal processing platform is also an important issue to make bio-sensor signal processing system.In this paper,we proposed interface which has unique protocols to control sensor array and operate platform.There are two types of protocols in the interface.One is between sensor array and MCU in platform and the other is between MCU and board for wireless communication.Basically,each protocol has two kinds of modes(single,frame)and it can be extended if needed.

A Classification Algorithm for Ground Moving Targets Based on Magnetic Sensors

A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.