Yanshan Li

Energy efficient design for multiuser downlink energy and uplink information transfer in 5G

Simultaneous wireless information and power transfer (SWIPT) is studied in this paper for the wireless powered downlink (DL) and multiuser information uplink (UL) systems. The objective is to maximize the energy efficiency defined as the ratio of the achieved throughput over the energy cost by optimizing the time allocation for the DL and multi-user UL traffics and its goal is to obtain the analytical expression to the optimal time allocation yet the resulting difficulty comes from the sum throughput of the multiuser in UL as well as the corresponding power consumption. To tackle this, the Jensen inequality is applied to approximating the exact expression of the sum throughput for the UL multi-users, leading to an upper-bound of the counterpart. The final closed form is exact in the single-user scenario yet approximate in the multi-user scenario. Numerical simulations verify the tightness of this approximation and the performances of the proposed analytical scheme.创新点1建立了能量效率最大化的时间分配数学问题,该问题适用于任意多用户的下行能量传输和上行无线信息传输的多用户传输系统2推导得到系统的能量效率函数的上界函数,该函数来能够较紧的逼近原始准确能量效率函数3基于界函数,推导出每个用户的上行链路传输无线信息的持续时间,而且得到解析解,该解析解在单用户场景下具有全局最优性,在任意多用户场景下具有渐进最优性。

Traffic anomaly detection based on image descriptor in videos

The huge and ever growing volume of traffic video poses a compelling demand for efficient automatic detection of traffic anomaly. In this paper, a new traffic anomaly detection algorithm is introduced. It firstly divides a traffic video into several video cubes in temporal domain, and each video cube is divided into video blocks in spatial domain. Each image block of a video block is described using the local invariant features and the visual codebook approach. Based on the descriptor of the image block, we count the category number of the block (CNB) of a video block. Then, a Gaussian distribution model for estimating the probability of normal traffic with respect to the CNB is learned. The learned Gaussian distribution model is then used to detect the traffic anomaly from the test traffic video. Eventually, the results of all video blocks are fused to achieve the final decision. Experimental results show that the proposed algorithm performs better than two existing algorithms on both the intersection traffic videos and main road traffic videos.

Fuzzy bag of words for social image description

Rapid growth of social media resources brings huge challenges and opportunities for image description technologies. The performance of image description method directly affects the accuracy of image retrieval, image annotation and image recognition. Bag of Words (BoW) as an efficient approach to describing the images has been attracting more and more attention. However, in traditional BoW, the maps between the words in the codebook and the features extracted from the images are actually ambiguous. As the Fuzzy Sets Theory (FST) is a powerful means for dealing with uncertainty efficiently, we utilize the FST to solve the problem caused by the ambiguity between the features and words. Accordingly, we propose a new type of BoW named as FBoW to describe images based on FST. Firstly, the features are extracted from the images. Secondly, k-means is utilized to learn the codebook. Thirdly, a fuzzy membership function is designed to measure the similarity between the features and words. The optimal parameters of the fuzzy membership function are obtained by using a Genetic Algorithm (GA). The histogram is generated by adding up the fuzzy membership values of each word to describe the images. The experimental results show that the proposed FBoW outperforms traditional BoW for social image description.

A novel visual codebook model based on fuzzy geometry for large-scale image classification

The codebook model has been developed as an effective means for image classification. However, the inherent operation of assigning visual words to image feature vectors in traditional codebook approaches causes serious ambiguities in image classification. In particular, the nearest word may not be the best fit to a feature, and multiple words may be equally appropriate for one specific feature. To resolve these ambiguities, we propose a novel visual codebook model based on the n-dimensional fuzzy geometry (n-D FG) theory, where all visual words and features are modeled as fuzzy points in the n-D FG space, and appropriate uncertainty is introduced to each fuzzy point to enhance the representation capacity. This n-D FG-codebook model not only inherits advantages from the fuzzy set theory, but also facilitates the analysis and determination of the relationship between visual words and features in geometric form. By explicitly taking into account the ambiguities, we propose a novel measure of similarity between the visual words and fuzzy features. Following the proposed codebook model and the novel similarity measure, we develop two useful image classification algorithms by modifying popular image coding algorithms (i.e. SPM and LLC). Finally, experimental results demonstrate that the classification accuracy of the proposed algorithms is dramatically improved for a standard large-scale image database. For example, with a codebook size of 256, the proposed algorithms achieve similar performance as traditional algorithms with a codebook size of 1024, indicating that the proposed algorithms reduce the computational cost by 75% while achieving almost identical classification accuracy to traditional algorithms. Thus, the proposed algorithms represent a more efficient and appropriate scheme for big image data. This paper aims to overcome the drawbacks of traditional visual words.Fuzzy visual word and fuzzy feature are defined using n-dimensional fuzzy geometry.A new similarity measure between fuzzy features and fuzzy visual words is designed.Two modified image classification frameworks based on Fuzzy codebook are proposed.Experimental results demonstrated their advantages against traditional algorithms.

