The aim of this work is the development of an improved formulation of the double threshold algorithm for sEMG onset-offset detection presented by Bonato and co-workers. The original formulation, which keeps the thresh...
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The aim of this work is the development of an improved formulation of the double threshold algorithm for sEMG onset-offset detection presented by Bonato and co-workers. The original formulation, which keeps the threshold fixed, suffers from performance degradation whenever the SNR changes during the analysis. The novel approach is designed to be adaptive to SNR changes in either burst or inter-burst zones of sEMG signals recorded in static and dynamic conditions. The detection parameters (i.e. detection and false alarm probabilities) are updated on the basis of an on-line estimation of the SNR. The proposed formulation has been assessed on both simulated and real sEMG data. For constant SNR the performance of the original formulation is confirmed (for SNR > 8 dB, bias and standard deviation less than 10 and 15 ms, respectively;detection percentage higher than 95%), while the novel implementation performs better with time-varying SNR (for SNR varying in the range 10-25 dB the standard approach detection percentage decreases at 50%). detection on signals recorded during isometric contractions at different force levels confirms the performance on simulated signals (StD = 134 ms;FP = 22%, and StD = 42 ms;FP = 2%, respectively for standard and novel implementation calculated as average on five experimental trials). The pseudo real-time detection allowed by this formulation can be profitably exploited by biofeedback applications based on myoelectric information. (C) 2012 Elsevier Ltd. All rights reserved.
Due to the ease of setting up a Wi-Fi hotspot and the availability of readily configurable mobile operating systems, Wi-Fi tethering can be abused by misbehaving users to gain an unfair advantage in throughput perform...
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Due to the ease of setting up a Wi-Fi hotspot and the availability of readily configurable mobile operating systems, Wi-Fi tethering can be abused by misbehaving users to gain an unfair advantage in throughput performance. In this paper, we study the problem of detecting selfish nodes in Wi-Fi tethering environments. In particular, selfish nodes can manipulate carrier sensing thresholds to exploit recent advances in physical-layer concurrent transmission technologies as well as short link distance in a tethered network so as to achieve unfair throughput gains while evading detection. We propose a new MAC-layer detection algorithm based on frame sequence numbers that accurately detects selfish nodes.
The paper summarizes results of research work focused on development of the voltage stability assessment method and its software implementation. Developed voltage stability detection algorithm is based on local synchr...
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Background: A VDD-implantable cardioverter-defibrillator (ICD) provides atrioventricular (AV) synchronous stimulation when necessary and incorporates the advantages of dual chamber arrhythmia discrimination algorithms...
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Background: A VDD-implantable cardioverter-defibrillator (ICD) provides atrioventricular (AV) synchronous stimulation when necessary and incorporates the advantages of dual chamber arrhythmia discrimination algorithms both at potentially lower costs and less periprocedural complications than a DDD-ICD system. A prerequisite for correct dual chamber ICD function is reliable atrial sensing. Methods: We evaluated atrial near- and ventricular far-field sensing and its impact on the dual-chamber detection algorithm in 106 patients with a single-lead VDD-ICD during a 12-month follow-up period. Results: Six hundred and thirty-nine follow-ups were included. Mean near-field amplitude was 3.82 +/- 1.76 mV;mean far-field amplitude was 0.31 +/- 0.15 mV. 46% of patients had far-fields > 0.35 mV and 35% of patients showed atrial EGM markers corresponding to a ventricular far-field in at least one follow-up. Six hundred and forty-five tachycardia episodes were evaluated. Due to far-field sensing, three of 66 episodes (4.5%) of sinus tachycardia were misclassified as ventricular tachycardia (VT), leading to antitachycardia therapies. Delayed detection of VT was seen in a 12 of 323 episodes (3.7%) in five of 62 patients (8%) having VT events (delay 6.4 +/- 6.0 seconds (range 2-24 seconds)). Stable far-field amplitudes < 0.2 mV in a follow-up had a high negative predictive value for the occurrence of malfunction during tachycardia-conversely, high far-field amplitudes or a high incidence of far-field markers are only moderately correlated with malfunction. Conclusions: Ventricular far-field sensing in a VDD-ICD is not uncommon, however, tachycardia detection by the dual chamber algorithm is not seriously impaired by far-field sensing.
This paper mainly introduces the detection method and calculation method of load flicker signal. Firstly, the commonly used detection methods are theoretically analyzed, their advantages and disadvantages are pointed ...
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This paper mainly introduces the detection method and calculation method of load flicker signal. Firstly, the commonly used detection methods are theoretically analyzed, their advantages and disadvantages are pointed out, and a fundamental effective value detection method is proposed. Then the flicker calculation method is studied. The traditional flicker calculation method is complex and needs the design of some filters. Based on the traditional algorithm, the flicker calculation method is improved, and the accuracy and rapidity of the algorithm are verified by simulation.
Heart attack is a life threaten cardiac disease. Although advances in information and communication technologies have brought us tiny embedded electrocardiogram (ECG) devices and efficient algorithms to continuously m...
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Heart attack is a life threaten cardiac disease. Although advances in information and communication technologies have brought us tiny embedded electrocardiogram (ECG) devices and efficient algorithms to continuously monitor and analyze ECG signals, energy consumption of the system is a critical concern for practical usage. However, there have been not many studies focusing on energy consumption of mobile ECG monitoring, especially lacking guidelines in selecting proper data communication strategies and computational algorithms. In this paper, we examine three possible modes of data transmissions for energy consumption. A generic energy consumption model and a simulated environment were developed to examine the influence of different communication strategies and ventricular fibrillation detection methods in terms of computational efficiency and power consumption. This paper shows that the total ratio of energy saving between mode transmitting all data and our proposed event transmission is up to 84.67% per 508 s of data segment.
