In the case of through-the-wall localization of moving targets by ultra wideband (UWB) radars, there are applications in which handheld sensors equipped only with one transmitting and two receiving antennas are applie...
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In the case of through-the-wall localization of moving targets by ultra wideband (UWB) radars, there are applications in which handheld sensors equipped only with one transmitting and two receiving antennas are applied. Sometimes, the radar using such a small antenna array is not able to localize the target with the required accuracy. With a view to improve through-the-wall target localization, cooperative positioning based on a fusion of data retrieved from two independent radar systems can be used. In this paper, the novel method of the cooperative localization referred to as joining intersections of the ellipses is introduced. This method is based on a geometrical interpretation of target localization where the target position is estimated using a properly created cluster of the ellipse intersections representing potential positions of the target. The performance of the proposed method is compared with the direct calculation method and two alternative methods of cooperative localization using data obtained by measurements with the M-sequence UWB radars. The direct calculation method is applied for the target localization by particular radar systems. As alternative methods of cooperative localization, the arithmetic average of the target coordinates estimated by two single independent UWB radars and the Taylor series method is considered.
Neurons integrating sensory stimulus features are found in many brain areas up to cortical level, and their understanding is essential for building and improving neural prostheses. Here, we focus on auditory neural co...
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ISBN:
(纸本)9781467319690
Neurons integrating sensory stimulus features are found in many brain areas up to cortical level, and their understanding is essential for building and improving neural prostheses. Here, we focus on auditory neural coding in the inferior colliculus (IC). Much work has been conducted to identify the primary spectro-temporal sound features. However, a description of how reliable these features are encoded at IC level remains an open question. In a simplified model, the encoding process can be described by a linear integrator followed by a threshold nonlinearity that creates a binary spike sequence. To account for variability in neural responses, coding noise has to be taken into account. However, coding noise reduces the certainty about the stimulus features encoded. Traditional approaches to quantify the amount of noise based on information theory are prone to sampling bias and cannot be bounded in a simply way, making it hard to interpret the results obtained and to compare them across neural populations. Here, we reformulate neural coding as a spike detection task and show that methods from signal detection theory allow an alternative description to quantify coding noise. Using neural responses from the IC in Mongolian gerbils to acoustic stimuli, we demonstrate that this approach allows a reliable description of neural coding noise, particularly in the smalldata limit, while being highly correlated with information-theoretic quantities in the large-data regime.
This paper presents an Hidden Markov Models (HMM)-based snorer group recognition approach for Obstructive Sleep Apenea diagnosis. It models the spatio-temporal characteristics of different snorer groups belonging to d...
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ISBN:
(纸本)9781457702167
This paper presents an Hidden Markov Models (HMM)-based snorer group recognition approach for Obstructive Sleep Apenea diagnosis. It models the spatio-temporal characteristics of different snorer groups belonging to different genders and AHI severity levels. The current experiment includes selecting snore data from subjects, identifying snorer groups based on gender and AHI values (AHI < 15 and AHI > 15), detecting snore episodes, MFCC computation, training and testing HMMs. A set of multi-level classification rules is employed for incremental diagnosis of OSA. The proposed method, with a relatively smalldata set, produces results nearly comparable to any existing methods with single feature class. It classifies snore episodes with 62.0% (male), 67.0% (female) and recognizes snorer group with 78.5% accuracy. The approach makes its diagnosis decision at 85.7% (sensitivity), 71.4% (specificity) for males and 85.7% (sensitivity and specificity) for females.
small satellites with their limited computational capabilities require that software engineering techniques promote efficient use of spacecraft resources. A model-driven approach to software engineering is an excellen...
