三叉神经痛阈检测仪的压力传感器设计与实现

三叉神经痛是发生于脸部三叉神经分布区的骤然剧烈的疼痛,临床中医生主要通过感觉检查等方法诊断,缺乏准确性与系统性。运用Comsol Multiphysics仿真软件设计一种MEMS电容压力传感器,可用于三叉神经痛阈检测仪前端的压力传感探头对患者的压痛阈值进行采集,帮助医生诊断患者病情。通过对设计的压力传感器的受力进行物理场的模拟仿真及分析,结果表明,在固定外界压强0~2.5 kPa时,此传感器灵敏度可达1.05×10;pF/Pa,并且具有良好的线性度。通过硬件实验,最终可在液晶显示器上显示出采集到的压力值,满足三叉神经痛阈检测仪前端的压力采集探头对压力信号采集的要求。

鱼雷被动声自导检测概率研究

为了研究鱼雷声自导性能,有必要对自导系统检测概率进行分析。从自导接收机信号处理方式、检测概率与检测阈的关系出发,提出了基于信号余量的被动声自导检测概率计算方法。仿真结果表明,鱼雷声自导系统被动检测概率在距离上成三种分布。在一定距离内,检测概率均超过0.5;在远距离上仅有极个别深度可检测到潜艇目标;在前两者之间,检测概率受深度影响较大,因此有必要进行深度上的优化选择。

三值CMOS门电路的设计

该文通过一个实例说明三值逻辑电路的设计原理

听觉正常人群和听神经病变患者的听觉时间进程

Objective: To study objectively auditory temporal processing in a group of nor mal hearing subjects and in a group of hearing-impaired individuals with audito ry neuropathy (AN) using electrophysiological and psychoacoustic methods. Method s:Scalp recorded evoked potentials were measured to brief silent intervals (gaps ) varying between 2 and 50 ms embedded in continuous noise. Latencies and amplit udes of N100 and P200 were measured and analyzed in two conditions:(1) active, w hen using a button in response to gaps;(2) passive, listening, but not respondin g. Results: In normal subjects evoked potentials(N100/P200 components) were reco rded in response to gaps as short as 5 ms in both active and passive conditions. Gap evoked potentials in AN subjects appeared only with prolonged gap durations (10-50 ms). There was a close association between gap detection thresholds mea sured psychoacoustically and electrophysiologically in both normals and in AN su bjects.Conclusions: Auditory cortical potentials can provide objective measures of auditory temporal processes. Significance:The combination of electrophysiolog ical and psychoacoustic methods converged to provide useful objective measures f or studying auditory cortical temporal processing in normals and hearing-impair ed individuals. The procedure used may also provide objective measures of tempor al processing for evaluating special populations such as children who may not be able to provide subjective responses.

Design of 10 Gbit/s burst-mode transimpedance preamplifier for PON systems

A 10 Gbit/s burst-mode preamplifier is designed for passive optical networks （PONs）. To achieve a high dynamic range and fast response, the circuit is DC coupled, and a feed-back type peak detector is designed to perform auto-gaincontrol and threshold extraction. Regulated cascade （RGC） architecture is exploited as the input stage to reduce the input impedance of the circuit and isolate the large parasitic capacitance including the photodiode capacitance from the determination pole, thus increasing the bandwidth. This preamplifier is implemented using the low-cost 0. 13 ixm CMOS technology. The die area is 425 μm × 475 μm and the total power dissipation is 23.4 mW. The test results indicate that the preamplifier can work at a speed from 1.25 to 10.312 5 Gbit/s, providing a high transimpedance gain of 64.0 dBΩ and a low gain of 54. 6 dBl2 with a dynamic input range of over 22.9 dB. The equivalent input noise current is 23. 4 pA/ Hz1/2. The proposed burst amplifier satisfies related specifications defined in 10G-EPON and XG-PON standards.

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.

Determination of Threshold for Energy Detection in Cognitive Radio Sensor Networks

The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.

Joint Optimal Sensing Threshold and Subcarrier Power Allocation in Wideband Cognitive Radio for Minimising Interference to Primary User

Cognitive Radio （CR） can use the fre- quency band allocated to a Primary User （PU） on the premise that it will prevent significant of avoiding causing great interference to the PU. In this paper, we consider a wideband CR system where the Secondary User （SU） mini- raises its interference to the PU by jointly al- locating the optimal sensing threshold and sub- carrier power. A multi-parameter optimization problem is formulated to obtain the joint opt- imal allocation by alternating direction opti- mization, which minimises the total interfer- ence to the PU over all of the subcarriers sub- ject to the constraints on the throughput, Bit Error Rate （BER） and maximal total power of the SU, the subcarrier rate and interference power of the PU, and the false alarm and mis- detection probabilities of each subcarrier. The simulation results show that the proposed joint allocation algorithm can achieve the desired mitigation on the interference to the PU at the different subcarrier gains.

