We consider a stabilization problem of a class of input-delayed nonlinear systems that have not only feedforward, but also some non-feedforward nonlinearity. While there are some existing results that deal with input-delayed non-feedforward nonlinear systems, they often assume a small input delay. It has been often the case that for a large input delay, the results are limited to only feedforward systems. In this letter, combined with the LMI approach in [3] and the reduction method in [5], we show that some feedforward and non-feedforward systems with a large delay in the input can be stabilized via the proposed controller.

Femtocell is considered a promising solution for indoor service enhancement in IEEE 802.16e cellular systems. However, the scanning scheme of IEEE 802.16e is not suitable for direct use in scanning femtocells in terms of efficiency and scan duration. In this paper, we propose an efficient scanning scheme for femtocells in IEEE 802.16e systems. The proposed scheme can achieve a lower scanning overhead by reducing the number of femtocells needed to be scanned. Numerical results show that the proposed scanning scheme can reduce the control message overhead and the scan duration.

This work describes a 12-bit 100 MS/s 0.13 µm CMOS three-stage pipeline ADC with various circuit design techniques to reduce power and die area. Digitally controlled timing delay and gate-bootstrapping circuits improve the linearity and sampling time mismatch of the SHA-free input network composed of an MDAC and a FLASH ADC. A single two-stage switched op-amp is shared between adjacent MDACs without MOS series switches and memory effects by employing two separate NMOS input pairs based on slightly overlapped switching clocks. The interpolation, open-loop offset sampling, and two-step reference selection schemes for a back-end 6-bit flash ADC reduce both power consumption and chip area drastically compared to the conventional 6-bit flash ADCs. The prototype ADC in a 0.13 µm CMOS process demonstrates measured differential and integral non-linearities within 0.44LSB and 1.54LSB, respectively. The ADC shows a maximum SNDR and SFDR of 60.5 dB and 71.2 dB at 100 MS/s, respectively. The ADC with an active die area of 0.92 mm2 consumes 19 mW at 100 MS/s from a 1.0 V supply. The measured FOM is 0.22 pJ/conversion-step.

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.

The VoIP-based Internet Phonesystem is now seen as one of the killer applications in the high speed and broadband internet environment. Given the wide-spread use of the Internet Phone, it is necessary to provide security services for guaranteeing users' privacy. However, providing security service in Internet Phone has the possibility of incurring additional overheads such as call setup delay time. In this paper, we present a one-way key agreement model based on VoIP in order to reduce call setup time as well as protecting user privacy. The proposed approach decreases the delay time of the call setup in comparison with the previous models because our model enables the key generation in caller side without waiting the response from the receiver.

DV-Hop algorithm produces errors in location estimations due to inaccurate hop size. We propose a novel localization scheme based on DV-Hop to improve positioning accuracy with least error hop sizes of anchors and average hop sizes of unknowns. The least error hop size of an anchor minimizes its location error, but it may be far small or large. To cope with this inconsistent hop size, each unknown node calculates its average hop size with hop sizes from anchors. Simulation results show that the proposed algorithm outperforms the DV-Hop algorithm in location estimations.

We present the recovery of 2.5 Gb/s synchronous 16-point quadrature amplitude modulation data in real-time for an linewidth-times-symbol-duration ratio of 0.00048 after transmission over 1.6 km standard single mode fiber.

Traffic adaptive 2-level active period control has been proposed to enhance system performance in cluster-based wireless sensor networks (WSNs) employing IEEE 802.15.4 medium access control (MAC) under temporal and spatial (geographical) non-uniform traffic environments. This paper proposes an adaptive method of controlling the backoff window for traffic adaptive 2-level active period control. The proposed method adjusts the size of the backoff window according to the length of the current active period, which is determined by 2-level active period control, and the time position for channel access in the active period. The results evaluated through computer simulations reveal that the proposed method can improve throughput as well as achieve high energy efficiency in cluster-based WSNs with non-uniform traffic distributions.

