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Early power modeling and analysis using electronic system-level methodology enables designers to explore energy saving opportunities more efficiently at a higher abstraction level. However, power modeling for third party IPs are challenging due to the limited observability and unknown architecture details. To model the data dependency for blackbox IPs, several works rely on adopting Hamming distance...
Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS — achieving the position estimation by reusing the available WLAN and mobile devices, and is...
In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the...
This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The training data patterns are processed incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is added to the most similar cluster. During the clustering...
Spectral efficiency and capacity of Land Mobile Satellite (LMS) systems can be enhanced by using MIMO (Multiple Input Multiple Output). Polarization diversity is an efficient technique to realize MIMO in LMS communications. Polarization diversity employs different polarization to realize multiple independent propagation paths. Dual polarized antennas offer a space and cost effective alternative compared...
Efficient crowd counting is an essential task in crowd monitoring, and significant advances have been made in this field recently by counting-by-regression techniques. We propose in this work a learning-to-count strategy with a generic detection algorithm which benefits from a counting regressor in order to identify crowded subregions with inadequate head detection performance, and to improve their...
In this paper, we present a new approach to count the number of people that cross a counting line from video images. This paper focuses on point-level annotation in training images and incorporate spatial features along with novel temporal features in training the structured random forest for estimating crowd density. By computing the crowd velocity, we model the crowd counting map as elementwise...
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level...
Reliability of energy storage systems, for stationary as well as mobile applications, is crucial for their stable long term operation. Among all the components that are susceptible to failure, modules made up of individual batteries determine the useful life of such a system. Hence, estimating health quotients of battery packs in typical energy storage systems takes on a high priority. The computational...
To understand the behavior of moving entities in a given environment, one should be capable of predicting their motion, that is, to model their dynamics. In a setting where different behaviors can arise, one can assume that each of them corresponds to different motivational states of observed entities. Here, those motivations are understood as goal positions or spots where entities seek to arrive...
We present an end-to-end system for musical key estimation, based on a convolutional neural network. The proposed system not only out-performs existing key estimation methods proposed in the academic literature; it is also capable of learning a unified model for diverse musical genres that performs comparably to existing systems specialised for specific genres. Our experiments confirm that different...
In this work, the problem of channel estimation in multicarrier communications with the Type-I even discrete cosine transform (DCT1e) is addressed. A novel scheme, based on using the DCT1e, both at the transmitter and the receiver, is introduced. The proposed approach does not require adding any redundancy or knowing the exact length of the channel's impulse response. By constructing a symmetric training...
The conventional algorithm of channel estimation based on IEEE802.11ac is Least Square (LS) algorithm, which uses the Very High Throughput-Long Training Field (VHTLTF) of frame header as training sequence. While the conventional algorithm does not take into account the effects of noise and the varying characteristics of channel in time domain. In order to describe the slow change of channel in time...
Degradation trend estimation of rolling bearing is widely applied in many engineering applications. With the rapid development of sensors, massive data will be acquired because of the monitoring of machinery. So how to extract and use the effective data from the big data for trend estimation has profound research value. The least square support vector machine (LSSVM), which is a kind of novel artificial...
This study presents an age and gender estimation system that considers ethnic difference in face images using a Convolutional Neural Network(CNN) and Support Vector Machine(SVM). Most age and gender estimation systems using face images are trained on ethnicity-biased databases. Therefore, these systems show limited performance on face images of ethnic groups occupying a small proportion of the training...
In this paper, we design a novel training symbol structure to estimate in-phase/quadrature (IQ) mismatch. Based on this structure the estimation method is deduced in frequency domain which can achieve the estimation of IQ mismatch and channel distortion independently and improve the system performance obviously. Numerical simulation shows that the proposed method has better system BER performance...
Pilot contamination attack is an important kind of active eavesdropping activity conducted by a malicious user during channel training phase. In this paper, motivated by the fact that frequency asynchronism could introduce divergence of the transmitted pilot signals between intended user and attacker, we propose a new uncoordinated frequency shift (UFS) scheme for detection of pilot contamination...
This paper presents a method to estimate the remaining useful life for degrading systems operating under time-varying operational conditions. This method considers a non-monotone degradation process that is significantly affected by stochastically-evolving operational conditions. The failure zone instead of the deterministic failure threshold is used to identify the failures, and different operational...
Manifold causes of image blurring make the no-reference evaluation of realistic blurred images very challenging. Previous studies indicate that handcrafted features suffer from poor representation of the intrinsic characteristics of image blurring and thus blind image sharpness assessment (BISA) is unsatisfactory. This paper explores a shallow convolutional neural network (CNN) to address this problem...
Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a CNN based patch matching approach for optical flow estimation. An important contribution of our approach is a novel thresholded loss for Siamese networks. We demonstrate that our loss performs clearly better than existing...
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