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Passive source location determination is a very active research area. In this paper, we present a received signal strength difference (RSSD) source localization method based on a total least square (TLS) estimator. Due to errors in the data vector and system matrix, the least squares (LS) and weighted least squares (WLS) methods are not applicable as they produce large bias in the location estimation...
Pattern matching is a fundamental operation in finding knowledge from large amount of biosequence data. Finding patterns help in analyzing the property of a sequence. This paper focuses on the problem of maximal pattern matching with flexible wildcard gaps and length constraints under the one-off condition. The problem is to find the maximum number of occurrences of a pattern P with user specified...
In Long Term Evolution (LTE) networks, resource allocation and link adaptation rely on the channel quality indicator (CQI), which is a quantized representation of the signal interference plus noise ratio (SINR). Since CQI is measured by user equipment (UE) and sent to eNodeB via a feedback channel, in time varying channel, feedback delay should be compensated with prediction based on previous knowledge...
A novel low-complexity visual saliency detection algorithm for detecting salient regions in images is proposed. The algorithm derives salient regions based on in-focus regions and image centre sensitivity. The performance of the algorithm in predicting human eye fixations is validated against ten state-of-the-art algorithms using a public image dataset. The results demonstrate that the proposed algorithm...
Designing an efficient MIMO decoder for higher order MIMO system such as 8×8 is a big challenge. The Group Detection (GD) method has been considered as a good choice because it balances well the trade-off between detection accuracy and computational complexity. This method converts the higher order MIMO channel matrix into several lower order matrices and then decodes these lower order matrices separately...
The paper analyzes the problem of predicting the outcome of elections (how many votes each candidate is going to get), given an imperfect information on the preferences of the voters. We assume that we have a fixed prior on the preferences of each voter for each candidate. We have used two naive algorithms which predict the votes obtained by each candidate in an election. The algorithms are fast and...
Designing an efficient MIMO decoder for higher order MIMO system such as 8×8 is a big challenge. MLD is well-known as the optimal solution in terms of detection performance. However, the complexity of the MLD increases exponentially with the number of constellation points and the number of spatial streams. The Group Detection (GD) method has been considered as a good choice because it balances well...
Fetal Heart Rate (FHR) monitoring represents a powerful tool for checking the arousal of pathological fetal conditions during pregnancy. This paper proposes a multivariate approach for the discrimination of Normal and Intra Uterine Growth Restricted (IUGR) fetuses based on a small set of parameters computed on the FHR signal. We collected FHR recordings in a population of 120 fetuses (60 normals and...
A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around...
Clustering is an exploratory data analysis technique, which categorizes the dataset into some groups. These groups are formed in a way so that items which have similar features live in same group and those have dissimilar features remain in other. There are many clustering algorithm available. Different kinds of algorithms are best used for different kinds of data. K-means is most used clustering...
State-of-the-art volume integral equation (VIE) solvers for solving electrically large problems are iterative solvers with the complexity of each matrix-vector multiplication being O(NlogN), where N is matrix size. In this work, we reduce this complexity to strictly O(N) irrespective of electrical size. Furthermore, we develop a fast inversion based direct VIE solver of O(NlogN) complexity, which...
In real-time systems, power gating is widely adopted by processing cores but not working memory because of data loss. Meanwhile, new non-volatile memory technology, which is comparable to volatile memory, quickly emerges. Thus, in this paper, we propose data-aware power management for real-time systems with both volatile and non-volatile memories. Considering the trade-off between data migration energy...
In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number...
Behavior Model Inference techniques aim at mining behavior models from execution traces. While most of approaches usually ground on local similarities in traces, recent work, referred to as behavior mining with temporal steering, propose to include long term dependencies in the mining process. Such dependencies correspond to temporal implications between events in execution traces, whose consideration...
In this paper, we develop a fast direct finite-element solver of linear (optimal) complexity for solving large-scale system-level signal and power integrity problems. The proposed direct solver has successfully analyzed an industry product-level full-package problem and correlated with measurements in time domain. The finite-element matrix of over 15.8 million unknowns resulting from the analysis...
While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams...
This paper proposes a novel approach to explore emergent patterns in images in an unsupervised setting. We consider emergent patterns to be sets of co-occurring visual words that appear together more often than chance would indicate. Rather than focusing on finding ways to learn a large number of objects or their categories we focus on analyzing behavior associated with emergent patterns. We show...
Recently, methods for the unsupervised learning of features from large data sets have been attracting much attention. These methods have been especially successful in the area of computer vision. However, there is a problem that it is difficult to determine what kind of features will result in a high classification performance. Indeed, the difficulty of determining the learning parameters is a widely...
Semi-Supervised Learning (SSL) techniques have become very relevant since they require a small set of labeled data. In this context, graph-based algorithms have gained prominence in the area due to their capacity to exploiting, besides information about data points, the relationships among them. Moreover, data represented in graphs allow the use of collective inference (vertices can affect each other),...
We propose a gender classifier using two types of local features, the gradient features which have strong discrimination capability on local patterns, and the Gabor wavelets which reflect the multi-scale directional information. The Real Ad a Boost algorithm with complexity penalty term is applied to choose meaningful regions from human face for feature extraction, while balancing the discriminative...
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