Since the release of human genome sequences, one of the most important research issues is about indexing the genome sequences, and the suffix tree is most widely adopted for that purpose. The traditional suffix tree construction algorithms suffer from severe performance degradation due to the memory bottleneck problem. The recent disk-based algorithms also provide limited performance improvement due to random disk accesses. Moreover, they do not fully utilize the recent CPUs with multiple cores. In this paper, we propose a fast algorithm based on `divide-and-conquer' strategy for indexing the human genome sequences. Our algorithm nearly eliminates random disk accesses by accessing the disk in the unit of contiguous chunks. In addition, our algorithm fully utilizes the multi-core CPUs by dividing the genome sequences into multiple partitions and then assigning each partition to a different core for parallel processing. Experimental results show that our algorithm outperforms the previous fastest DIGEST algorithm by up to 10.5 times.

In this paper, a preemptive priority queueing model is developed to derive the system dwelling time of secondary calls in a cognitive radio system in which a primary call's reoccupation of the channel is modeled as a preemptive event that forces a secondary call to attempt a spectrum handover. The suspension of secondary call service which may happen when the immediate spectrum handover fails, is included in our computation of the system dwelling time. The results are helpful in evaluating cognitive radio systems in terms of service delay and in determining system design parameters such as required buffer size and system capacity.

In wireless networks, the mechanism to adaptively select a link transmission rate based on channel variations is referred to as RA (rate adaptation). The operation may have a critical impact on the upper-layer application, specifically video streaming which has strict QoS requirements. Thus, RA should consider the QoS requirements and radio conditions at the same time. In this paper, we present a CV-RA (cross-layer video-oriented rate adaptation) scheme for video transmission over multi-rate wireless networks. The transmission rate is switched in a cross-layer optimized way, by simultaneously considering video R-D (rate-distortion) characteristics as well as wireless conditions. At the radio link layer, transmission rate selection is made using cross-layer optimization. As a result of RA, the effective link throughput dynamically changes. At the application layer, video source rate is adaptively controlled using cross-layer adaptation. CV-RA is compared to three traditional RA schemes. It can realize the highest possible visual communications for any channel condition. For the previous schemes, the variations of visual quality is high due to dynamic packet error rates. In contrast, for CV-RA, visual quality improves with the channel condition.

Manufacturing defects in the deep sub-micron VLSI process and aging resulted problems of devices during lifecycle are inevitable, and fault-tolerant routing algorithms are important to provide the required communication for NoCs in spite of failures. The proposed algorithm, referred to as scalable and reconfigurable fault-tolerant distributed routing (RFDR), partitions the system into nine regions using the concept of divide-and-conquer. It is a distributed algorithm, and each router guarantees fault-tolerance within one's own region and the system can be still sustained with multiple fault areas. The proposed RFDR has excellent scalability with hardware cost keeping constant independent of system size. Also it is completely reconfigurable when new nodes fail. Simulations under various synthetic traffic patterns show its better performance compared to Extended-XY routing algorithm. Moreover, there is almost no hardware overhead compared to Logic-Based Distributed Routing (LBDR), but the fault-tolerance capacity is enhanced in the proposed algorithm. Hardware cost is reduced 37% compared to Reconfigurable Distributed Scalable Predictable Interconnect Network (R-DSPIN) which only supports single fault region.

In this paper, we propose a text corpus design method for a Korean stereo super-wideband speech database. Since a small-sized text corpus for speech coding is generally required for speech coding, the corpus should be designed to comply with the pronunciation behavior of natural conversation in order to ensure efficient speech quality tests. To this end, the proposed design method utilizes a similarity measure between the phoneme distribution occurring from natural conversation and that from the designed text corpus. In order to achieve this goal, we first collect and refine text data from textbooks and websites. Next, a corpus is designed from the refined text data based on the similarity measure to compare phoneme distributions. We then construct a Korean stereo super-wideband speech (K-SW) database using the designed text corpus, where the recording environment is set to meet the conditions defined by ITU-T. Finally, the subjective quality of the K-SW database is evaluated using an ITU-T super-wideband codec in order to demonstrate that the K-SW database is useful for developing and evaluating super-wideband codecs.

