Microblog, especially twitter, has become an integral part of our daily life for searching latest news and events information. Due to the short length characteristics of tweets and frequent use of unconventional abbreviations, content-relevance based search cannot satisfy user's information need. Recent research has shown that considering temporal and contextual aspects in this regard has improved the retrieval performance significantly. In this paper, we focus on microblog retrieval, emphasizing the alleviation of the vocabulary mismatch, and the leverage of the temporal (e.g., recency and burst nature) and contextual characteristics of tweets. To address the temporal and contextual aspect of tweets, we propose new features based on query-tweet time, word embedding, and query-tweet sentiment correlation. We also introduce some popularity features to estimate the importance of a tweet. A three-stage query expansion technique is applied to improve the relevancy of tweets. Moreover, to determine the temporal and sentiment sensitivity of a query, we introduce query type determination techniques. After supervised feature selection, we apply random forest as a feature ranking method to estimate the importance of selected features. Then, we make use of ensemble of learning to rank (L2R) framework to estimate the relevance of query-tweet pair. We conducted experiments on TREC Microblog 2011 and 2012 test collections over the TREC Tweets2011 corpus. Experimental results demonstrate the effectiveness of our method over the baseline and known related works in terms of precision at 30 (P@30), mean average precision (MAP), normalized discounted cumulative gain at 30 (NDCG@30), and R-precision (R-Prec) metrics.

Multi-view video streaming plays an important role in new interactive and augmented video applications such as telepresence, remote surgery, and entertainment. For those applications, interactive multi-view video transmission schemes have been proposed that aim to reduce the amount of video traffic. Specifically, these schemes only encode and transmit video frames, which are potentially displayed by users, based on periodical feedback from the users. However, existing schemes are vulnerable to frame loss, which often occurs during transmissions, because they encode most video frames using inter prediction and inter-view prediction to reduce traffic. Frame losses induce significant quality degradation due to the collapse of the decoding operations. To improve the loss resilience, we propose an encoding/decoding system, Frame Popularity-based Multi-view Video Streaming (FP-MVS), for interactive multi-view video streaming services. The main idea of FP-MVS is to assign intra (I) frames in the prediction structure for less/more popular (i.e., few/many observed users) potential frames in order to mitigate the impact of a frame loss. In addition, FP-MVS utilizes overlapping and non-overlapping areas between all user's potential frames to prevent redundant video transmission. Although each intra-frame has a large data size, the video traffic can be reduced within a network constraint by combining multicast and unicast for overlapping and non-overlapping area transmissions. Evaluations using Joint Multi-view Video Coding (JMVC) demonstrated that FP-MVS achieves higher video quality even in loss-prone environments. For example, our scheme improves video quality by 11.81dB compared to the standard multi-view video encoding schemes at the loss rate of 5%.

In this letter, a novel mainlobe anti-jamming method via eigen-projection processing and covariance matrix reconstruction is proposed. The present work mainly focuses on two aspects: the first aspect is to obtain the eigenvector of the mainlobe interference accurately in order to form the eigen-projection matrix to suppress the mainlobe interference. The second aspect is to reconstruct the covariance matrix which is uesd to calculate the adaptive weight vector for forming an ideal beam pattern. Additionally, the self-null effect caused by the signal of interest and the sidelobe interferences elimination are also considered in the proposed method. Theoretical analysis and simulation results demonstrate that the proposed method can suppress the mainlobe interference effectively and achieve a superior performance.

The theory of compressed sensing (CS) is very attractive in that it makes it possible to reconstruct sparse signals with sub-Nyquist sampling rates. Considering that CS can be regarded as a joint source-channel code, it has been recently applied in communication systems and shown great potential. This paper studies compressed cooperation in an amplify-and-forward (CC-AF) relay channel. By discussing whether the source transmits the same messages in two phases, and the different cases of the measurement matrices used at the source and the relay, four decoding strategies are proposed and their transmission rates are analyzed theoretically. With the derived rates, we show by numerical simulations that CC-AF outperforms the direct compressed transmission without relay. In addition, the performance of CC-AF and the existing compressed cooperation with decode-and-forward relay is also compared.

Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.

