The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such t...
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ISBN:
(纸本)9789531842358
The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machinelearning, patternrecognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machinelearning solutions and other gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based s
Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who ass...
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ISBN:
(纸本)9781538692097
Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who assess each situation and prescribe appropriate exercises. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions, and therefore literature on this topic is still in its infancy. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns including walking with crutches with various levels of weight-bearing, walking with different frames, limping and walking normally. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. A gait detection accuracy of 94.9% was achieved among nine different gait classes, as these were labeled by professional physiotherapists.
Unlike roads, shipping lanes are not carved in stone. Their size, boundaries and content vary over space and time, under the influence of trade and carrier patterns, but also infrastructure investments, climate change...
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ISBN:
(纸本)9783319735214;9783319735207
Unlike roads, shipping lanes are not carved in stone. Their size, boundaries and content vary over space and time, under the influence of trade and carrier patterns, but also infrastructure investments, climate change, political developments and other complex events. Today we only have a vague understanding of the specific routes vessels follow when travelling between ports, which is an essential metric for calculating any valid maritime statistics and indicators (e.g. trade indicators, emissions and others). Whilst in the past though, maritime surveillance had suffered from a lack of data, current tracking technology has transformed the problem into one of an overabundance of information, as huge amounts of vessel tracking data are slowly becoming available, mostly due to the Automatic Identification System (AIS). Due to the volume of this data, traditional datamining and machinelearning approaches are challenged when called upon to decipher the complexity of these environments. In this work, our aim is to transform billions of records of spatiotemporal (AIS) data into information for understanding the patterns of global trade by adopting distributed processing approaches. We describe a four-step approach, which is based on the MapReduce paradigm, and demonstrate its validity in real world conditions.
Ultrasound testing is a popular technique to find some hidden rail damages. In this paper we focus on the modern Russian railway flaw detectors, such as AVICON-14, which produce the results of ultrasound testing in th...
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Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machinelearning need a supportive process model to ...
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ISBN:
(纸本)9781538662564
Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machinelearning need a supportive process model to guide the developers and stakeholders during the development process. One major problem type of machinelearning is anomaly detection. Its goal is to identify anomalous data points (outlier) between the normal data instances. Anom- aly detection has a wide scope of applications in industrial and scienti c areas. Detecting intruders in computer networks, distin- guishing between cancerous and healthy tissue in medical images, cleaning data from disturbing outliers for further evaluation and many more. The cross-industry standard process for datamining (CRISP-DM) has been developed to support developers with all kinds of datamining applications. It describes a generic model of six phases that covers the whole development cycle. The generality of the CRISP-DM model is as much a strength as it is a weakness, since the particularities of di erent problem types like anomaly detection can not be addressed without making the model overly complex. There is a need for a more practical, specialised process model for anomaly detection applications. We demonstrate this issue and outline an approach towards a practical process model tailored to the development of anomaly detection systems.
We present a simple yet effective LstM-based approach for recognizing machine-print text from raw pixels. We use a fully-connected feed-forward neural network for feature extraction over a sliding window, the output o...
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ISBN:
(纸本)9781509066285
We present a simple yet effective LstM-based approach for recognizing machine-print text from raw pixels. We use a fully-connected feed-forward neural network for feature extraction over a sliding window, the output of which is directly fed into a stacked bi-directional LstM. We train the network using the CTC objective function and use a WFst language model during recognition. Experimental results show that this simple system outperforms extensively tuned state-of-the-art HMM models on the DARPA Arabic machine Print corpus.
Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to...
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In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with...
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ISBN:
(纸本)9781509066285
In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.
Intrusion Detection System is a patternrecognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a...
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The deliberate misuse of technical infrastructure (including the Web and social media) for cyber deviant and cybercriminal behaviour, ranging from the spreading of extremist and terrorism related material to online fr...
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ISBN:
(纸本)9781450346757
The deliberate misuse of technical infrastructure (including the Web and social media) for cyber deviant and cybercriminal behaviour, ranging from the spreading of extremist and terrorism related material to online fraud and cyber security attacks, is on the rise. This workshop aims to better understand such phenomena and develop methods for tackling them in an effective and efficient manner The workshop brings together interdisciplinary researchers and experts in Web search, security informatics, social media analysis, machinelearning, and digital forensics, with particular interests in cyber security. The workshop programme includes refereed papers, invited talks and a panel discussion for better understanding the current landscape, as well as the future of datamining for detecting cyber deviance.
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