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ISBN:
(纸本)9781467318112
small satellites with their limited computational capabilities require that software engineering techniques promote efficient use of spacecraft resources. A model-driven approach to software engineering is an excellent solution to this resource maximization challenge as it facilitates visualization of the key solution processes and data elements. The software engineering process utilized for the OpenOrbiter spacecraft, which is a remote sensing technology demonstrator, is presented. Key challenges presented by the Open Orbiter project included concurrent operation and tasking of five computer-on-module (COM) units and a flight computer and the associated data marshaling between local and general storage. The payload processing system (consisting of the five COM units) would determine (based on high-level ground controller instructions) targets of interest which it then tasks to the operating software to effect image capture of. The operating software, in addition to satisfying the aforementioned tasks from the payload software, must also monitor spacecraft health, status and operations and control the activities of the various subsystems (e. g., communications, attitude determination and control, etc.). One of the principal challenges with this highly-distributed architecture is to ensure that tasks are reasonably evenly distributed between different elements of the computing units, such that a single unit is not acting as a bottleneck for other onboard computers' tasks. A work distribution model was created to characterize the system needs and facilitate high-level task-type assignment between the computing systems. A communications model was also created to characterize the level of data throughput required (based on user needs) and possible (based on several communications approaches) to the ground. This allowed selection of the most optimal communications approach and, thus, allowed maximization of each communications opportunity. This paper presents the model-b
For the efficient measurement of gravity anomaly which is used for the estimation of ground structure, compact gravity observation system using force-balance (FB) accelerometer is presented. It has a problem that the ...
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Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in th...
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ISBN:
(纸本)9781479909414;9781479909421
Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods;lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined.
Considering the problem of rearview mirror blind spot during driving, the paper studied and designed the blind spot detection system based on MMW radar. Radar was installed at an appropriate position on the detection ...
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ISBN:
(纸本)9783037856932
Considering the problem of rearview mirror blind spot during driving, the paper studied and designed the blind spot detection system based on MMW radar. Radar was installed at an appropriate position on the detection target signal by transmitting, when another car enter the detecting area, the small alarm light beside A pillar would shine or alarm few times, to remind drivers careful change road. And the effect would not effect by weather or time. For the radar sensor application environment, triangle wave LFMCW can effectively solve the speed from the coupling phenomenon. The paper showed experimental and simulation data.
In blind scene analysis, the aim is to obtain information about background and targets without any prior information Blind methods can be considered as pre-processing steps for scene understanding. By means of blind s...
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ISBN:
(纸本)9780819495341
In blind scene analysis, the aim is to obtain information about background and targets without any prior information Blind methods can be considered as pre-processing steps for scene understanding. By means of blind signal separation methodologies, anomalies can be detected and these anomalies can be exploited for target detection. There are many imaging sensor systems which uses different properties of the emittance or the reflectance characteristics of the scene components. Spectral reflectance properties are related to the material composition and these multispectral characteristics can be exploited for detection, identification and classification of the scene components. As the light scattered from the scene elements shows polarization, polarized measurements can be used as extra features. Multispectral and polarimetric images of a scene provide information to some level and this information can be used to get further information on the scene and to facilitate detection. In this study, spectral and polarimetric images of a scene are analyzed via Canonical Correlation Analysis (CCA) which is a powerful multivariate statistical methodology. Multispectral and polarimetric data (spectro-polarimetric data) are treated as two different sets. Canonical variants obtained by CCA give different scene components such as background elements and some man-made objects. The linear relationship of the polarimetric and multispectral data of the same scene is also obtained by CCA.
A small and light-weight wearable electrocardio-graph (ECG) equipment with a tri-axis accelerometer (x, y and z-axis) was developed for prolonged monitoring of everyday stress. It consists of an amplifier, a microcomp...
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ISBN:
(纸本)9781457702167
A small and light-weight wearable electrocardio-graph (ECG) equipment with a tri-axis accelerometer (x, y and z-axis) was developed for prolonged monitoring of everyday stress. It consists of an amplifier, a microcomputer with an AD converter, a triaxial accelerometer, and a memory card. Four parameters can be sampled at 1 kHz for more than 24 h and a maximum of 27 h with a default battery and a memory card of one giga byte (1 GB). Off-line dataprocessing includes motion information along three axes and autonomic nervous system (ANS) activity bispectral analysis and the tone-entropy method (T-E method) from HRV data. The availability of the system was tested through simulated office work and three-day monitoring by replacing the battery and the memory card every 24 h. Both short-term and circadian rhythms of ANS activity were clearly observed. In addition, sympathetic nervous activities gradually increased from the second to the third day. The experimental data presented verifies the functionality of the proposed system.
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