Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation, owing to its advantages over conventional versions. However the performance of compressive sensing (CS)-based estimation methods degrades when the true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this letter, on the basis of the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.

In this paper, we investigate the security property of RSA when some middle bits of the private key d are known to an attacker. Using the technique of unravelled linearization, we present a new attack on RSA with known middle bits, which improves a previous result under certain circumstance. Our approach is based on Coppersmith's method for finding small roots of modular polynomial equations.

A terahertz-wave communication system directly connected to an optical fiber network is promising for application to future mobile backhaul and fronthaul links. The possible broad bandwidth in the terahertz band is useful for high-speed signal transmission as well as radio-space encapsulation to the high-frequency carrier. In both cases, the low-latency feature becomes important to enhance the throughput in mobile communication and is realized by waveform transport technology without any digital-signal-processing-based media conversion. A highly precise optical frequency comb signal generated by optical modulation and the vector signal demodulation technology adopted from advanced optical fiber communication technologies help perform modulation and demodulation with impairment compensation at just the edges of the link. Terahertz wave, radio over fiber, waveform transport, coherent detection, multilevel modulation, radio on radio.

This report describes an all-digital phase-locked loop (ADPLL) using a temperature compensated settling time reduction technique. The novelty of this work is autonomous oscillation control word estimation without a look-up table or memory circuits. The proposed ADPLL employs a multi-phase digitally controlled oscillator (DCO). In the proposed estimation method, the optimum oscillator tuning word (OTW) is estimated from the DCO frequency characteristic in the setup phase of ADPLL. The proposed ADPLL, which occupies 0.27×0.36mm2, is fabricated by a 65 nm CMOS process. The temperature compensation PLL controller (TCPC) is implemented using an FPGA. Although the proposed method has 20% area overhead, measurement results show that the 47% settling time is reduced. The average settling time at 25°C is 3µs. The average reduction energy is at least 42% from 0°C to 100°C.

An earthquake is a destructive natural disaster, which cannot be predicted accurately and causes devastating damage and losses. In fact, many of the damages can be prevented if people know what to do during and after earthquakes. Earthquake education is the most important method to raise public awareness and mitigate the damage caused by earthquakes. Generally, earthquake education consists of conducting traditional earthquake drills in schools or communities and experiencing an earthquake through the use of an earthquake simulator. However, these approaches are unrealistic or expensive to apply, especially in underdeveloped areas where earthquakes occur frequently. In this paper, an earthquake drill simulation system based on virtual reality (VR) technology is proposed. A User is immersed in a 3D virtual earthquake environment through a head mounted display and is able to control the avatar in a virtual scene via Kinect to respond to the simulated earthquake environment generated by SIGVerse, a simulation platform. It is a cost effective solution and is easy to deploy. The design and implementation of this VR system is proposed and a dormitory earthquake simulation is conducted. Results show that powerful earthquakes can be simulated successfully and the VR technology can be applied in the earthquake drills.

This paper reviews a recent progress in terahertz wireless communications enabled by photonics technologies. After briefly summarizing transceiver configurations with electronics and photonics technologies, photonics-based approaches to achieving over 100-Gbit/s data rates are discussed. Then, some of our updated results on real-time wireless transmission experiments using discrete components are shown at data rates up to 50 Gbit/s. Finally, integration technologies are described by demonstrating latest advances in integrated optical sources and transmitters.

Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.

A threshold secret sharing scheme can realize reliable delivery of important content using redundant routes through a network. Furthermore, multicast delivery of threshold secret shared content can achieve efficient resource utilization thanks to the application of multicast and network coding techniques to multiple pieces of the content. Nevertheless, a tradeoff exists between reliability and efficiency if multicast content delivery uses network coding. This paper proposes a flexible multicast delivery scheme for threshold secret shared content that can control the tradeoff between reliability and efficiency. The proposed scheme classifies all the pieces obtained from the original content into multiple groups, and each group is subjected to network coding independently. An optimization procedure is proposed for the multicast delivery scheme, which involves two different heuristic delivery route computation methods applicable to large-scale networks. Evaluation results show that the optimized multicast delivery scheme adopting an appropriate grouping method and classifying the pieces into a suitable number of groups can minimize the required link bandwidth while satisfying a specified content loss probability requirement.