Purpose: Myocardial perfusion imaging (MPI) is used for diagnosis and prognosis of coronary artery disease. When MPI studies are performed with positron emission tomography (PET) and the radioactive tracer rubidium-82...
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Purpose: Myocardial perfusion imaging (MPI) is used for diagnosis and prognosis of coronary artery disease. When MPI studies are performed with positron emission tomography (PET) and the radioactive tracer rubidium-82 chloride (Rb-82), a small but non-negligible fraction of studies (similar to 10%) suffer from extracardiac interference: high levels of tracer uptake in structures adjacent to the heart which mask the true cardiac tracer uptake. At present, there are no clinically available options for automated detection or correction of this problem. This work presents an algorithm that detects and classifies the severity of extracardiac interference in Rb-82 PET MPI images and reports the accuracy and failure rate of the method. Methods: A set of 200 Rb-82 PET MPI images were reviewed by a trained nuclear cardiologist and interference severity reported on a four-class scale, from absent to severe. An automated algorithm was developed that compares uptake at the external border of the myocardium to three thresholds, separating the four interference severity classes. A minimum area of interference was required, and the search region was limited to that facing the stomach wall and spleen. Maximizing concordance (Cohen's Kappa) and minimizing failure rate for the set of 200 clinician-read images were used to find the optimal population-based constants defining search limit and minimum area parameters and the thresholds for the algorithm. Tenfold stratified cross-validation was used to find optimal thresholds and report accuracy measures (sensitivity, specificity, and Kappa). Results: The algorithm was capable of detecting interference with a mean [95% confidence interval] sensitivity/specificity/Kappa of 0.97 [0.94, 1.00]/0.82 [0.66, 0.98]/0.79 [0.65, 0.92], and a failure rate of 1.0% +/- 0.2%. The four-class overall Kappa was 0.72 [0.64, 0.81]. Separation of mild versus moderate-or-greater interference was performed with good accuracy (sensitivity/specificity/Kappa = 0
The retinopathy diseases occur when the neurons do not transmit signals from retina to the brain. These disorders are: Diabetic retinopathy, hypertensive retinopathy, macular degeneration, vein branch occlusion, vitre...
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The retinopathy diseases occur when the neurons do not transmit signals from retina to the brain. These disorders are: Diabetic retinopathy, hypertensive retinopathy, macular degeneration, vein branch occlusion, vitreous hemorrhage, and normal retina. This work presents a novel detection algorithm about retinopathy disorders from retina images. For this purpose, the retina images were pre-processed and resized at first. Then the discrete cosine transform was used as feature extraction before applying a neural network classifier. The performance of recognition rates of the novel detection algorithm were found as 50%, 70%, 85%, 90%. and 95% for testing five retinopathy cases respectively.
The research project “International Forest Fire Fighting” (iWBB) was funded by the Minister for Economic Affairs and Energy of the State of North Rhine-Westphalia, Germany. A group of companies, research institutes ...
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The research project “International Forest Fire Fighting” (iWBB) was funded by the Minister for Economic Affairs and Energy of the State of North Rhine-Westphalia, Germany. A group of companies, research institutes and universities have been working together to develop an integrated, but modular system. An integrated approach for early forest fire detection and suppression is based on an adequate combination of different detection systems depending on wildfire risk, the size of the area and human presence affiliated with an adequate logistical infrastructure, training by simulation, and innovative extinguishing technology. As in the case of wildfires large areas have to be monitored only remote sensing technologies (e.g. video based systems) are able to perform early detection adequately. To reduce false alarms a remote controlled unmanned aerial vehicle (UAV) equipped with gas sensors and a thermal camera flies to a potential fire to specify the origin of the reported cloud. The UAV can also be used as a scout for fire fighters. After successful fire extinction an unmanned blimp can be used as a fireguard to reduce the risk of re-ignition of the fire. As monitoring tools, a microwave radiometer detecting hot spots also at insufficient vision (due to smoke clouds and below the ground surface), gas and smoke sensors and a thermal camera are mounted on the blimp. The benefit of a blimp is a higher payload. This paper presents an investigation of an early forest fire detection system on the basis of indoor (performed in the fire lab of the University of Duisburg-Essen) and outdoor tests. A commercial highly sensitive aspirating smoke detector, two gas sensors (H2 and CXHX), a microwave radiometer and the detection algorithms are described. A general overview about the project and the carrier platforms is presented.
To construct safe workspaces or daily life environments, it is important to develop a technology for automatically recording dangerous environmental situations. Based on increased finger skin conductance and decreased...
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To construct safe workspaces or daily life environments, it is important to develop a technology for automatically recording dangerous environmental situations. Based on increased finger skin conductance and decreased instantaneous heart (pulse) rate, we developed an algorithm for detecting the startle state of humans when encountering dangerous situations. The parameter values in the algorithm were optimized by tuning them with experimental results obtained in a virtual reality system, in which dangerous scenarios were presented to 21 subjects. The detection sensitivity of the optimized algorithm was 100% during a static standing condition and also while cycling. The detection specificity attained 90% and 87% for the static and exercising conditions, respectively. The optimized algorithm was applied to a real world situation such as car driving, and relatively high sensitivity of 87% and specificity of 80% were obtained. (c) 2006 Elsevier Ltd. All rights reserved.
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