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%.

This paper presents an ultra-low power and temperature-independent voltage detector with a post-fabrication programming method, and presents a theoretical analysis and measurement results. The voltage detector is composed of a programmable voltage detector and a glitch-free voltage detector to realize both programmable and glitch-free operation. The programmable voltage detector enables the programmable detection voltages in the range from 0.52V to 0.85V in steps of less than 49mV. The glitch-free voltage detector enables glitch-free operation when the supply voltage is near 0V. A multiple voltage copier (MVC) in the programmable voltage detector is newly proposed to eliminate the tradeoff between the temperature dependence and power consumption. The design consideration and a theoretical analysis of the MVC are introduced to clarify the relationship between the current in the MVC and the accuracy of the duplication. From the analysis, the tradeoff between the duplication error and the current of MVC is introduced. The proposed voltage detector is fabricated in a 250nm CMOS process. The measurement results show that the power consumption is 248pW and the temperature coefficient is 0.11mV/°C.

This paper presents an unsupervised approach to feature binary coding for efficient semantic image retrieval. Although the majority of the existing methods aim to preserve neighborhood structures of the feature space, semantically similar images are not always in such neighbors but are rather distributed in non-linear low-dimensional manifolds. Moreover, images are rarely alone on the Internet and are often surrounded by text data such as tags, attributes, and captions, which tend to carry rich semantic information about the images. On the basis of these observations, the approach presented in this paper aims at learning binary codes for semantic image retrieval using multimodal information sources while preserving the essential low-dimensional structures of the data distributions in the Hamming space. Specifically, after finding the low-dimensional structures of the data by using an unsupervised sparse coding technique, our approach learns a set of linear projections for binary coding by solving an optimization problem which is designed to jointly preserve the extracted data structures and multimodal data correlations between images and texts in the Hamming space as much as possible. We show that the joint optimization problem can readily be transformed into a generalized eigenproblem that can be efficiently solved. Extensive experiments demonstrate that our method yields significant performance gains over several existing methods.

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.

This paper presents a design optimization method for a Gm-C active filter via geometric programming (GP). We first describe a GP-compatible model of a cascaded Gm-C filter that forms a biquadratic output transfer function. The bias, gain, bandwidth, and signal-to-noise ratio (SNR) of the Gm-C filter are described in a GP-compatible way. To further enhance the accuracy of the model, two modeling techniques are introduced. The first, a two-step selection method, chooses whether a saturation or subthreshold model should be used for each transistor in the filter to enhance the modeling accuracy. The second, a bisection method, is applied to include non-posynomial inequalities in the filter modeling. The presented filter model is optimized via a GP solver along with proposed modeling techniques. The numerical experiments over wide ranges of design specifications show good agreement between model and simulation results, with the average error for gain, bandwidth, and SNR being less than 9.9%, 4.4%, and 14.6%, respectively.

COordinate Rotation DIgital Computer (CORDIC) is an efficient algorithm to compute elementary arithmetic such as trigonometric, exponent, and logarithm. However, the main drawback of the conventional CORDIC is that the number of iterations is equal to the number of angle constants. Among a great deal of research to overcome this disadvantage, angle recording method is an effective method because it is capable of reducing 50% of the number of iterations. Nevertheless, the hardware architecture of this algorithm is difficult to implement in pipeline. Therefore, a low-latency parallel pipeline hybrid adaptive CORDIC (PP-CORDIC) architecture is proposed in this paper. In the design hybrid architecture was exploited together with pipeline and parallel technique to achieve low latency. This design is able to operate at 122.6 MHz frequency and costs 8, 12, and 15 clock cycles latency in the best, average, and worst case, respectively. More significantly, the latency of PP-CORDIC in the worst case is 1.1X lower than that of the Altera's commercial floating-point sine and cosine IP cores.

We propose a Simulink model of a ring oscillator using saturating integrators. The oscillator's period is tuned via the saturation time of the integrators. Thus, timing jitters due to white and flicker noises are easily introduced into the model, enabling an efficient phase noise evaluation before transistor-level circuit design.

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.

Border Gateway Protocol (BGP), with its advantages in routing isolation support and mature application, is a promising candidate to integrate satellite systems into the terrestrial IP network. However, with more and more ground stations accessing satellites by BGP, a significant amount of routing overhead can be produced on limited satellite links, especially for geostationary satellite networks with thousands of accessing terminals in extremely large areas. To solve this challenge, multicast transport of BGP was proposed, which takes advantage of the inherent broadcast property of wireless channels. However, its performance can be seriously degraded when interfered with the environment. In this paper, NCSR (Network Coding for Satellite network BGP Routing transport) [1] is explored in depth. Unlike existing counterparts, NCSR pays more attention to the lossy space links and can achieve reliability with more bandwidth savings. A greedy based coding algorithm is proposed to realize the network coding operation. To demonstrate the efficiency of NCSR, we conduct theoretical analyses and extensive simulations in typical scenarios of satellite systems. Simulation results show that NCSR can greatly reduce the bandwidth usage while achieving comparable latency. Discussions on practical considerations when applying network coding method for reliability assurance are also presented in detail.

