Two simple and high performance multichannel distributed queue dual bus (MDQDB) protocols based on the wavelength division multiplexing (WDM) network technology are proposed, and the network architecture and operations are presented. To be suited for a high speed network, they inherit the advantages of the DQDB protocol such as node simplicity, network flexibility and distributed operations of individual nodes. The network capacity can also be greatly increased with marginal increase of node complexity. Simulation has been done to estimate the performances such as throughput and average access delays for individual nodes. The influence of the bandwidth balancing mechanism on the two protocols is considered at medium, high, and overload conditions. We also investigate the fairness characteristics in asymptotic conditions for various initial states.

MEMS storage devices are new non-volatile secondary storages that have outstanding advantages over magnetic disks. MEMS storage devices, however, are much different from magnetic disks in the structure and access characteristics in the following ways. They have thousands of heads called probe tips and provide the following two major access facilities: (1) flexibility : freely selecting a set of probe tips for accessing data, (2) parallelism: simultaneously reading and writing data with the set of probe tips selected. Due to these characteristics, it is nontrivial to find data placements that fully utilize the capability of MEMS storage devices. In this paper, we propose a simple logical model called the Region-Sector (RS) model that abstracts major characteristics affecting data retrieval performance, such as flexibility and parallelism, from the physical MEMS storage model. We also suggest heuristic data placement strategies based on the RS model. To show the usability of the RS model, we derive new data placements for relational data and two-dimensional spatial data by using these strategies. Experimental results show that the proposed data placements improve the data retrieval performance by up to 4.7 times for relational data and by up to 18.7 times for two-dimensional spatial data of approximately 320 Mbytes compared with those of existing data placements. Further, these improvements are expected to be more marked as the database size grows.

We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).

Internal user threats such as information leakage or system destruction can cause significant damage to the organization, however it is very difficult to prevent or detect this attack in advance. In this paper, we propose an anomaly-based insider threat detection method with local features and global statistics over the assumption that a user shows different patterns from regular behaviors during harmful actions. We experimentally show that our detection mechanism can achieve superior performance compared to the state of the art approaches for CMU CERT dataset.

To implement a fast and reliable question-answering system in Korean, we propose a two-pass answer indexer using co-occurrence information between answer candidates and adjacent content words. The two-pass indexer scans documents twice for obtaining local scores and global scores. Then, the two-pass indexer calculates the degrees of association between answer candidates and co-occurring content words. Using this technique, the proposed QA system shortens the response time and enhances the precision.

The objective is to generate a suboptimal weight vector for an adaptive array system operating in a multipath fading CDMA (Code Division Multiple Access) channel. The total computational load of the proposed procedure is about including the update of autocovariance matrix as well as the weight update itself, where N is the number of antenna elements. The performance of the proposed array system is shown in terms of BER (Bit Error Rate), allowable capacity, and convergence rate in practical CDMA signal environments such as IS95 and IS2000 1X.

A 12 Gb/s 10-level pulse amplitude modulation (PAM) serial-link transmitter was implemented using a 0.18 µm CMOS process. The proposed 10-PAM transmitter achieves a channel efficiency of 4 bit/symbol by dual-mode amplitude modulations using 10 differential-mode levels and 3 common-mode levels. The measured maximum data-rate was 12 Gb/s over 0.7-m cable and 2-cm printed circuit board (PCB) traces. The entire transmitter consumes 432 mW such that the figure of merit of the transmitter is 36 pJ/bit. The present work demonstrates the greater channel efficiency of 4 bit/symbol than the currently reported multi-level PAM transmitters.

In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a way that the SAP has the same value of GSAP in the case of speech absence while it is maintained to its original value when the speech is present. Moreover, for improving the performance of the SAP's at voice tails (transition periods from speech to silence), we revise the SAP's using a hang-over scheme based on the hidden Markov model (HMM). In addition, we suggest a robust noise update algorithm in which the noise power is estimated not only in the periods of speech absence but also during speech activity based on soft decision. Also, for improving the SAP determination and noise update routines, we present a new signal to noise ratio (SNR) concept which is called the predicted SNR in this paper. Moreover, we demonstrate that the discrete cosine transform (DCT) enhances the accuracy of the SAP estimation. A number of tests show that the proposed method which is called the speech enhancement based on soft decision (SESD) algorithm yields better performance than the conventional approaches.

Nonnegative matrix factorization (NMF) is an unsupervised technique to represent nonnegative data as linear combinations of nonnegative bases, which has shown impressive performance for source separation. However, its source separation performance degrades when one signal can also be described well with the bases for the interfering source signals. In this paper, we propose a discriminative NMF (DNMF) algorithm which exploits the reconstruction error for the interfering signals as well as the target signal based on target bases. The objective function for training the bases is constructed so as to yield high reconstruction error for the interfering source signals while guaranteeing low reconstruction error for the target source signals. Experiments show that the proposed method outperformed the standard NMF and another DNMF method in terms of both the perceptual evaluation of speech quality score and signal-to-distortion ratio in various noisy environments.