In this paper, we propose SimCS (similarity based on contribution scores) to compute the similarity of scientific papers. For similarity computation, we exploit a notion of a contribution score that indicates how much a paper contributes to another paper citing it. Also, we consider the author dominance of papers in computing contribution scores. We perform extensive experiments with a real-world dataset to show the superiority of SimCS. In comparison with SimCC, the-state-of-the-art method, SimCS not only requires no extra parameter tuning but also shows higher accuracy in similarity computation.

In this paper, we propose a method for designing finite impulse response (FIR) filters with canonic signed digit (CSD) coefficients using particle swarm optimization (PSO). In such a design problem, a large number of local minimums appear in an evaluation function for the optimization. An updating procedure of PSO tends to stagnate around such local minimums and thus indicates a premature convergence property. Therefore, a new framework for avoiding such a situation is proposed, in which the evaluation function is modified around the stagnation point. Several design examples are shown to present the effectiveness of the proposed method.

The conventional non-negative matrix factorization (NMF)-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. With the probabilistic estimation of whether the speech is present or not in a certain frame, this letter proposes a speech enhancement algorithm incorporating the speech presence probability (SPP) obtained via noise estimation to the NMF process. To take advantage of both the NMF-based and statistical model-based approaches, the final enhanced speech is achieved by applying a statistical model-based filter to the output of the SPP weighted NMF. Objective evaluations using perceptual evaluation of speech quality (PESQ) on TIMIT with 20 noise types at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed algorithm over the conventional NMF and statistical model-based baselines.

Non-volatile memory has many advantages such as high density and low leakage power but it consumes larger writing energy than SRAM. It is quite necessary to reduce writing energy in non-volatile memory design. In this paper, we propose write-reduction codes based on error correcting codes and reduce writing energy in non-volatile memory by decreasing the number of writing bits. When a data is written into a memory cell, we do not write it directly but encode it into a codeword. In our write-reduction codes, every data corresponds to an information vector in an error-correcting code and an information vector corresponds not to a single codeword but a set of write-reduction codewords. Given a writing data and current memory bits, we can deterministically select a particular write-reduction codeword corresponding to the data to be written, where the maximum number of flipped bits are theoretically minimized. Then the number of writing bits into memory cells will also be minimized. Experimental results demonstrate that we have achieved writing-bits reduction by an average of 51% and energy reduction by an average of 33% compared to non-encoded memory.

A conventional HMM-based speech synthesis system for Hanoi Vietnamese often suffers from hoarse quality due to incomplete F0 parameterization of glottalized tones. Since estimating F0 from glottalized waveform is rather problematic for usual F0 extractors, we propose a pitch marking algorithm where pitch marks are propagated from regular regions of a speech signal to glottalized ones, from which complete F0 contours for the glottalized tones are derived. The proposed F0 parameterization scheme was confirmed to significantly reduce the hoarseness whilst slightly improving the tone naturalness of synthetic speech by both objective and listening tests. The pitch marking algorithm works as a refinement step based on the results of an F0 extractor. Therefore, the proposed scheme can be combined with any F0 extractor.

Network coding on the physical-layer has recently been widely discussed as a potentially promising solution to the wireless access problem in a relay network. However, the existing research on physical-layer network coding (PNC), usually assumes that the symbol timing of the nodes is fully synchronized and hardly investigates the unavoidable symbol timing errors. Similar to many telecommunication systems, symbol timing plays a critical role in PNC and precise alignment has to be provided for the encoding. In this work, we propose a novel symbol timing algorithm with a low oversampling factor (samples per symbol) based on the a priori knowledge of the transmitted pulse shape. The proposed algorithm has the dual advantages of the low oversampling rate and high precision. The mean square error (MSE) performance is verified by simulations to be at least one order of magnitude better than that of the conventional optimum phase (OP) algorithm for a signal noise ratio (SNR) greater than 5dB.

Almost sure convergence coding theorems of one-shot and multi-shot Tunstall codes are proved for stationary memoryless sources. Coding theorem of one-shot Tunstall code is proved in the case that the leaf count of Tunstall tree increases. On the other hand, coding theorem is proved for multi-shot Tunstall code with increasing parsing count, under the assumption that the Tunstall tree grows as the parsing proceeds. In this result, it is clarified that the theorem for the one-shot Tunstall code is not a corollary of the theorem for the multi-shot Tunstall code. In the case of the multi-shot Tunstall code, it can be regarded that the coding theorem is proved for the sequential algorithm such that parsing and coding are processed repeatedly. Cartesian concatenation of trees and geometric mean of the leaf counts of trees are newly introduced, which play crucial roles in the analyses of multi-shot Tunstall code.