In this paper we study the structure of self-dual cyclic codes over the ring $Lambda= Z_4+uZ_4$. The ring Λ is a local Frobenius ring but not a chain ring. We characterize self-dual cyclic codes of odd length n over Λ. The results can be used to construct some optimal binary, quaternary cyclic and self-dual codes.

We propose a pre-filtering system for blind equalization in order to separate orthogonal frequency division multiplexing (OFDM) symbols in a multiple-input multiple-output (MIMO) - OFDM system. In a conventional blind MIMO-OFDM equalization without the pre-filtering system, there is a possibility that originally transmitted streams are permutated, resulting in the receiver being unable to retrieve desired signals. We also note that signal permutation is different for each subcarrier. In order to solve this problem, each transmitted stream of the proposed MIMO-OFDM system is pre-filtered by a unique allpass filter. In this paper, the pre-filter is referred to as transmit tagging filter (TT-Filter). At a receiver, an inverse filter of the TT-filter is used to blindly equalize a MIMO channel without permutation problem. Further, in order to overcome the issue of phase ambiguity, this paper introduces blind phase compensation.

Ground-truth identification - the process, which infers the most probable labels, for a certain dataset, from crowdsourcing annotations - is a crucial task to make the dataset usable, e.g., for a supervised learning problem. Nevertheless, the process is challenging because annotations from multiple annotators are inconsistent and noisy. Existing methods require a set of data sample with corresponding ground-truth labels to precisely estimate annotator performance but such samples are difficult to obtain in practice. Moreover, the process requires a post-editing step to validate indefinite labels, which are generally unidentifiable without thoroughly inspecting the whole annotated data. To address the challenges, this paper introduces: 1) Attenuated score (A-score) - an indicator that locally measures annotator performance for segments of annotation sequences, and 2) label aggregation method that applies A-score for ground-truth identification. The experimental results demonstrate that A-score label aggregation outperforms majority vote in all datasets by accurately recovering more labels. It also achieves higher F1 scores than those of the strong baselines in all multi-class data. Additionally, the results suggest that A-score is a promising indicator that helps identifying indefinite labels for the post-editing procedure.

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.

This letter presents a Preferable Mad-y (PMad-y) turn model and Low-cost Adaptive and Fault-tolerant Routing (LAFR) method that use one and two virtual channels along the X and Y dimensions for 2D mesh Network-on-Chip (NoC). Applying PMad-y rules and using the link status of neighbor routers within 2-hops, LAFR can tolerate multiple faulty links and routers in more complicated faulty situations and impose the reliability of network without losing the performance of network. Simulation results show that LAFR achieves better saturation throughput (0.98% on average) than those of other fault-tolerant routing methods and maintains high reliability of more than 99.56% on average. For achieving 100% reliability of network, a Preferable LAFR (PLAFR) is proposed.

In this paper, a Boolean algebra approach is proposed to encode various acceptability semantics for abstract argumentation frameworks, where each semantics can be equivalently encoded into several Boolean constraint models based on Boolean matrices and a family of Boolean operations between them. Then, we show that these models can be easily translated into logic programs, and can be solved by a constraint solver over Boolean variables. In addition, we propose some querying strategies to accelerate the calculation of the grounded, stable and complete extensions. Finally, we describe an experimental study on the performance of our encodings according to different semantics and querying strategies.

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.

We report on the fabrication of a magnetic metallic contaminant detector using multi-channel high-Tc RF-SQUIDs (superconducting quantum interference devices) for large packaged food. For food safety finding small metallic contaminants is an important issue for a food manufacturer. Hence, a detection method for small sized contaminants is required. Some detection systems for food inspection using high-Tc SQUIDs have been reported to date. The system described here is different from the previous systems in its permitted size for inspection, being larger at 150mm in height × 300mm in width. For inspection of large sized food packages, improvement of the signal to noise ratio (SNR) is an important issue because the signal intensity is inversely proportional to the cube of the distance between the SQUID sensor and the object. Therefore a digital filter was introduced and its parameters were optimized. As a result, a steel ball as small as 0.5mm in diameter at a stand-off distance of 167mm was successfully detected with more than SNR = 3.3.