GPU implementation of RX detection using spectral derivative features

Hyperspectral image (HSI), which can record abundance information of a pixel, has shown huge potential on many applications such as image classification, target and anomaly detection and so on. Nowadays, anomaly detection has attracted more attention because there is no limitation of spectral library. A standard approach for anomaly detection is the method developed by Reed and Xiaoli, called RX algorithm. However, the data volume is getting bigger with the developing of imaging technology. A problem that ensues is the rapid increase of computation complexity and this will lead a time-consumed application. In addition, there will be noise in HSI with the influence of illumination and atmospheric. In this paper, we realize an implementation of RX algorithm on NVIDIA GeForce 1060 GPU with the utilization of derivative features. On one hand, the GPU parallel implementation reach the purpose of real-time processing and it also eliminates the storage burden of on-board processing. On the other hand, the derivative features have better performance on salient features detection and noise restraint. Thus, it can further promote the detection performance of RXD. In our experiments, three real HSI datasets were tested to verify the effect of GPU parallel implementation. The experiment results had indicated that the utilization of derivative features can promote the detection performance. Compared with serial computation, the parallel implementation achieves a great reduction on processing time.     

Automated Design and Programming of a Microfluidic DNA Computer

Previously, we described ways to implement the functions AND and OR in a DNA computer consisting of microreactors with attached heating elements that control annealing of DNA. Based on these findings, we have devised a similar device that can solve a satisfiability problem in any form. The device occupies linear space and operates in quadratic time, while a previously described competing device is built in quadratic space and operates in quadratic time or greater. Reducing the number of reactors in a DNA computer reduces the loss of DNA through binding to the surfaces of the system.     

Simultaneous correlation of complementary set of sequences using a transpose generation approach

Complementary sets of sequences are currently being applied to signal coding, radar, and multi-user systems, among others. Their particular mathematical properties make them adequate for multi-emission and noisy environments. Nowadays sustained efforts are being devoted to reduce the calculations involved in the generation and/or correlation of these signals by means of recursive algorithms. Some authors have proposed efficient algorithms that are based on modular architectures made up of adders, multipliers and delays. This work introduces a new approach to correlation algorithms of complementary sets of sequences, which is based on a transposition of the generation process. This approach allows to notoriously reduce calculations, and enables the simultaneous correlation of M sequences, without adopting time multiplexing schemes or complex parallel implementations. The correlation algorithm is theoretically demonstrated and its calculation performance is evaluated in a hardware reconfigurable platform. A comparison with other algorithms is included, considering the amount of calculations as a function of the length of the sequences.     

基于MSP430的大面积电阻式触摸屏信号检测电路的设计

对大面积电阻式触摸屏信号检测电路进行硬件设计和软件编程,实现大面积电阻式触摸屏作为输入装置,通过电阻屏控制芯片ADS7846,MSP430单片机为核心控制器,实现数据的接收、处理和发送功能,通过上位机MATLab工具GUI中一个友好界面显示其功能.硬件设计包括大面积电阻触摸屏、触摸屏控制器和单片机之间的连接、计算机接口设计、单片机与上位机通信设计;软件设计主要包括电阻屏线性校正、单片机对电阻屏控制器的编程设计、单片机与上位机通信编程设计和上位机软件编程设计.通过测试,系统具有较好的应用效果.     

Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model

We present a new formulation to derive evaporative fraction (EF) and evapotranspiration (ET) maps from remotely sensed data without auxiliary relationships or site-specific relationships. This formulation is based on Granger's complementary relationship and Priestley-Taylor's equation. The proposed model eliminates the wind function and resistance parameters commonly applied to ET calculation by including a relative evaporation parameter (ET/Epot). By combining this relative evaporation parameter, Granger's complementary relationship and Priestley-Taylor equation, we obtain a simple equation to estimate ET. We tested and validated the proposed formulation over the Southern Great Plains (SGP) region of the United States for seven clear sky days during March-October 2003. MODIS Atmospheric and Land products were the only source of data used in this study. Estimates of ET show an overall root mean square error and bias of 33.89 and − 10.96 Wm^− 2, respectively. Our results suggest that the proposed approach is robust and valid for a wide range of atmospheric and surface conditions.     

Lidar detection of underwater objects using a neuro-SVM-based architecture

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.     