Localized content-based image retrieval (LCBIR) has emerged as a hot topic more recently because in the scenario of CBIR, the user is interested in a portion of the image and the rest of the image is irrelevant. In this paper, we propose a novel region-level relevance feedback method to solve the LCBIR problem. Firstly, the visual attention model is employed to measure the regional saliency of each image in the feedback image set provided by the user. Secondly, the regions in the image set are constructed to form an affinity matrix and a novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. After the iteration, regions in the positive images with high confident scores are selected as the candidate query set to conduct the next-round retrieval task until the retrieval results are satisfactory. Experimental results conducted on the SIVAL dataset demonstrate the effectiveness of the proposed approach.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

In an undirected graph, the feedback vertex set (FVS for short) problem is to find a set of vertices of minimum cardinality whose removal makes the graph acyclic. The FVS has applications to several areas such that combinatorial circuit design, synchronous systems, computer systems, VLSI circuits and so on. The FVS problem is known to be NP-hard on general graphs but interesting polynomial solutions have been found for some special classes of graphs. In this paper, we present an O(n2.68 + γn) time algorithm for solving the FVS problem on trapezoid graphs, where γ is the total number of factors included in all maximal cliques.

In this paper, a linear optimization of the dual-tree complex wavelet transform (DTCWT) based on the least squares method is proposed. The proposed method can design efficient DTCWTs by improving the design degrees of freedom and solving the least square solution iteratively. Because the resulting DTCWTs have good approximation accuracy of the half sample delay condition and the stopband attenuation, they provide precise shift-invariance and directionality. Finally, the proposed DTCWTs are evaluated by applying to non-linear approximation and image denoising, and showed their effectiveness, compared with the conventional DTCWTs.

This paper presents efficient frame-synchronous beam pruning for HMM-based automatic speech recognition. In the conventional beam pruning, a few hypotheses that have greater potential to reach various words on a lexical tree are likely to be pruned out by a number of hypotheses that have limited potential, since all hypotheses are treated equally without considering this potential. To make the beam pruning less restrictive for hypotheses with greater potential and vice versa, the proposed method adds to the likelihood of each hypothesis a tentative reward as a monotonically increasing function of the number of reachable words from the HMM state where the hypothesis stays in a lexical tree. The reward is designed not to collapse the ASR probabilistic framework. The proposed method reduced 84% of the processing time for a grammar-based 10k-word short sentence recognition task. For a language-model-based dictation task, it also resulted in an additional 23% reduction in processing time from the beam pruning with the language model look-ahead technique.

The least-squares probabilistic classifier (LSPC) is a computationally-efficient alternative to kernel logistic regression. However, to assure its learned probabilities to be non-negative, LSPC involves a post-processing step of rounding up negative parameters to zero, which can unexpectedly influence classification performance. In order to mitigate this problem, we propose a simple alternative scheme that directly rounds up the classifier's negative outputs, not negative parameters. Through extensive experiments including real-world image classification and audio tagging tasks, we demonstrate that the proposed modification significantly improves classification accuracy, while the computational advantage of the original LSPC remains unchanged.

Let T be a tree in which every edge is associated with a real number. The sum of a path in T is the sum of the numbers associated with the edges of the path and its length is the number of the edges in it. For two positive integers L1 ≤ L2 and two real numbers S1 ≤ S2, a path is feasible if its length is between L1 and L2 and its sum is between S1 and S2. We address the problem: Given a tree T, and four numbers, L1, L2, S1 and S2, find the longest feasible path of T. We provide an optimal O(n log n) time algorithm for the problem, where n =|T|.