This letter deals with an audio declipping problem and proposes a multiple matrix rank minimization approach. We assume that short-time audio signals satisfy the autoregressive (AR) model and formulate the declipping problem as a multiple matrix rank minimization problem. To solve this problem, an iterative algorithm is provided based on the iterative partial matrix shrinkage (IPMS) algorithm. Numerical examples show its efficiency.

Human action recognition in videos draws huge research interests in computer vision. The Bag-of-Word model is quite commonly used to obtain the video level representations, however, BoW model roughly assigns each feature vector to its nearest visual word and the collection of unordered words ignores the interest points' spatial information, inevitably causing nontrivial quantization errors and impairing improvements on classification rates. To address these drawbacks, we propose an approach for action recognition by encoding spatio-temporal log Euclidean covariance matrix (ST-LECM) features within the low-rank and sparse representation framework. Motivated by low rank matrix recovery, local descriptors in a spatial temporal neighborhood have similar representation and should be approximately low rank. The learned coefficients can not only capture the global data structures, but also preserve consistent. Experimental results showed that the proposed approach yields excellent recognition performance on synthetic video datasets and are robust to action variability, view variations and partial occlusion.

The existing methods to estimate satellite attitude by using radar cross section (RCS) sequence suffer from problems such as low precision, computation complexity, etc. To overcome these problems, a novel model of satellite attitude estimation by the local maximum points of the RCS sequence is established and can reduce the computational time by downscaling the dimension of the feature vector. Moreover, a particle swarm optimization method is adopted to improve efficiency of computation. Numerical simulations show that the proposed method is robust and efficient.

Erasure codes have been considered as one of the most promising techniques for data reliability enhancement and storage efficiency in modern distributed storage systems. However, erasure codes often suffer from a time-consuming coding process which makes them nearly impractical. The opportunity to solve this problem probably rely on the parallelization of erasure-code-based application on the modern multi-/many-core processors to fully take advantage of the adequate hardware resources on those platforms. However, the complicated data allocation and limited I/O throughput pose a great challenge on the parallelization. To address this challenge, we propose a general multi-threaded parallel coding approach in this work. The approach consists of a general multi-threaded parallel coding model named as MTPerasure, and two detailed parallel coding algorithms, named as sdaParallel and ddaParallel, respectively, adapting to different I/O circumstances. MTPerasure is a general parallel coding model focusing on the high level data allocation, and it is applicable for all erasure codes and can be implemented without any modifications of the low level coding algorithms. The sdaParallel divides the data into several parts and the data parts are allocated to different threads statically in order to eliminate synchronization latency among multiple threads, which improves the parallel coding performance under the dummy I/O mode. The ddaParallel employs two threads to execute the I/O reading and writing on the basis of small pieces independently, which increases the I/O throughput. Furthermore, the data pieces are assigned to the coding thread dynamically. A special thread scheduling algorithm is also proposed to reduce thread migration latency. To evaluate our proposal, we parallelize the popular open source library jerasure based on our approach. And a detailed performance comparison with the original sequential coding program indicates that the proposed parallel approach outperforms the original sequential program by an extraordinary speedups from 1.4x up to 7x, and achieves better utilization of the computation and I/O resources.

We present a design approach and analysis of a multimode stripline resonator (MSR). Furthermore, a bandpass filter (BPF) using a single MSR is presented. MSR has three fundamental modes, incorporating two transmission resonance modes and one quasi-lumped component (LC) resonance mode. The resonant frequencies and unloaded Q factors of those modes are theoretically derived by transmission modes and LC modes. By our equations, it is also explained that the resonant frequencies can be shown to be easily handled by an increase and decrease in the number of via holes. These frequencies calculated by our equations are in good agreement with those of 3-D simulations and measurements. Finally, design approach of a narrow bandpass filter using our resonator is introduced. Good agreement between measured and computed result is obtained.

The centralized controller of SDN enables a global topology view of the underlying network. It is possible for the SDN controller to achieve globally optimized resource composition and utilization, including optimized end-to-end paths. Currently, resource composition in SDN arena is usually conducted in an imperative manner where composition logics are explicitly specified in high level programming languages. It requires strong programming and OpenFlow backgrounds. This paper proposes declarative path composition, namely Compass, which offers a human-friendly user interface similar to natural language. Borrowing methodologies from Semantic Web, Compass models and stores SDN resources using OWL and RDF, respectively, to foster the virtualized and unified management of the network resources regardless of the concrete controller platform. Besides, path composition is conducted in a declarative manner where the user merely specifies the composition goal in the SPARQL query language instead of explicitly specifying concrete composition details in programming languages. Composed paths are also reused based on similarity matching, to reduce the chance of time-consuming path composition. The experiment results reflect the applicability of Compass in path composition and reuse.