Multiresolution feature detection using a family of isotropicbandpass filters

Presents a novel method which uses a special type of multi-scale isotropic band-pass filters to detect image landmark and corner features. This paper mainly contributes to the solutions of the following problems: (1) defining a general family of feature detectors under a unified framework that are able to enhance and detect the desired features; (2) explaining theoretically and experimentally why these feature points can be detected by the proposed detectors; and (3) extending the detectors to multi-scale versions for jointly achieving good detectability and localization where an automatic scale selection method is applied. The paper then presents several applications for detecting landmarks and corners using the proposed methods, in order to illustrate their usage. These include detecting landmarks from gesture images (of the face and hand), from airborne and vehicle-borne IR landmine images, and from images containing object corners. Experiments have been performed using the proposed detectors to these applications. Some comparisons and evaluations have also been performed. The results have demonstrated the effectiveness of the proposed detectors in terms of feature detectability and localization.     

Comparison of edge detection algorithms using a structure frommotion task

This paper presents an evaluation of edge detector performance. We use the task of structure from motion (SFM) as a "black box" through which to evaluate the performance of edge detection algorithms. Edge detector goodness is measured by how accurately the SFM could recover the known structure and motion from the edge detection of the image sequences. We use a variety of real image sequences with ground truth to evaluate eight different edge detectors from the literature. Our results suggest that ratings of edge detector performance based on pixel-level metrics and on the SFM are well correlated and that detectors such as the Canny detector and Heitger detector offer the best performance.     

On-line detection of toxic components using a microbial fuel cell-based biosensor

Safe drinking water without toxic chemicals is crucial for people's health. A recently developed sensor for the detection of toxic components in water is the microbial fuel cell (MFC)-based biosensor. In this biosensor, substrate consumption rate and metabolic activity of bacteria are directly related to the electric current. A reduction in current under otherwise similar conditions is an indication of toxic inhibition. Under steady state conditions, current can be described by the Butler–Volmer–Monod (BVM) model. Knowing which parameters of this model change under toxic contamination can give an indication on the type of toxicity. The model requires that the substrate concentration is known. It is shown in this paper that is not possible to estimate both the substrate concentration as well as the BVM parameters on-line from current data at constant overpotential. However, it appears that substrate concentration and substrate consumption rate can be estimated on-line, and that after a linear reparametrization the BVM parameters can be estimated by ordinary least-squares techniques from a polarization curve that is generated as soon as a suspect change in current occurs. Analysis shows that a weighted least-squares method is necessary to secure a good fit at the overpotentials where current is most sensitive to changes in kinetic parameters. A protocol for on-line detection of toxicity and for detection of the type of kinetic inhibition is provided.     

Simple and flexible detection of contiguous repeats using a suffix tree

We study the problem of detecting all occurrences of (primitive) tandem repeats and tandem arrays in a string. We first give a simple time- and space-optimal algorithm to find all tandem repeats, and then modify it to become a time and space-optimal algorithm for finding only the primitive tandem repeats. Both of these algorithms are then extended to handle tandem arrays. The contribution of this paper is both pedagogical and practical, giving simple algorithms and implementations based on a suffix tree, using only standard tree traversal techniques.     

Computer simulation of cardiac rhythm and artificial pacemakers using a ten-element heart model

This paper describes a universal simulation system which enables simulation models of cardiac rhythm disturbances and of artificial pacemaker actions to be constructed. Three types of heart element are introduced according to the way in which excitation impulses are propagated or produced. Cycle rate dependence of repolarization periods and of impulse transmission speeds is incorporated. Random and predetermined deviations from natural behavior are also allowed for in the model. Using the universal system, a model of simple configuration has been developed. There are five physiological and five pathological heart components joined together by uni- and bidirectional connectors. Additional bidirectional channels allow a variety of pacemakers to be "implanted" in the simulated heart.     

Remote optical detection of biomass burning using a potassium emission signature

A remotely detectable signature for biomass burning that is specific to flaming combustion is found in the strong emission lines of potassium (K) at 766.5 nm and 769.9 nm. Ground level spectra of a test fire illustrate the high contrast signal provided by K emission. Image data collected at high altitude using the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and analysed for K emission vividly displays the fire fronts of a 1995 fire in Brazil. Sensors for K emission can use silicon detector technology for advantages in high sensitivity, low cost, wide area coverage and fine spatial resolution.     

Early detection of heart diseases using a low-cost compact ECG sensor

Multimedia Tools and Applications - Heart disease patients are continuously increasing. The patients face the problem of a delayed diagnosis as the subjects do not undergo routine tests and consult...     

Performance and touch characteristics of disabled and non-disabled participants during a reciprocal tapping task using touch screen technology

Touch screens are becoming more prevalent in everyday environments. Therefore, it is important that this technology is accessible to those with varying disabilities. The objective of the current study was to evaluate performance and touch characteristics (forces, impulses, and dwell times) of individuals with and without a movement disorder during a reciprocal tapping touch screen task. Thirty-seven participants with a motor control disability and 15 non-disabled participants participated. Outcome measures include number of correct taps, dwell time, exerted force, and impulse. Results indicate non-disabled participants had 1.8 more taps than participants with fine motor control disabilities and 2.8 times more than those with gross motor impairments (p<0.05). Additionally, people with gross motor control disabilities demonstrated longer dwell times and greater impulses (p<0.05). The average force used to activate the buttons was 6.2 N, although the button activation force was 0.98 N. Differences in reciprocal tapping and touch characteristics exist between those with and without motor control disabilities. Understanding how people (including those with disabilities) interact with touch screens may allow designers and engineers to ultimately improve usability of touch screen technology.     