PERFORMANCE EVALUATION OF THE DOUBLE-THRESHOLD CFAR DETECTOR IN MULTIPLE-TARGET SITUATIONS

In radar systems of automatic detection, an estimate of background clutter power is used to set the detection threshold. An interference saturated environment is frequently encountered in these systems (multiple target situations). Therefore, the detection of signals in such an environment becomes one of the most important problems to be solved. The double-threshold algorithm is one of the more interesting detectors used in these situations. While the first threshold operation ensures that the calculation of the detection (second) threshold is based on a set of samples which is free of strong interferers and is therefore much more representative of the noise level, the second threshold is used to declare the presence or the absence of the radar target. The object of the present paper is to analyze the performance of such type of CFAR schemes when the radar receiver contains a noncoherent integrator amongst its basic elements. It is found that the processor detectabil ity loss is very low and the performance degradation, caused by interferers is quite small even if the number of outlying targets is large, given that the first threshold is properly chosen.

A new approach for real time object detection and tracking on high resolution and multi-camera surveillance videos using GPU

High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computational algorithms for real time processing of high resolution videos. Motion detection and background separation play a vital role in capturing the object of interest in surveillance videos, but as we move towards high resolution cameras, the time-complexity of the algorithm increases and thus fails to be a part of real time systems. Parallel architecture provides a surpass platform to work efficiently with complex algorithmic solutions. In this work, a method was proposed for identifying the moving objects perfectly in the videos using adaptive background making, motion detection and object estimation. The pre-processing part includes an adaptive block background making model and a dynamically adaptive thresholding technique to estimate the moving objects. The post processing includes a competent parallel connected component labelling algorithm to estimate perfectly the objects of interest. New parallel processing strategies are developed on each stage of the algorithm to reduce the time-complexity of the system. This algorithm has achieved a average speedup of 12.26 times for lower resolution video frames(320×240, 720×480, 1024×768) and 7.30 times for higher resolution video frames(1360×768, 1920×1080, 2560×1440) on GPU, which is superior to CPU processing. Also, this algorithm was tested by changing the number of threads in a thread block and the minimum execution time has been achieved for 16×16 thread block. And this algorithm was tested on a night sequence where the amount of light in the scene is very less and still the algorithm has given a significant speedup and accuracy in determining the object.

Towards a cure for HIV： the identification and characterization of HIV reservoirs in optimally treated people

Currently available anti-HIV-1 drugs suppress viral replication and maintain viral levels below the detection threshold of most assays but do not eliminate cellular reservoirs. As a result, very low levels of circulating virus can be detected in most people despite long-term treatment with potent anti-HIV drug combinations. Not surprisingly, viral levels rebound with discontinuation of treatment. New evidence indicates that there is a viral reservoir in bone marrow progenitor cells.

Proximal and distal esophageal sensitivity is decreased in patients with Barrett’s esophagus

AIM: To investigate sensations to multimodal pain stimulation in the metaplastic and normal parts of the esophagus in patients with Barrett’s esophagus (BE). METHODS: Fifteen patients with BE and 15 age-matched healthy volunteers were subjected to mechanical, thermal and electrical pain stimuli of the esophagus. Both the metaplastic part and the normal part (4 and 14 cm, respectively, above the esophago-gastric junction) were examined. At sensory thresholds the stimulation intensity, referred pain areas, and evoked brain potentials were recorded. RESULTS: Patients were hyposensitive to heat stimulation both in the metaplastic part [median stimulation time to reach the pain detection threshold: 15 (12-34) s vs 14 (6-23) s in controls; F = 4.5, P = 0.04] and the normal part of the esophagus [median 17 (6-32) s vs 13 (8-20) s in controls; F = 6.2, P = 0.02]. Furthermore, patients were hyposensitive in the metaplastic part of the esophagus to mechanical distension [median volume at moderate pain: 50 (20-50) mL vs 33 (13-50) mL in controls; F = 5.7, P = 0.02]. No indication of central nervous system abnormalities was present, as responses were comparable between groups to electrical pain stimuli in the metaplastic part [median current evoking moderate pain: 13 (6-26) mA vs 12 (9-24) mA in controls; F = 0.1, P = 0.7], and in the normal part of the esophagus [median current evoking moderate pain: 9 (6-16) mA, vs 11 (5-11) mA in controls; F = 3.4, P = 0.07]. Furthermore, no differences were seen for the referred pain areas (P-values all > 0.3) or latencies and amplitudes for the evoked brain potentials (P-values all > 0.1). CONCLUSION: Patients with BE are hyposensitive both in the metaplastic and normal part of esophagus likely as a result of abnormalities affecting peripheral nerve pathways.

