Experimental Study of Monitoring and Controlling of Composite Cure Process in Autoclave Featured with Fiber Optic Sensor

With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely and efficiently.In this paper, experiment results of viscosity measurement in composite cure process in autoclave using fiber optic sensors are presented. Based on the sensed information, a computer program is utilized to control the cure process. With this technology, the cure process becomes more apparent and controllable, which will greatly improve the cured products and reduce the cost.

Organic bulk heterojunction enabled with nanocapsules of hydrate vanadium pentaoxide layer for high responsivity self-powered photodetector

This article demonstrates the fabrication of organic-based devices using a low-cost solution-processable technique.A blended heterojunction of chlorine substituted 2D-conjugated polymer PBDB-T-2Cl,and PC71BM supported nanocapsules hy-drate vanadium penta oxides(HVO)as hole transport layer(HTL)based photodetector fabricated on an ITO coated glass sub-strate under ambient condition.The device forms an excellent organic junction diode with a good rectification ratio of~200.The device has also shown excellent photodetection properties under photoconductive mode(at reverse bias)and zero bias for green light wavelength.A very high responsivity of~6500 mA/W and high external quantum efficiency(EQE)of 1400%have been reported in the article.The proposed organic photodetector exhibits an excellent response and recovery time of~30 and~40 ms,respectively.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Robust high-resolution beam-forming based on high order cross sensor processing method

In order to obtain the robust high-resolution beamforming, a high order cross sensor processing(CSP) approach is developed. According to the relation ship between the target bearing and the phase difference of each element receiving signal, this method exploits the property that the same diagonal of covariance matrix with the same phase difference and obtains(2M-1)(N-1)virtual elements(N is the original array number) by executing M order CSP. The extended virtual elements can effectively increase the physical aperture of linear array, reduce the main lobe width of beam-forming, and improve the bearing resolution. The CSP method accumulates the data on the same sub-diagonal of the covariance matrix, which can decrease the impact of background noise on beam-forming. The theoretical analysis and experimental results both show that this method has high resolution in bearing estimation, compared with the MUSIC method, which has better robustness under the lower signal-to-noise ratio(SNR).

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.

CMOS vision sensor with fully digital image process integrated into low power 1/8-inch chip

A digital still camera image processing system on a chip, different from the video camera system, is pre- sented for mobile phone to reduce the power consumption and size. A new color interpolation algorithm is proposed to enhance the image quality. The system can also process fixed patten noise （FPN） reduction, color correction, gamma correction, RGB/YUV space transfer, etc. The chip is controlled by sensor regis- ters by inter-integrated circuit （I2C） interface. The voltage for both the front-end analog and the pad cir- cuits is 2.8 V, and the volatge for the image signal processing is 1.8 V. The chip running under the external 13.5-MHz clock has a video data rate of 30 frames/s and the measured power dissipation is about 75 roW.

A new physical unclonable function architecture

This paper describes a new silicon physical unclonable function （PUF） architecture that can be fabri- cated on a standard CMOS process. Our proposed architecture is built using process sensors, difference amplifier, comparator, voting mechanism and diffusion algorithm circuit. Multiple identical process sensors are fabricated on the same chip. Due to manufacturing process variations, each sensor produces slightly different physical charac- teristic values that can be compared in order to create a digital identification for the chip. The diffusion algorithm circuit ensures further that the PUF based on the proposed architecture is able to effectively identify a population of ICs. We also improve the stability of PUF design with respect to temporary environmental variations like temperature and supply voltage with the introduction of difference amplifier and voting mechanism. The PUF built on the proposed architecture is fabricated in 0.18 μm CMOS technology. Experimental results show that the PUF has a good output statistical characteristic of uniform distribution and a high stability of 98.1% with respect to temperature variation from -40 to 100 ℃, and supply voltage variation from 1.7 to 1.9 V.

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

In recent years, Compressed Sensing（CS） has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit（OMP） is fixed, thus limiting reconstruction precision.Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit（RIOMP） algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations.The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses.When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.

How much information is lost when sampling driving behavior data?Indicators to quantify the extent of information loss

Purpose–Individuals’driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems.The incoming data can be sampled at rates ranging from one Hertz(or even lower)to hundreds of Hertz.Failing to capture substantial changes in vehicle movements over time by“undersampling”can cause loss of information and misinterpretations of the data,but“oversampling”can waste storage and processing resources.The purpose of this study is to empirically explore how micro-driving decisions to maintain speed,accelerate or decelerate,can be best captured,without substantial loss of information.Design/methodology/approach–This study creates a set of indicators to quantify the magnitude of information loss(MIL).Each indicator is calculated as a percentage to index the extent of information loss(EIL)in different situations.An overall information loss index named EIL is created to combine the MIL indicators.Data from a driving simulator study collected at 20 Hertz are analyzed(N=718,481 data points from 35,924 s of driving tests).The study quantifies the relationship between information loss indicators and sampling rates.Findings–The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz,but the relationship is not linear.With four indicators of MILs,the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data.If sampling rates are higher than 2 Hz,all MILs are under 5 per cent for importation loss.Originality/value–This study contributes by developing a framework for quantifying the relationship between sampling rates,and information loss and depending on the objective of their study,researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.