Survey of Spatio-Temporal Interest Point Detection Algorithms in Video

Recently, increasing attention has been paid to the detection of spatio-temporal interest points (STIPs), which has become a key technique and research focus in the field of computer vision. Its applications include human action recognition, video surveillance, video summarization, and content-based video retrieval. Amount of work has been done by many researchers in STIP detection. This paper presents a comprehensive review on STIP detection algorithms. We first propose the detailed introductions and analysis of the existing STIP detection algorithms. STIP detection algorithms are robust in detecting interest points for video in the spatio-temporal domain. Next, we summarize the existing challenges in the STIP detection for video, such as low time efficiency, poor robustness with respect to camera movement, illumination change, perspective occlusion, and background clutter. This paper also presents the application situations of STIP and discusses the potential development trends of STIP detection.

Extreme-constrained spatial-spectral corner detector for image-level hyperspectral image classification

Abstract As one type of local invariant feature, corner feature plays an important role in diverse applications such as: video mining, target detection, image classification, image retrieval, and image matching, etc. However, there are few studies on corner feature for hyperspectral image (HSI). Therefore, this paper proposes a novel corner feature for HSI named extreme-constrained spatial-spectral corner (ECSSC for short) and its corresponding detector. The definition of ECSSC is developed based on the definition of spectral-spatial interest point and the characteristic of HSI. Based on this definition, the detector of ECSSC is put forward and introduced in detail. Then, as an important application of ECSSC, an efficient framework for image-level HSI classification is designed based on ECSSC and parallel computation. The experimental results show that the proposed algorithm can detect abundant corner features with high repeatability rate from HSI and the accuracy of image-level HSI based on ECSSC is dramatically higher than that of the state of the art.

A new framework of hyperspectral image classification based on spatial spectral interest point

With the development of the hyperspectral sensor technologies, the spatial and spectral resolution of Hyperspectral Image (HSI) have been improved largely. In this case, objects are made up of several pixels which may belong to different materials. It is difficult to separate hyperspectral data with the traditional hyperspectral image classification algorithms. Therefore, based on spatial spectral interest point (SSIP), a new parallel framework of hyperspectral image classification is proposed. The experimental results show that the classification accuracy of our proposed algorithm is effective and dramatically higher than that of existing classification algorithms for 2D pseudo color images.

A new bag of words model based on fuzzy membership for image description

Bag of Words (BoW) as an efficient approach to describing the images has been attracting more and more attention. However, in traditional BoW, the maps between words in codebook and features extracted from images are ambiguous. We propose a new type of BoW based on Gaussian membership function (Gaussian-BoW) to describe images. In Gaussian-BoW, the codebook is obtained by using k-means like the traditional BoW. Then, words are assigned to the feature with Gaussian membership values. At last, histogram is generated by adding up the fuzzy membership values of each word to describe the images. The experimental results show that the proposed Gaussian-BoW outperforms traditional BoW for image description.

Discovery of trading points based on Bayesian modeling of trading rules

Mining hidden patterns with different technical indicators from the historical financial data has been regarded as an efficient way to determine the trading decisions in the financial market. Technical analysis has shown that a number of specific combinations of technical indicators could be treated as trading patterns for forecasting efficient trading directions. However, it is a challenging assignment to discover those combinations. In this paper, we innovatively propose to use a biclustering algorithm to detect the trading patterns. The discovered trading patterns are then utilized to forecast the market movement based on the Naive Bayesian algorithm. Finally, the Adaboost algorithm is applied to improve the accuracy of the forecasts. The proposed method was implemented on seven historical stock datasets and the average performance was compared with that of four existing algorithms. Experimental results demonstrated that the proposed algorithm outperforms the other four algorithms and can provide a valuable reference in the financial investments.

A unified model of appearance and motion of video and its application in STIP detection

Spatio-temporal interest points (STIPs) are local invariant features of a video and are both distinctive and descriptive and therefore can be applied in action recognition; however, most existing STIP detectors extend spatial descriptions by adding a temporal component for the appearance description, which separate spatio-temporal domain correlations in the spatio-temporal domain and only implicitly capture motion information. Therefore, by regarding the video as a 3-d structure, this research aims to develop a novel STIP detector which synthetically exploits appearance and motion information using Clifford algebra. This study firstly establishes a general Clifford algebra model for video and then builds the unified model of appearance and motion (UMAM) based thereon to synthetically analyse appearance and motion information. Subsequently, in the spirit of the well-known Harris 3-d detector, a UMAM-based spatio-temporal Harris corner detector (UMAM-Harris) for videos is developed. The experimental results indicate that the UMAM-Harris detector proposed in this study extracts the UMAM-Harris corners that contain distinctive features in the spatial domain and reflect substantial motion in the time domain, and it offers a better performance than the traditional STIP detection algorithms used in video action recognition.