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Accurately estimating the probability distribution of renewable power production is a fundamental and challenging task in the probabilistic analysis of power systems with a high penetration of renewable energy. In this study, a novel hybrid method of minimum frequency and maximum entropy (MFME) is proposed for accurately and rapidly estimating the probability density function (PDF) of renewable power...
Scene recognition applications on mobile devices receive increasing attentions in recent years. Due to mobile users' real-time requirement, an accurate and efficient scene recognition system is urgent for mobile applications. In this paper, we propose a novel discriminative codeword selection method by using the ensemble extreme learning machine (ELM) algorithm for fast and accurate scene recognition...
The ultrafast segmented model identification of linearity error (uSMILE) algorithm dramatically reduces ADC linearity test time while achieving superior test accuracy. This method avoids the gross inefficiencies in the conventional histogram test method to reduce the test data by a factor of over 100. However, in low noise environment where the quantization noise becomes dominant, uSMILE leads to...
Accurately characterizing linearity performance of high resolution Analog-to-Digital Converters (ADCs) has been a challenging task for many years, as providing input signals whose purity is beyond ADC under test becomes harder and harder as the ADC performance becomes better. This paper proposes a novel method that uses impure test signals to accurately test linearity performance of ADC. Two nonlinear...
Automatic vegetation coverage detection plays a key role for monitoring and management of land usage, environmental variation, and urban planning. This paper presents a novel vegetation coverage detection technique for very high resolution multi-spectral satellite imagery. The proposed technique consists of two stages including a supervised patch-level scoring stage and an unsupervised pixel-level...
Linearity test of an analog-to-digital converter (ADC) can be very challenging because it requires a signal generator substantially more linear than the ADC under test. For high performance ADCs, the overall manufacturing cost could be dominated by the long test time and the high-precision test instruments. This paper introduces the ultrafast stimulus error removal and segmented model identification...
Bag-of-visual phrase (BoP) has been proposed and developed for landmark recognition recently. However, existing BoP methods for landmark recognition have two major shortcomings: (i) they try to construct a universal phrase vocabulary for all object categories, which lacks specific descriptive capabilities for a particular category, and (ii) they often adopt simple criterion such as the frequency information...
This paper proposes a new information fusion approach that employs two information components for mobile landmark recognition, which includes: content analysis and context analysis. Existing landmark recognition works are mainly based on PC platform, which uses content analysis alone for recognition, and thus has a large computation cost and cannot satisfy mobile users' fast response time requirements...
This paper proposes a discriminative learning bags-of-words (BoW) approach for mobile landmark recognition at patch and image levels. Conventional methods often treat the local patches and images equally important for recognition and do not differentiate their different importance. Although there exist several works that consider the patches' discrimination information, they mainly focus on which...
This paper proposes an adaptive bag-of-phrases (BoP) algorithm for mobile scene recognition based on bag-of-words approach. Conventional BoW methods do not consider the dependence and pairwise relationship among different codewords. However, these contextual relations between pairwise codewords play an important role for users to recognize an image. In light of this problem, this paper proposes an...
This paper presents a self-adapting algorithm based on Mean Shift model to track the target in video sequences. Firstly, two-dimensional histogram is used to represent the target instead of one-dimensional histogram, so as to better distinguish the target from background. Secondly, algorithm has been improved by adding self-adapting progress to remove errors caused by local maximum. Experiments on...
The growing usage of mobile devices has led to proliferation of many mobile applications. A growing trend in mobile applications is centered on mobile landmark recognition. It is a new mobile application that recognizes a captured landmark using the mobile device and retrieves related information. This paper will present a survey on mobile landmark recognition for information retrieval. A general...
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