In this paper, we propose a localization algorithm that uses the time difference of arrival (TDOA) and the angle of arrival (AOA). The problem is formulated in a hybrid linear matrix equation. TDOA and AOA measurements are used for estimating the target's position. Although it is known that the accuracy of TDOA based localization is superior to that of AOA based localization, TDOA based localization has a poor vertical accuracy in deteriorated geometrical conditions. This paper, therefore, proposes a localization algorithm in which the vertical position is estimated by AOA measurements and the horizontal position is estimated by TDOA measurement in order to achieve high location accuracy in three dimensions. In addition, the Lagrange multipliers are obtained efficiently and robustly. The simulation analysis shows that the proposed constrained linear squares (CLS) algorithm is an unbiased estimator, and that it approaches the Cramer-Rao lower bound (CRLB) when the measurement noise and the sensor's location errors are sufficiently small.

In this letter, we propose a novel supervised pre-training technique for deep neural network (DNN)-hidden Markov model systems to achieve robust speech recognition in adverse environments. In the proposed approach, our aim is to initialize the DNN parameters such that they yield abstract features robust to acoustic environment variations. In order to achieve this, we first derive the abstract features from an early fine-tuned DNN model which is trained based on a clean speech database. By using the derived abstract features as the target values, the standard error back-propagation algorithm with the stochastic gradient descent method is performed to estimate the initial parameters of the DNN. The performance of the proposed algorithm was evaluated on Aurora-4 DB, and better results were observed compared to a number of conventional pre-training methods.

In many radio frequency identification (RFID) applications, the reader identifies the tags in its scope repeatedly. For these applications, many algorithms, such as an adaptive binary splitting algorithm (ABS), a single resolution blocking ABS (SRB), a pair resolution blocking ABS (PRB) and a dynamic blocking ABS (DBA) have been proposed. All these algorithms require the staying tags to reply with their IDs to be recognized by the reader. However, the IDs of the staying tags are stored in the reader in the last identification round. The reader can verify the existence of these tags when identifying them. Thus, we propose an anti-collision algorithm with short reply for RFID tag identification (ACSR). In ACSR, each staying tag emits a short reply to indicate its continued existence. Therefore, the data amount transmitted by staying tags is reduced significantly. The identification rate of ACSR is analyzed in this paper. Finally, simulation and analysis results show that ACSR greatly outperforms ABS, SRB and DBA in terms of the identification rate and average amount of data transmitted by a tag.

Superconducting single-flux-quantum (SFQ) device is an emerging device which can realize digital circuits with high switching speed and low power consumption. In SFQ digital circuits, voltage pulses are used for carrier of information, and the representation of logic values is different from that of CMOS circuits. Design methods exclusive to SFQ circuits have been developed. In this paper, we present timing analysis and functional verification methods for SFQ circuits based on new timing model which we call delay-based time frame model. Assuming that possible pulse arrival is periodic, the model defines comprehensive time frames and representation of logic values. In static timing analysis, expected pulse arrival time is checked based on the model, and the order among pulse arrival times is calculated for each logic gate. In functional verification, the circuit behavior is abstracted in a form similar to a synchronous sequential circuit using the order of pulse arrival times, and then the behavior is verified using formal verification tools. Using our proposed methods, we can verify the functional behavior of SFQ circuits with complex clocking scheme, which appear often in practical design but cannot be dealt with in existing verification method. Experimental results show that our method can be applied to practical designs.

The tree-based routing approach has been known as an efficient method for node mobility management and data packet transmission between two long-distance parties; however, its parameter adjustment must balance control overhead against the convergence speed of topology information according to node mobility. Meanwhile, location-based routing works more efficiently when the distance between the source and destination is relatively short. Therefore, this paper proposes a roadside unit (RSU) based hybrid routing protocol, called RSU-HRP that combines the strengths of both protocols while offsetting their weaknesses. In RSU-HRP, the tree construction is modified to take into account the link and route quality to construct a robust and reliable tree against high node mobility, and an optimized broadcast algorithm is developed to reduce control overhead induced by the advertisement message periodically sent from a roadside unit. In addition, the two routing methods are selectively used based on the computed distance in hops between a source and a destination. Simulation results show that RSU-HRP far outperforms TrafRoute in terms of packet delivery ratio, end-to-end delay, and control overhead in both Vehicle-to-Infrastructure and Vehicle-to-Vehicle communication models.