The effects of using a single display screen versus dual screens on neck-shoulder muscle activity during computer tasks

The present study compared the effects of using one versus two display screens on cervical muscle activity of computer users. Healthy pain-free university students were recruited (11 males and 11 females), and surface electromyography in bilateral cervical erector spinae and upper trapezius (UT) muscles was measured. Each subject performed standardized text editing tasks for 15 min using a single screen and dual screens in a randomized order. In the dual screen condition, the primary screen was placed directly in front while the secondary screen was angled to the right of the user. Significant reductions of the 50th and 90th percentile amplitudes, representative of dynamic muscle loading, were found in the right UT muscle for dual screen condition. The 10th percentile muscle activity was similar in all muscles in the two conditions. These results suggest that viewing dual screens may be associated with different postural muscle activity compared to single screen.     

A DNA methylation assay for detection of ovarian cancer cells using a HpaII/MspI digestion-based PCR assay in an integrated microfluidic system

Early and accurate diagnosis of cancer plays a very important role in favorable clinical outcomes. DNA methylation of tumor suppressor genes has been recognized as a diagnostic biomarker for early carcinogenesis. The presence of 5-methylcytosine in the CpG islands in the promoter region of a tumor suppressor gene is an important indicator of DNA methylation. However, the standard detection assay utilizing a bisulfite treatment and HpaII/MspI endonuclease digestion is a tedious and lengthy process and requires a relatively large amount of DNA for testing. In this study, the methylated DNAs of various tumor suppressor genes, HAAO, HOXA9 and SFRP5, were chosen as candidates for detection of ovarian cancer cells. The entire experimental process for the DNA methylation assay, including target DNA isolation, HpaII/MspI endonuclease digestion, and nucleic acid amplification has been realized in an integrated microfluidic system. The limit of detection using this developed system has been experimentally determined to be 10² cells/reaction. The entire process from sample loading to analysis of the results only took 3 h which is much faster than the existing protocols. Different sources of biosamples, such as cells, ascites and serums, could be detected with the methylated DNA, indicating that this developed microfluidic system could be adapted for clinical use. Thus, this developed microsystem may be a promising platform for the rapid and early diagnosis of cancers.     

A metric for rating the effectiveness of industrial automation systems using a derivative of function point analysis

This paper describes the on-going design of a factory automation performance and satisfaction metric that is based on variations to the function point analysis (FPA) algorithm used in software engineering and analytical hierarchy process methodology common to systems engineering. The paper asserts that a satisfaction rating can be obtained for completed automation projects based on this tautology. The metric uses five high-level functional ratings, which are subdivided according to complexity criteria. The value of the metric is modified by calculating the effect of 14 appropriate adjustment factors. A level of configurability is added by weighting these factors to suit any specific installation. The scores and configuration data are elicited by averaging results obtained from a multi-part survey administered to four or five company employees selected from a wide range of job titles and responsibilities. A simple implementation of the algorithm was completed and an extension of the work will provide an Internet-based version and a diagnostic tool for company use.     

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

Observer-based robust fault detection of multirate linear system using a lift reformulation

This paper presents observer-based robust fault detection for multirate sampled-data linear system. It is assumed that all plant inputs are updated with a unique sampling period and that the plant outputs are sampled uniformly at integer multiples of the input sampling period. A state observer is constructed, as in the single-rate case, and a residual generator is derived. The paper shows that the residual signals can be decoupled from noise and disturbances. An illustrative numerical example is provided.     

Robust fault detection and isolation of time-delay systems using a geometric approach

This paper investigates the development of Fault Detection and Isolation (FDI) filters for both retarded and neutral time-delay systems with unknown time-varying delays. Using a geometric framework, the notion of a finite unobservability subspace is introduced for time-delay systems and an algorithm for its construction is presented. A bank of residual generators is then designed so that each residual is affected by one fault and is partially decoupled from the others while the H_∞ norm of the transfer function between the disturbances and the uncertainties in delays and the residuals are guaranteed to remain less than a prescribed value. Furthermore, it is shown that in the case of known delays it is possible to generate residuals that enjoy perfect decoupling properties among faults. Simulation results presented demonstrate the effectiveness of our proposed FDI algorithms.