In this paper, we investigate the channel characteristics of underwater optical wireless communications (UOWC) based on Monte Carlo simulation method. The impulse response and channel time dispersion of the link are discussed. Also we consider the channel parameters comprehensively like the water type, attenuation length, divergence angle, beam width, field-of-view (FOV), receiver aperture and position. Simulation results suggest that in clear water, the channel can effectively be considered as non inter-symbol interference (ISI) when working over distance of up to 40m. Therefore, in practice the receiver does not need to perform computationally complex signal processing operations. However, in harbor water, the channel time dispersion will enlarge with larger FOV or divergence angle, and reduce the data transmission efficiency. When the attenuation length is smaller than diffused length, larger receivers offer lower intensity than smaller ones. In contrast, the intensity enhances with larger receiver at the small FOV, however, they trend to similar regardless of the apertures at large FOV. Furthermore, we study the effect of misalignment of the transmitter and receiver on the received intensity. The results give us some insight in terms of what constitutes an accurate UOWC channel.

Traditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.

In this paper, a hamburger architecture with a 3D stacked reconfigurable memory is proposed for a 4K motion estimation (ME) processor. By positioning the memory dies on both the top and bottom sides of the processor die, the proposed hamburger architecture can reduce the usage of the signal through-silicon via (TSV), and balance the power delivery network and the clock tree of the entire system. It results in 1/3 reduction of the usage of signal TSVs. Moreover, a stacked reconfigurable memory architecture is proposed to reduce the fabrication complexity and further reduce the number of signal TSVs by more than 1/2. The reduction of signal TSVs in the entire design is 71.24%. Finally, we address unique issues that occur in electronic design automation (EDA) tools during 3D large-scale integration (LSI) designs. As a result, a 4K ME processor with 7-die stacking 3D system-on-chip design is implemented. The proposed design can support real time 3840 × 2160 @ 120 fps encoding at 130 MHz with less than 540 mW.

In this paper we propose a method for estimating time-to-contact in scattering media. Images taken in scattering media are often unclear and blurry, making it difficult to detect appropriate geometric information from these images for computing the 3 dimensional properties of the scene. Therefore, instead of searching for geometric information, we attempt to use photometric information instead. In our approach, we use the observed image intensity. The method proposed in this paper is able to utilize the effect of scattering media on the resultant image and estimate the time-to-contact toward objects without any prior knowledge of the scene, cameras, and the scattering media. This method is then evaluated using simulated and real images.

The compressive sensing (CS) theory has been widely used in synthetic aperture radar (SAR) imaging for its ability to reconstruct image from an extremely small set of measurements than what is generally considered necessary. Because block-based CS approaches in SAR imaging always cause block boundaries between two adjacent blocks, resulting in namely the block artefacts. In this paper, we propose a weighted overlapped block-based compressive sensing (WOBCS) method to reduce the block artefacts and accomplish SAR imaging. It has two main characteristics: 1) the strategy of sensing small and recovering big and 2) adaptive weighting technique among overlapped blocks. This proposed method is implemented by the well-known CS recovery schemes like orthogonal matching pursuit (OMP) and BCS-SPL. Promising results are demonstrated through several experiments.

In this work, we study efficient scheduling with network coding in a scalable video coding (SVC) multicast system. Transmission consists of two stages. The original SVC packets are multicasted by the server in the first stage and the lost packets are retransmitted in the second stage. With deadline constraint, the consumer can be only satisfied when the requested packets are received before expiration. Further, the hierarchical encoding architecture of SVC introduces extra decoding delay which poses a challenge for providing acceptable reconstructed video quality. To solve these problems, instantly decodable network coding is applied for reducing the decoding delay, and a novel packet weighted policy is designed to better describe the contribution a packet can make in upgrading the recovered video quality. Finally, an online packet scheduling algorithm based on the maximal weighted clique is proposed to improve the delay, deadline miss ratio and users' experience. Multiple characteristics of SVC packets, such as the packet utility, the slack time and the number of undelivered/wanted packets, are jointly considered. Simulation results prove that the proposed algorithm requires fewer retransmissions and achieves lower deadline miss ratio. Moreover, the algorithm enjoys fine recovery video quality and provides high user satisfaction.

This paper presents a hybrid push/pull streaming scheme to take advantage of both the interval caching-based push method and the mesh-based pull method. When a new peer joins, a mesh-based pull method is adopted to avoid the overhead to reorganize the structure only if all of its potential preceding peers are likely to leave before the end of its playback. Otherwise, an interval caching-based push method is adopted so that the better performance of the push method can be maintained until it completes the playback. We demonstrate that our proposed scheme outperforms compared with when either the interval caching-based push method or mesh-based pull method is employed alone.

