With the process scaling, the leakage current reduction has been the primary design concerns in a nanometer-era VLSI circuit. In this paper, we propose a new lithography process-aware edge effects correction method to reduce the leakage current in the shallow trench isolation (STI). We construct the various test structures to model Ileakage and Ileakage_fringe which represent the leakage currents at the center and edge of the transistor, respectively. The layout near the active edge is modified using the look-up table generated by the calibrated analytic model. On average, the proposed edge effects correction method reduces the leakage current by 18% with the negligible decrease of the drive current at sub-40nm DRAM device.

Bugs are becoming unavoidable in complex integrated circuit design. It is imperative to identify the bugs as soon as possible through post-silicon debug. For post-silicon debug, observability is one of the biggest challenges. Scan-based debug mechanism provides high observability by reusing scan chains. However, it is not feasible to scan dump cycle-by-cycle during program execution due to the excessive time required. In fact, it is not necessary to scan out the error-free states. In this paper, we introduce Suspect Window to cover the clock cycle in which the bug is triggered. Then, we present an efficient approach to determine the suspect window. Based on Suspect Window, we propose a novel debug mechanism to locate the bug both temporally and spatially. Since scan dumps are only taken in the suspect window with the proposed mechanism, the time required for locating the bug is greatly reduced. The approaches are evaluated using ISCAS'89 and ITC'99 benchmark circuits. The experimental results show that the proposed mechanism can significantly reduce the overall debug time compared to scan-based debug mechanism while keeping high observability.

We propose a new speech enhancement method suitable for mobile devices used in the presence of various types of noise. In order to achieve high-performance speech recognition and auditory perception in mobile devices, various types of noise have to be removed under the constraints of a space-saving microphone arrangement and few computational resources. The proposed method can reduce both the directional noise and the diffuse noise under the abovementioned constraints for mobile devices by employing a square microphone array and conducting low-computational-cost processing that consists of multiple null beamforming, minimum power channel selection, and Wiener filtering. The effectiveness of the proposed method is experimentally verified in terms of speech recognition accuracy and speech quality when both the directional noise and the diffuse noise are observed simultaneously; this method reduces the number of word errors and improves the log-spectral distances as compared to conventional methods.

The access privileges in distributed systems can be effectively organized as a partial-order hierarchy that consists of distinct security classes, and the access rights are often designated with certain temporal restrictions. The time-bound hierarchical key assignment problem is to assign distinct cryptographic keys to distinct security classes according to their privileges so that users from a higher class can use their class key to derive the keys of lower classes, and these keys are time-variant with respect to sequentially allocated temporal units called time slots. In this paper, we present the involved principle, survey the state of the art, and particularly, look into two representative approaches to time-bound hierarchical key assignment for in-depth case studies.

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

An improved bidirectional search algorithm for computing the weight spectrum of convolutional codes is presented. This algorithm does not employ the column distance function of a code which plays an important role in the original bidirectional search algorithm. We show the proposed algorithm can reduce computaion time for obtaining the weigth spectrum of convolutional codes significantly compared with that of the bidirectional search algorithm.

Automated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the co-location relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.

This paper describes an efficient image enhancement method based on the Multi-Scale Retinex (MSR) approach for pre-processing of video applications. The processing amount is drastically reduced to 4 orders less than that of the original MSR, and 1 order less than the latest fast MSR method. For the efficient processing, our proposed method employs multi-stage and multi-rate filter processing which is constructed by a x-y separable and polyphase structure. In addition, the MSR association is effectively implemented during the above multi-stage processing. The method also modifies a weighting function for enhancement to improve color rendition of bright areas in an image. A variety of evaluation results show that the performance of our simplified method is similar to those of the original MSR, in terms of visual perception, contrast enhancement effects, and hue changes. Moreover, experimental results show that pre-processing of the proposed method contributes to clear foreground object separation.

In a society preoccupied with gradual erosion of electronic privacy, loss of privacy in the current Domain Name System is an important issue worth considering. In this paper, we first review the DNS and some security & privacy threats to make average users begin to concern about the significance of privacy preservation in DNS protocols. Then, by an careful survey of four noise query generation based existing privacy protection approaches, we analyze some benefits and limitations of these proposals in terms of both related performance evaluation results and theoretic proofs. Finally, we point out some problems that still exist for research community's continuing efforts in the future.

We designed and fabricated 4H-SiC PIN avalanche photodiodes (APD) for UV detection. The thickness of an intrinsic layer in a PIN structure was optimized in order to achieve the highest quantum efficiency at the wavelength of interest. The optimized 4H-SiC PIN APDs exhibited a maximum external quantum efficiency of >80% at the wavelength of 280 nm and a gain greater than 40000. Both electrical and optical characteristics of the fabricated APDs were in agreement with those predicted from simulation.

Quantum cryptography has become a subject of widespread interest. In particular, quantum key distribution, which provides a secure key agreement by using quantum systems, is believed to be the most important application of quantum cryptography. Quantum key distribution has the potential to achieve the "unconditionally" secure infrastructure. We also have many cryptographic tools that are based on "modern cryptography" at the present time. They are being used in an effort to guarantee secure communication over open networks such as the Internet. Unfortunately, their ultimate efficacy is in doubt. Quantum key distribution systems are believed to be close to practical and commercial use. In this paper, we discuss what we should do to apply quantum cryptography to our communications. We also discuss how quantum key distribution can be combined with or used to replace cryptographic tools based on modern cryptography.

Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.

We fabricated and examined current limiting effect for InP Gunn diodes with stable depletion layer mode operation of diodes for high efficiency Gunn oscillators. Current limiting at the cathode was achieved by a shallow Schottky barrier at the interface. We discussed fabrication procedure, the results for negative differential resistance and rf tests for InP Gunn diodes. It was shown that the fabricated Gunn diodes have the output power of 10.22 dBm at a frequency of 90.13 GHz. Its input voltage and corresponding current were 8.55 V and 252 mA, respectively.

We have studied transmission property of electron in vertical MOSFET (V-MOSFET) from the viewpoint of quantum electro-dynamics. To obtain the intuitive picture of electron transmission property through channel of the V-MOSFET, we solve the time-dependent Schrodinger equation in real space by employing the split operator method. We injected an electron wave packet into the body of the V-MOSFET from the source, and traced the time-development of electron-wave function in the body and drain region. We successfully showed that the electron wave function propagates through the resonant states of the body potential. Our suggested approaches open the quantative and intuitive discussion for the carrier dynamics in the V-MOSFET on quantum limit.

System implementation for e-Government initiatives should be reliable. Unreliable system implementation could, on the one hand, be insufficient to fulfill basic system requirements, and more seriously on the other hand, break the trust of citizens on governments. The objective of this paper is to advocate the use of formal methods in general, the OTS/CafeOBJ method in particular in this paper, to help develop reliable system implementation for e-Government initiatives. An experiment with the OTS/CafeOBJ method on an e-Government messaging framework proposed for providing citizens with seamless public services is described to back up our advocation. Two previously not well-clarified problems of the framework and their potential harm realized in this experiment are reported, and possible ways of revisions to the framework are suggested as well. The revisions are proved to be sufficient for making the framework satisfy certain desired properties.

Differing from the long-term prediction used in the modern speech codec, the standard of the internet low bit rate codec (iLBC) independently encodes the residual of the linear predictive coding (LPC) frame by frame. In this paper, a complexity scalability design is proposed for the coding of the dynamic codebook search in the iLBC speech codec. In addition, a trade-off between the computational complexity and the speech quality can be achieved by dynamically setting the parameter of the proposed approach. Simulation results show that the computational complexity can be effectively reduced with imperceptible degradation of the speech quality.

A dependency structure interprets modification relationships between words or phrases and is recognized as an important element in semantic information analysis. With the conventional approaches for extracting this dependency structure, it is assumed that the complete sentence is known before the analysis starts. For spontaneous speech data, however, this assumption is not necessarily correct since sentence boundaries are not marked in the data. Although sentence boundaries can be detected before dependency analysis, this cascaded implementation is not suitable for online processing since it delays the responses of the application. To solve these problems, we proposed a sequential dependency analysis (SDA) method for online spontaneous speech processing, which enabled us to analyze incomplete sentences sequentially and detect sentence boundaries simultaneously. In this paper, we propose an improved SDA integrating a labeling-based sentence boundary detection (SntBD) technique based on Conditional Random Fields (CRFs). In the new method, we use CRF for soft decision of sentence boundaries and combine it with SDA to retain its online framework. Since CRF-based SntBD yields better estimates of sentence boundaries, SDA can provide better results in which the dependency structure and sentence boundaries are consistent. Experimental results using spontaneous lecture speech from the Corpus of Spontaneous Japanese show that our improved SDA outperforms the original SDA with SntBD accuracy providing better dependency analysis results.

A hash chain H for a one-way hash function h() is a sequence of hash values < v0, v1, ..., vn >, where v0 is a public value, vn a secret value, and vi = h(vi+1). A hash chain traversal T computes and outputs the hash chain H, returning vi in time period (called round) i for 1 ≤ i ≤ n. While previous hash chain traversal algorithms were designed to output all hash values vi (1 ≤ i ≤ n) in order, there are applications where every m-th hash value (i.e., vm, v2m, v3m, ...) is required to be output. We introduce a hash chain traversal algorithm that selectively outputs every m-th hash value efficiently. The main technique is a transformation from a hash chain traversal algorithm outputting every hash value into that outputting every m-th hash value. Compared with the direct use of previous hash chain traversal algorithms, our proposed method requires less memory storages and computational costs.

In this paper, we propose a robust room inverse filtering algorithm for speech dereverberation based on a kurtosis maximization. The proposed algorithm utilizes a new normalized kurtosis function that nonlinearly maps the input kurtosis onto a finite range from zero to one, which results in a kurtosis warping. Due to the kurtosis warping, the proposed algorithm provides more stable convergence and, in turn, better performance than the conventional algorithm. Experimental results are presented to confirm the robustness of the proposed algorithm.

This paper proposes a robust state observer for multi-input multi-output LTI systems. Unknown inputs of polynomial form and high-frequency measurement noises are considered in the system model. The unknown inputs and the noises are not in the same form. Multiple integrations of both the observer error signal and the measurement output are used for the observer design. The existence condition of the proposed observer is shown to be the same as that of the proportional-integral (PI) observer. Computer simulations show the effectiveness of the proposed observer.