This paper proposes a partial transmit sequences (PTS)-based PAPR reduction method and a phase factor estimation method without side information for OFDM systems with QPSK and 16QAM modulation. In the transmitter, an iterative algorithm that minimizes the p-norm of a transmitted signal determines phase factors to reduce PAPR. Unlike conventional methods, the phase factors are allowed to take continuous values in a limited range. In the receiver, the phase factor is blindly estimated by evaluating the phase differences between the equalizer's output and its closest constellation points. Simulation results show that the proposed PAPR reduction method is more computationally efficient than the conventional PTS. Moreover, the combined use of the two proposed methods achieves a satisfactory tradeoff between PAPR and BER by limiting the phase factors properly.

A grammar compression is a restricted context-free grammar (CFG) that derives a single string deterministically. The goal of a grammar compression algorithm is to develop a smaller CFG by finding and removing duplicate patterns, which is simply a frequent pattern discovery process. Any frequent pattern can be obtained in linear time; however, a huge working space is required for longer patterns, and the entire string must be preloaded into memory. We propose an online algorithm to address this problem approximately within compressed space. For an input sequence of symbols, a1,a2,..., let Gi be a grammar compression for the string a1a2…ai. In this study, an online algorithm is considered one that can compute Gi+1 from (Gi,ai+1) without explicitly decompressing Gi. Here, let G be a grammar compression for string S. We say that variable X approximates a substring P of S within approximation ratio δ iff for any interval [i,j] with P=S[i,j], the parse tree of G has a node labeled with X that derives S[l,r] for a subinterval [l,r] of [i,j] satisfying |[l,r]|≥δ|[i,j]|. Then, G solves the frequent pattern discovery problem approximately within δ iff for any frequent pattern P of S, there exists a variable that approximates P within δ. Here, δ is called the approximation ratio of G for S. Previously, the best approximation ratio obtained by a polynomial time algorithm was Ω(1/lg2|P|). The main contribution of this work is to present a new lower bound Ω(1/<*|S|lg|P|) that is smaller than the previous bound when lg*|S|

The conventional generalized spatial modulation (GSM) simultaneously activates multiple transmit antennas in order to improve the spectral efficiency of the original SM. In this letter, to lessen the hardware burden of the multiple RF chains, we provide a new scheme that is designed by combining the GSM scheme using only two active antennas with quaternary quasi-orthogonal sequences of a length of two. Compared with the other SM schemes, the proposed scheme has significant benefits in average error performances and/or their hardware complexities of the RF systems.

Peer-to-peer overlay networks can easily achieve a large-scale content sharing system on the Internet. Although unstructured peer-to-peer networks are suitable for finding entire partial-match content, flooding-based search is an inefficient way to obtain target content. When the shared content is semantically specified by a great number of attributes, it is difficult to derive the semantic similarity of peers beforehand. This means that content search methods relying on interest-based locality are more advantageous than those based on the semantic similarity of peers. Existing search methods that exploit interest-based locality organize multiple peer groups, in each of which peers with common interests are densely connected using short-cut links. However, content searches among multiple peer groups are still inefficient when the number of incident links at each peer is limited due to the capacity of the peer. This paper proposes a novel content search method that exploits interest-based locality. The proposed method can organize an efficient peer-to-peer network similar to the semantic small-world random graph, which can be organized by the existing methods based on the semantic similarity of peers. In the proposed method, topology transformation based on local link replacement maintains the numbers of incident links at all the peers. Simulation results confirm that the proposed method can achieve a significantly higher ratio of obtainable partial-match content than existing methods that organize peer groups.

An optimal design method for a sub-ranging Analog-to-Digital Converter (ADC) based on stochastic comparator is demonstrated by performing theoretical analysis of random comparator offset voltages. If the Cumulative Distribution Function (CDF) of the comparator offset is defined appropriately, we can calculate the PDFs of the output code and the effective resolution of a stochastic comparator. It is possible to model the analog-to-digital conversion accuracy (defined as yield) of a stochastic comparator by assuming that the correlations among the number of comparator offsets within different analog steps corresponding to the Least Significant Bit (LSB) of the output transfer function are negligible. Comparison with Monte Carlo simulation verifies that the proposed model precisely estimates the yield of the ADC when it is designed for a reasonable target yield of >0.8. By applying this model to a stochastic comparator we reveal that an additional calibration significantly enhances the resolution, i.e., it increases the Number of Bits (NOB) by ∼ 2 bits for the same target yield. Extending the model to a stochastic-comparator-based sub-ranging ADC indicates that the ADC design parameters can be tuned to find the optimal resource distribution between the deterministic coarse stage and the stochastic fine stage.

