Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted...
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ISBN:
(纸本)9789897582950
Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. the presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using patternrecognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are presented.
Face recognition is a popular technique that uses image processing to identify people's faces. Face recognition is becoming momentous due to the growing populace, which necessitates high security and monitoring sy...
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the paper presents a possibility to determine the electromagnetic pattern for the reinforcement electromotor, for the opening and closing electromagnets of the operating mechanism for a medium voltage circuit breaker ...
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ISBN:
(纸本)9781728107509
the paper presents a possibility to determine the electromagnetic pattern for the reinforcement electromotor, for the opening and closing electromagnets of the operating mechanism for a medium voltage circuit breaker using an electromagnetic field transducer.
the proceedings contain 72 papers. the topics discussed include: ultrafast patternrecognition and tracking in FLIR imagery;embodied cognition and gesture-based learning;real-time stereo matching for depth estimation ...
ISBN:
(纸本)9781467382700
the proceedings contain 72 papers. the topics discussed include: ultrafast patternrecognition and tracking in FLIR imagery;embodied cognition and gesture-based learning;real-time stereo matching for depth estimation using GPU;collecting field data in an environment combining APP and fusion tables;a mobile application for an ecological campus navigation system using augmented reality;intelligent evacuation system integrated with image recognition technology;performance improvement on duplicate address detection in IP mobility protocols;non-commutative path planning strategy;locality-aware P2P networks based on cloud-assistance;an intelligent classroom management system based on wireless sensor networks;a fuzzy-based dynamic channel allocation scheme in cognitive radio networks;and offline tracking system for deep sea going vessels using GPS and GPRS.
this book constitutes the refereed proceedings of the 8thinternationalconference on Intelligent Data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral present...
ISBN:
(数字)9783642039157
ISBN:
(纸本)9783642039140
this book constitutes the refereed proceedings of the 8thinternationalconference on Intelligent Data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and patternrecognition, clustering, applications, modeling, and interactive dynamic data visualization.
As different posture has different projection histogram pattern,. the projection histogram can be used as one of the features to discriminate different postures. In this paper, a new method using projection histogram ...
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ISBN:
(纸本)9780780397361
As different posture has different projection histogram pattern,. the projection histogram can be used as one of the features to discriminate different postures. In this paper, a new method using projection histogram for static human posture recognition is proposed. It comprises of three key modules: background subtraction, projection histogram computing and template matching. Comparing with many other methods, our approach is fast, simple and less sensitive to noise. Using our new method, a system is implemented and tested with ten static postures. It can automatically recognize them with high percentage of right decisions.
the task of faults localization is discussed in a model-free setting. As a tool for its solution we consider a multiclass patternrecognition problem with a metric in the label space. then, this problem is approximate...
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ISBN:
(纸本)3540357483
the task of faults localization is discussed in a model-free setting. As a tool for its solution we consider a multiclass patternrecognition problem with a metric in the label space. then, this problem is approximately solved, providing hints on selecting appropriate RBF nets. It was shown that the approximate solution is the exact one in several important cases. Finally, we propose the algorithm for learning the proposed RBF net. the results of its testing are briefly reported.
Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-s...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-server (or cloud). Data compression is applied to the intermediate tensor from the DNN that needs to be transmitted, addressing the challenge of optimizing the rate-accuracy-complexity trade-off. Existing split-computing approaches adopt ML-based data compression, but require that the parameters of either the entire DNN model, or a significant portion of it, be retrained for different compression levels. this incurs a high computational and storage burden: training a full DNN model from scratch is computationally demanding, maintaining multiple copies of the DNN parameters increases storage requirements, and switching the full set of weights during inference increases memory bandwidth. In this paper, we present an approach that addresses all these challenges. It involves the systematic design and training of bottleneck units - simple, low-cost neural networks - that can be inserted at the point of split. Our approach is remarkably lightweight, both during training and inference, highly effective and achieves excellent rate-distortion performance at a small fraction of the compute and storage overhead compared to existing methods.
Crossbar-enabled analog computing-in-memory (CACIM) systems can significantly improve the computation speed and energy efficiency of deep neural networks (DNNs). However, an important issue is that the performance of ...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Crossbar-enabled analog computing-in-memory (CACIM) systems can significantly improve the computation speed and energy efficiency of deep neural networks (DNNs). However, an important issue is that the performance of DNNs degrades severely when deploying the DNNs onto the CACIM systems. Because the devices in the CACIM systems have low precision to present the weights, which is caused by the intrinsic variation and high programming overhead. the computational paradigms of the CACIM systems and the digital systems are essentially different. One of the main differences is that the weights are expressed in analog terms, and it has no encoding and decoding process during the computation. We can take advantage of the characteristic of data presentation to get better performance in limited data precision. A generalized quantization method that does not constrain the range of quanta and can obtain less quantization error will be effective in the CACIM systems. For the first time, we introduced a generalized quantization method into CACIM systems and showed superior performance on a series of computer vision tasks, such as image classification, object detection, and semantic segmentation. Using the generalized quantization method, the DNN with8-level analog weights can outperform the 32-bit networks. With fewer levels, the generalized quantization method can obtain less accuracy loss than other uniform quantization methods.
We investigate the reaction-diffusion system of the classical Bazykin model in spatial two dimensional domain. In this paper, we derive the conditions for turing instability in detail and obtain the turing space, in w...
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