Assessing proprioception:A critical review of methods

To control movement,the brain has to integrate proprioceptive information from a variety of mechanoreceptors.The role of proprioception in daily activities,exercise,and sports has been extensively investigated,using different techniques,yet the proprioceptive mechanisms underlying human movement control are still unclear.In the current work we have reviewed understanding of proprioception and the three testing methods:threshold to detection of passive motion,joint position reproduction,and active movement extent discrimination,all of which have been used for assessing proprioception.The origin of the methods,the different testing apparatus,and the procedures and protocols used in each approach are compared and discussed.Recommendations are made for choosing an appropriate technique when assessing proprioceptive mechanisms in different contexts.

Assessment of HRP2-Based RDT in Diagnosing Malaria Cases

Background： HRP2-based Rapid Diagnostic Tests （RDTs） for malaria ensure a rational use of artemisinin-based combination therapy （ACT）. The HRP2 antigen can go through the cerebrospinal fluid （CSF）. Purpose： To assess the HRP2-based RDT in malaria and detect the HRP2 in CSF. Methods： From November 2006 to May 2007, all patients under 15 years showing clinical symptoms of malaria were included. RDT was performed on the CSF and peripheral blood. Results： Out of the 951 patients included, 131 （13.7%） were confirmed through RDT and 130 （13.6%） through thick blood smear. Sensitivity and specificity stood at 96.96% and 99.71% respectively, for uncomplicated cases and at 100% and 99.13% for severe cases. Tests to detect HRP2 in the CSF of 52 cases were negative. Conclusion： Tests to detect HRP2 in the CSF could make it possible to diagnose severe malaria cases when peripheral parasitemia would be below the detection threshold.

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

The cloud-detection procedure developed by McNally and Watts(MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual(MMR) scheme for both simulated and real Advanced Infrared Sounder(AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR(Vis/NIR) cloud mask.

The Study of Image Segmentation Based on the Combination of the Wavelet Multi-scale Edge Detection and the Entropy Iterative Threshold Selection

This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.

Speech detection method based on a multi-window analysis

Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.

Reliability of weak signals detection in neurons with noise

Previous experimental and theoretical studies have demonstrated that a weak signal can be amplified and optimized by the assistance of noise. The response of the system undergoes stochastic resonance(SR) like behavior. The underlying mechanisms are fairly simple and robust. This phenomenon has been observed in a large variety of systems, including physical and biological systems. In the nervous system, the majority of synaptic input is too weak to make neurons fire a spike. This weak(or subthreshold) signals detection is very important for animal survival, and pulse detection is a simple but basic task of neuronal information transmission and processing. Some studies have found that subthreshold signals can be transmitted by the application of external noise. However, neurons are subjected to various kinds of inherent noise. Recently, theoretical work has revealed that this inherent noise improves signals detection ability. The detection ability of a single neuron is limited, and the neuronal circuit can perform this task very well by detecting the synchronization of presynaptic potentials. Here, we review recent studies of subthreshold detection by both single neurons and neuronal circuits.

A block zero-padding method based on DCFT for L1 parameter estimations in weak signal and high dynamic environments

Weak L1 signal acquisition in a high dynamic environment primarily faces a challenge： the integration peak is neg- atively influenced by the possible bit sign reversal every 20 ms and the frequency error. The block accumulating semi-coherent integration of correlations （BASIC） is a state-of-the-art method, but calculating the inter-block conjugate products restricts BASIC in a low signal-to-noise ratio （SNR） acquisition. We propose a block zero-padding method based on a discrete chirp-Fourier transform （DCFT） for parameter estimations in weak signal and high dynamic environments. Compared with the conventional receiver architecture that uses closed-loop acquisition and tracking, it is more suitable for open-loop acquisition. The proposed method combines DCFT and block zero-padding. In this way, the post-correlation signal is coherently post-integrated with the bit sequence stripped off, and the high dynamic parameters are precisely estimated using the threshold set based on a false alarm probability. In addition, the detection performance of the proposed method is analyzed. Simulation results show that compared with the BASIC method, the proposed method can precisely detect the high dynamic parameters in lower SNR when the length of the received signal is fixed.