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

A comprehensive model is presented for estimating the bit error rate (BER) of write disturbance in a resistive memory composed of a cross-point array. While writing a datum into the selected address, the non-selected addresses are biased by word-line (WL) and bit-line (BL). The stored datum in the non-selected addresses will be disturbed if the bias is large enough. It is necessary for the current flowing through the non-selected address to be calculated in order to estimate the BER of the write disturbance. Since it takes a long time to calculate the current flowing in a large-scale cross-point array, several simplified circuits have been utilized to decrease the calculating time. However, these simplified circuits are available to the selected address, not to the non-selected one. In this paper, new simplified circuits are proposed for calculating the current flowing through the non-selected address. The proposed and the conventional simplified circuits are used, and on that basis the trade-off between the write disturbance and the write error is discussed. Furthermore, the error correcting code (ECC) is introduced to improve the trade-off and to provide the low-cost memory chip matching current production lines.

3GPP (3rd Generation Partnership Project) has started to discuss D2D (Device-to-Device)-aided OTDOA (Observed Time Difference Of Arrival) as one of the mobile positioning enhancement techniques for LTE (Long Term Evolution) systems. It is a kind of multi-node based OTDOA which directly receives D2D signals from adjacent multiple UEs (User Equipment) to measure RSTD (Reference Signal's Time Difference). D2D signals provide valuable advantages in terms of OTDOA positioning because it can guarantee more reference nodes and high SNR (Signal-to-Noise Ratio) of PRS (Positioning Reference Signal). Two typical methods for multi-node based OTDOA can be applied to D2D-aided OTDOA. Multiple OTDOA positioning is one of the multi-node based methods that averages multiple results from OTDOA; however, it cannot always guarantee high accuracy due to the non-uniform geometry of UEs. OTDOA positioning based on TSE (Taylor Series Expansion) algorithm may be one of the solutions; however, it has the initial value problem and high computational complexity due to its iterative procedure. Therefore, in this paper, we propose a novel D2D-aided OTDOA positioning method which utilizes UEs not as reference node of OTDOA but as assisting node for RSTD error reduction. The proposed method can reduce RSTD error of eNB based hyperbola by using multiple hyperbola bands. The hyperbola band indicates the possible range in which a hyperbola can occur due to RSTD error. Then, by using principal axes of hyperbolas, we estimate a modified hyperbola from the overlap area of hyperbola bands, which has less RSTD error. We verify that the proposed method can effectively reduce RSTD error and improve positioning performance with lower computational complexity.

In this review, we present recent advances relating to superconducting nanowire single-photon detectors (SSPDs or SNSPDs) and their broad range of applications. During a period exceeding ten years, the system performance of SSPDs has been drastically improved, and lately excellent detection efficiencies have been realized in practical systems for a wide range of target photon wavelengths. Owing to their advantages such as high system detection efficiency, low dark count rate, and excellent timing jitter, SSPDs have found application in various research fields such as quantum information, quantum optics, optical communication, and also in the life sciences. We summarize the photon detection principle and the current performance status of practical SSPD systems. In addition, we introduce application examples in which SSPDs have been applied.

For reliable communication, this letter proposes cooperative transmission scheme with spatial phase coding (SPC) in the edge area among base stations. The diversity method has the a difficulty in terms of the price and complexity in a base station with multiple antennas. Thus, this problem may be resolved by using the cooperative scheme among the base stations and the proposed scheme increases that uses economically resource by using less feedback bits. Especially, if the coverage of many base stations is overlapped, the performance of the proposed scheme is improved. From the simulation results, the proposed scheme has the better performance compared to the conventional scheme in heterogeneous network.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

We consider the problem of two-dimensional (2-D) angles of arrival estimation using a newly proposed structure of nonuniform linear array, referred to as nested coprime array with compressed inter-element spacing (CACIS). By constructing a cross-correlation matrix of the received signals, the nested CACIS exhibits a larger number of degrees of freedom. A two-step weighted l1-norm penalty strategy is proposed to fully utilize these degrees of freedom, where the weight matrices are constructed by MUSIC spectrum function and the threshold function, respectively. The proposed method has several salient advantages over the compared method, including increased resolution and accuracy, estimating many more number of sources and suppressing spurious peaks efficiently. Simulation results validate the superiority of the proposed method.