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A 65nm CMOS 4.78mm2 integrated neuromodulation SoC consumes 417µW from a 1.2V supply while operating 64 acquisition channels with epoch compression at an average firing rate of 50Hz and engaging two stimulators with a pulse width of 250µs/phase, differential current of 150µA, and a pulse frequency of 100Hz. Compared to the state of the art, this represents the lowest area and power for the highest...
On-site and on-line partial discharge (PD) measurements are well recognized as difficult tasks due to the large scale and diversity of interferences usually encountered in high voltage facilities. Several techniques have been proposed in literature to improve test conditions on the field, based either on analog and digital approaches. More recently, wavelets and their derivatives have shown to be...
This paper presents a chance constrained approach to extracting linear models from reference data to be used in subsequence identification or pattern matching. Due to the ordered nature of time series data, the extracted models are sequential, with feasible domains separated by transition points. In a sequence of models, a transition point is defined as the point where one model is invalid and the...
Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA);...
Detecting changes in data streams is an important area of research in many applications. The challenging issue is to know how to monitor, update and diagnose these changes so that the accuracy of the learner will be improved whatever the nature of the encountered drifts. In this paper a new error distance based approach for drift detection and monitoring, namely EDIST, is proposed. In EDIST, a difference...
In ubiquitous environment, too much information exist, and it is not easy to obtain the well classified data from the information. Therefore an algorithm which should be fast and deduce good result is needed. About it, a decision tree algorithm is much useful in the field of data mining or machine learning system for the problem of classification. However sometimes according to several reasons, a...
Many datasets are numerical tensors, i. e., associate n-tuples with numerical values. Until recently, the discovery of relevant local patterns in such numerical and multidimensional data has received little attention despite the broad applicative perspectives offered by this general framework. Even in the simpler 2-dimensional case, almost every proposal so far is either incomplete (i. e., it does...
According to the situation that many workflow instances may deviate from the predefined model, this paper proposed a new process mining approach based on analyzing the workflow log to realize the workflow process reconstruction. First, build the Markov transition matrix based on the workflow log, then design a multi-step process mining algorithm to mine the structural relationships between the activities,...
Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract...
Most of the clustering algorithms are affected by the number of attributes and instances with respect to the computation time. Thus, the data mining community has made efforts to enable induction of the clustering efficient. Hence, scalability is naturally a critical issue that the data mining community faces. A method to handle this issue is to use a subset of all instances. This paper suggests an...
Nowadays, organizations are facing several challenges when they try to analyze generated data with the aim of extracting useful information. This analytical capacity needs to be enhanced with tools capable of dealing with big data sets without making the analytical process a difficult task. Clustering is usually used, as this technique does not require any prior knowledge about the data. However,...
The radial sand ridge of south yellow sea is the biggest in the world. It has unique combination of factors in dynamic geomorphology. In order to study the trend prediction of beach evolution in radial sand ridge, waterline, which is the best expression for the complex terrain of the seashore intertidal region, is indispensible for research in the region. The study gain the best method to extract...
In this paper we introduce a new framework called Med Cat to delineate and demonstrate an approach for projecting representations of concept-derived content in clinical notes into a new categorization space to reduce dimensionality and noise in the data. Constructing Med Cat framework required several steps including manual annotation, knowledge base expansion using MetaMap, concept category construction,...
Many real time applications, they are generated continues flow of data streams have became more popular now a days. Therefore many researches attracted clustering data streams. Most of data stream clustering algorithms based on distance function which find out clusters with spiracle of shape clusters and unable to deal noisy data. Therefore density based clustering algorithms substitute remarkable...
Spatio-temporal clustering is a sub field of data mining that is increasingly gaining more scientific attention due to the advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process pretends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large...
Biclustering solutions generally depend upon various parameters like number of biclusters and random initialisations. Ensemble techniques have been used to eliminate the impact of such parameters on the output. In this paper, we present a novel ensemble technique for biclustering solutions using mutual information. Unlike the existing approaches, the proposed technique does not require the biclusters...
Personalization is omnipresent everywhere in today's modern world applications. It is primarily employed to improve user experience by adapting and learning from the patterns and information extracted from the user. There are various methods of making a system learn from the user behaviour. This paper gives a review of some of the techniques used for user profiling and personalisation systems. The...
Density-based clustering can detect arbitrary shape clusters, handle outliers and do not need the number of clusters in advance. However, they cannot work properly in multi density environments. The existing multi density clustering algorithms have some problems in order to be applicable for data streams such as the need of whole data to perform clustering, two-pass clustering and high execution time...
Clustering is an important tool which has seen an explosive growth in Machine Learning Algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is one of the most primary methods for clustering in data mining. DBSCAN has ability to find the clusters of variable sizes and shapes and it will also detect the noise. The two important parameters Epsilon (Eps)...
According to the features of SAR image, a new algorithm for image registration based on straight lines is proposed. It is divided to four steps: firstly, compute the attribute parameters of straight lines according to the angles between the line and its neighbor lines. Then, design proper similarity measure function to compute the similarity between lines and find the cursory match result. Thirdly,...
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