Emphysema is a chronic lung disease that causes breathlessness. HRCT is the reliable way of visual demonstration of emphysema in patients. The fact that dangerous and widespread nature of the disease require immediate...
详细信息
ISBN:
(纸本)9781467379120
Emphysema is a chronic lung disease that causes breathlessness. HRCT is the reliable way of visual demonstration of emphysema in patients. The fact that dangerous and widespread nature of the disease require immediate attention of a doctor with a good degree of specialized anatomical knowledge. This necessitates the development of computer-based automatic identification system. This study aims to investigate the deep learning solution for discriminating emphysema subtypes by using raw pixels of input HRCT images of lung. Convolutional Neural Network (CNN) is used as the deep learning method for experiments carried out in the Caffe deep learning framework. As a result, promising percentage of accuracy is obtained besides low processing time.
This paper aims to present a universal mask that enables all adult patients to use it. This mask has a universal size and head gear. It has many options; from among we list the embedded SpO2 and EEG electrode connecti...
详细信息
This paper aims to present a universal mask that enables all adult patients to use it. This mask has a universal size and head gear. It has many options; from among we list the embedded SpO2 and EEG electrode connections. This mask enables also the input of oxygen, nebulizer, air CPAP and medication. A mechanical switch is used to select between air CPAP and nebulizer.
Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and ...
详细信息
Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+}...
详细信息
ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+})$ , into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive “failuresy” through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding $\text { TLA }^{+}$ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in $\text { TLA }^{+}$ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudi
An analysis of the signaling systems used in the Intelligent Communication Network has been carried out. The main probabilistic characteristics of signal information delay are determined. A method for calculating thes...
详细信息
This volume contains a selection of revised papers that were presented at the Software Aspects of Robotic Systems, SARS 2011 Workshop and the Machine Learning for System Construction, MLSC 2011 Workshop, held during O...
详细信息
ISBN:
(数字)9783642347818
ISBN:
(纸本)9783642347801
This volume contains a selection of revised papers that were presented at the Software Aspects of Robotic Systems, SARS 2011 Workshop and the Machine Learning for System Construction, MLSC 2011 Workshop, held during October 17-18 in Vienna, Austria, under the auspices of the International Symposium Series on Leveraging Applications of Formal Methods, Verification, and Validation, ISoLA. The topics covered by the papers of the SARS and the MLSC workshop demonstrate the breadth and the richness of the respective fields of the two workshops stretching from robot programming to languages and compilation techniques, to real-time and fault tolerance, to dependability, software architectures, computer vision, cognitive robotics, multi-robot-coordination, and simulation to bio-inspired algorithms, and from machine learning for anomaly detection, to model construction in software product lines to classification of web service interfaces. In addition the SARS workshop hosted a special session on the recently launched KOROS project on collaborating robot systems that is borne by a consortium of researchers of the faculties of architecture and planning, computer science, electrical engineering and information technology, and mechanical and industrial engineering at the Vienna University of Technology. The four papers devoted to this session highlight important research directions pursued in this interdisciplinary research project.
the paper proposes the use of 3D convolutional neural network for recognizing user emotions on videos in a recommender system. The data approach aims to use the recognized emotion as important implicit feedback and im...
the paper proposes the use of 3D convolutional neural network for recognizing user emotions on videos in a recommender system. The data approach aims to use the recognized emotion as important implicit feedback and improve the recommendation result. This is expected to significantly improve the performance of the recommender system.
Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from...
详细信息
Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from very large real world database. These databases contain potential gold mine of valuable information, but it is beyond human ability to analyze massive amount of data and elicit meaningful patterns by using conventional techniques. In this study, DNA sequence was analyzed to locate promoter which is a regulatory region of DNA located upstream of a gene, providing a control point for regulated gene transcription. In this study, some supervised learning algorithms such as artificial neural network (ANN), RULES-3 and newly developed keREM rule induction algorithm were used to analyse to DNA sequence. In the experiments different option of keREM, RULES-3 and ANN were used, and according to the empirical comparisons, the algorithms appeared to be comparable to well-known algorithms in terms of the accuracy of the extracted rule in classifying unseen data.
Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptom...
详细信息
Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.
暂无评论