The k-edge-connectivity augmentation problem with multipartition constraints (kECAMP, for short) is defined by “Given a multigraph G=(V,E) and a multipartition π={V1,...,Vr} (r≥2) of V, that is, $V = igcup_{h = 1}^r V_h$ and Vi∩Vj=∅ (1≤i

A 2nd-order ΔΣAD modulator architecture is proposed to simplify the operation phase using ring amplifier and SAR quantizer. The proposed modulator architecture can guarantee the reset time for ring amplifier and relax the speed requirement on asynchronous SAR quantizer. The SPICE simulation results demonstrate the feasibility of the proposed 2nd-order ΔΣAD modulator in 90nm CMOS technology. Simulated SNDR of 95.70dB is achieved while a sinusoid -1dBFS input is sampled at 60MS/s for the bandwidth is BW=470kHz. The power consumption of the analog part in the modulator is 1.67mW while the supply voltage is 1.2V.

This paper proposes a circuit modeling technique for electrically-very-small devices, e.g. electrodes for intrabody communications, coils for wireless power transfer systems, high-frequency transformers, etc. The proposed technique is based on the method of moments and can be regarded as an improved version of the partial element equivalent circuit method.

This letter presents a 12-bit column-parallel hybrid two-step successive approximation register/single-slope analog-to-digital converter (SAR/SS ADC) for CMOS image sensor (CIS). For achieving a high conversion speed, a simple SAR ADC is used in upper 6-bit conversion and a conventional SS ADC is used in lower 6-bit conversion. To reduce the power consumption, a comparator is shared in each column, and a 6-bit ramp generator is shared by all columns. This ADC is designed in SMIC 0.18µm CMOS process. At a clock frequency of 22.7MHz, the conversion time is 3.2µs. The ADC has a DNL of -0.31/+0.38LSB and an INL of -0.86/+0.8LSB. The power consumption of each column ADC is 89µW and the ramp generator is 763µW.

This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.

In order to utilize higher frequency bands above 6GHz, which is an important technical challenge in fifth generation mobile systems, radio propagation channel properties in a large variety of deployment scenarios should be thoroughly investigated. The authors' group has been involved in a fundamental research project aimed at investigating multiple-input-multiple-output (MIMO) transmission performance and propagation channel properties at microwave frequency above 10GHz from 2009 to 2013, and since then they have been conducting measurement and modeling for high frequency bands. This paper aims at providing comprehensive tutorial of a whole procedure of channel modeling; multi-dimensional channel sounding, propagation channel measurement, analysis, and modeling, by introducing the developed MIMO channel sounders at high frequency bands of 11 and 60GHz and presenting some measurement results in a microcell environment at 11GHz. Furthermore, this paper identifies challenges in radio propagation measurements, and discusses current/future channel modeling issues as future works.

The growth of Internet traffic and the variety of traffic classes make network performance extremely difficult to evaluate. Even though most current methods rely on complex or costly hardware, recent research on bandwidth sharing has suggested the possibility of defining evaluation methods that simply require basic statistics on aggregated link utilization, such as mean and variance. This would greatly simplify monitoring systems as these statistics are easily calculable from Simple Network Management Protocol (SNMP) calls. However, existing methods require knowledge of certain fixed information about the network being monitored (e.g. link capacities). This is usually unavailable when the operator's view is limited to its share of leased links or when shared links carry traffic with different priorities. In this paper, departing from the analysis of aggregated link utilization statistics obtainable from SNMP requests, we propose a method that detects traffic degradation based on link utilization samples. It does not require knowledge of the capacity of the aggregated link or any other network parameters, giving network operators the possibility to control network performance in a more reliable and cost-effective way.

This paper presents the Received-Signal-Strength-Indicator (RSSI) based living-body radar, which uses only a single RF front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of the parasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase information of the signal. In this paper, we aim to estimate the direction of living-body. Experiments are carried out and it is found that most angular errors are within the limit of the angular width of the living-body.

In this paper, we consider a two-hop communication system with an amplify-and-forward (AF) relay under channel estimation errors. According to the channel quality of the link between the base station (BS) and the relay, we investigate two typical relay scenarios. We study the capacity performance for both In-Band Full-Duplex (IBFD) and Half-Duplex (HD) transmission modes. Moreover, we consider two operation modes of the user equipment (UE) for each scenario. Closed-form expressions of ergodic capacities with channel estimation errors are obtained for scenario-1. And we derive accurate approximations of ergodic capacities for scenario-2. Numerical experiments are conducted to verify the analytical results and show that our theoretical derivations are perfectly matched with the simulations. We show that with practical signal-to-noise ratio values and effective interference cancellation techniques, IBFD transmission is preferable in terms of capacity.