Time-course of "off-line" prefrontal rTMS effects--a PET study.

Low-frequency "off-line" repetitive transcranial magnetic stimulation (rTMS) over the course of several minutes has attained considerable attention as a research tool in cognitive neuroscience due to its ability to induce functional disruptions of brain areas. This disruptive rTMS effect is highly valuable for revealing a causal relationship between brain and behavior. However, its influence on remote interconnected areas and, more importantly, the duration of the induced neurophysiological effects, remain unknown. These aspects are critical for a study design in the context of cognitive neuroscience. In order to investigate these issues, 12 healthy male subjects underwent 8 H(2)(15)O positron emission tomography (PET) scans after application of long-train low-frequency rTMS to the right dorsolateral prefrontal cortex (DLPFC). Immediately after the stimulation train, regional cerebral blood flow (rCBF) increases were present under the stimulation site as well as in other prefrontal cortical areas, including the ventrolateral prefrontal cortex (VLPFC) ipsilateral to the stimulation site. The mean increases in rCBF returned to baseline within 9 min. The duration of this unilateral prefrontal rTMS effect on rCBF is of particular interest to those who aim to influence behavior in cognitive paradigms that use an "off-line" approach.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

Teleconsultoria Off-line: uma arquitetura distribuída e compartilhada para sistemas heterogêneos

The Telehealth Brazil Networks Program, created in 2007 with the aim of strengthening primary care and the unified health system (SUS - Sistema Único de Saúde), uses information and communication technologies for distance learning activities related to health. The use of technology enables the interaction between health professionals and / or their patients, furthering the ability of Family Health Teams (FHT). The program is grounded in law, which determines a number of technologies, protocols and processes which guide the work of Telehealth nucleus in the provision of services to the population. Among these services is teleconsulting, which is registered consultation and held between workers, professionals and managers of healthcare through bidirectional telecommunication instruments, in order to answer questions about clinical procedures, health actions and questions on the dossier of work. With the expansion of the program in 2011, was possible to detect problems and challenges that cover virtually all nucleus at different scales for each region. Among these problems can list the heterogeneity of platforms, especially teleconsulting, and low internet coverage in the municipalities, mainly in the interior cities of Brazil. From this perspective, the aim of this paper is to propose a distributed architecture, using mobile computing to enable the sending of teleconsultation. This architecture works offline, so that when internet connection data will be synchronized with the server. This data will travel on compressed to reduce the need for high transmission rates. Any Telehealth Nucleus can use this architecture, through an external service, which will be coupled through a communication interface.

DEVELOPMENT OF AN OFF-LINE RAINFLOW COUNTING ALGORITHM WITH TEMPORAL PARAMETERS AND DATA REDUCTION

Rainflow counting methods convert a complex load time history into a set of load reversals for use in fatigue damage modeling. Rainflow counting methods were originally developed to assess fatigue damage associated with mechanical cycling where creep of the material under load was not considered to be a significant contributor to failure. However, creep is a significant factor in some cyclic loading cases such as solder interconnects under temperature cycling. In this case, fatigue life models require the dwell time to account for stress relaxation and creep. This study develops a new version of the multi-parameter rainflow counting algorithm that provides a range-based dwell time estimation for use with time-dependent fatigue damage models. To show the applicability, the method is used to calculate the life of solder joints under a complex thermal cycling regime and is verified by experimental testing. An additional algorithm is developed in this study to provide data reduction in the results of the rainflow counting. This algorithm uses a damage model and a statistical test to determine which of the resultant cycles are statistically insignificant to a given confidence level. This makes the resulting data file to be smaller, and for a simplified load history to be reconstructed.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

基于表面肌电信号的人体运动意图识别方法研究

提出了基于表面肌电信号进行人体运动意图识别的新方法。该方法将智能发育思想结合其中,分为离线知识库建立和在线知识库检索两个过程。首先,进行离线训练,建立IHDR(Incremental Hierarchical Discriminant Regression)知识库。采集人体表面肌电信号,对肌电信号进行小波分解,将各层小波系数的方差作为每个通道信号的特征;然后,将每个通道的特征作为输入,人体运动行为作为输出,离线建立基于智能发育思想的IHDR知识库。人体运动意图识别的过程即知识库的检索运用过程,通过每个通道的特征来在线检索IHDR知识库,搜索到合适的节点即为运动意图。实验表明,基于智能发育思想的人体运动意图识别方法可以达到满意的识别率。

Is it possible to make pixel-based radar image classification user-friendly?

In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.