With the process technological progress in recent years, low voltage power supplies have become quite predominant. With this, the voltage margin has decreased and therefore the power/ground design that satisfies the voltage drop constraint becomes more important. In addition, the reduction of the power/ground total wiring area and the number of layers will reduce manufacturing and designing costs. So, we propose an algorithm that satisfies the voltage drop constraint and at the same time, minimizes the power/ground total wiring area. The proposed algorithm uses the idea of a network algorithm [1] where the edge which has the most influence on voltage drop is found. Voltage drop is improved by changing the resistance of the edge. The proposed algorithm is efficient and effectively updates the edge with the greatest influence on the voltage drop. From experimental results, compared with the conventional algorithm, we confirmed that the total wiring area of the power/ground was reducible by about 1/3. Also, the experimental data shows that the proposed algorithm satisfies the voltage drop constraint in the data whereas the conventional algorithm cannot.

In this paper, we present a new frequency identification technique using the recent methodology of compressive sensing and discrete prolate spheroidal sequences with optimal energy concentration. Using the bandpass form of discrete prolate spheroidal sequences as basis matrix in compressive sensing, compressive frequency sensing algorithm is presented. Simulation results are given to present the effectiveness of the proposed technique for application to detection of carrier-frequency type signal and recognition of wideband signal in communication.

In deep-submicron era, wire delay is becoming a bottleneck while pursuing even higher system clock speed. Several distributed register (DR) architectures have been proposed to cope with this problem by keeping most wires local. In this article, we propose a new resource-constrained communication synthesis algorithm for optimizing both inter-island connections (IICs) and latency targeting on distributed register-file microarchitecture (DRFM). The experimental results show that up to 24.7% and 12.7% reduction on IIC and latency can be achieved respectively as compared to the previous work.

For recent and future nanometer-technology VLSIs, static and dynamic delay variations become a serious problem. In many cases, the hold timing constraint, as well as the setup timing constraint, becomes critical for latching a correct signal under delay variations. While the timing violation due to the fail of the setup timing constraint can be fixed by tuning a clock frequency or using a delayed latch, the timing violation due to the fail of the hold timing constraint cannot be fixed by those methods in general. Our approach to delay variations (in particular, the hold timing constraint) proposed in this paper is a novel register assignment strategy in high-level synthesis, which guarantees safe clocking by Backward-Data-Direction (BDD) clocking. One of the drawbacks of the proposed register assignment is the increase in the number of required registers. After the formulation of this new register minimization problem, we prove NP-hardness of the problem, and then derive an integer linear programming formulation for the problem. The proposed method receives a scheduled data flow graph, and generates a datapath having (1) robustness against delay variations, which is ensured by BDD-based register assignment, and (2) the minimum possible number of registers. Experimental results show the effectiveness of the proposed method for some benchmark circuits.

Wireless regional area network (WRAN) is intended to offer the fixed wireless access services using cognitive radio technology in the TV white space. Therefore, WRAN shall minimize the transmission power so that harmful interference is not imposed on the licensed users operating in the TV bands. In this paper, we propose a processing block that offers improvements in the SNR and diversity gain using the block to algebraically process two constellation symbols. Thus, the transmission power can be reduced by an amount equal to the gains. The simulation result shows that the proposed scheme has a better bit error performance than the transmission scheme defined in the IEEE 802.22 draft standard.

In this paper, we address adaptive link switching over heterogeneous wireless access networks including IEEE 802.11. When an IEEE 802.11 link is congested, the transmission link of a terminal with multi-RATs (radio access technologies) is switched to another radio access systems. To this end, we propose link-level metrics of LC (link cost) and AC (access cost) for quantifying TCP congestion over IEEE 802.11 networks. The proposed metric can be easily measured at a local wireless terminal, and that enables each multi-RAT terminal to work in a distributed way. Through various indoor and outdoor experiments using a test-bed system, we verify that the proposed link level metrics are good indicators of TCP traffic congestion. Experimental results show that the proposed metrics can detect congestion occurrence quickly, and avoid the TCP throughput degradation of other neighboring terminals, when they are used for transmission link switching.

This paper presents an improved Gini-Index algorithm to correct feature-selection bias in text classification. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, an improved Gini-Index algorithm for feature selection, designed for text categorization and based on Gini-Index theory, was introduced, and it has proved to be better than the other methods. However, we found that the Gini-Index still shows a feature selection bias in text classification, specifically for unbalanced datasets having a huge number of features. The feature selection bias of the Gini-Index in feature selection is shown in three ways: 1) the Gini values of low-frequency features are low (on purity measure) overall, irrespective of the distribution of features among classes, 2) for high-frequency features, the Gini values are always relatively high and 3) for specific features belonging to large classes, the Gini values are relatively lower than those belonging to small classes. Therefore, to correct that bias and improve feature selection in text classification using Gini-Index, we propose an improved Gini-Index (I-GI) algorithm with three reformulated Gini-Index expressions. In the present study, we used global dimensionality reduction (DR) and local DR to measure the goodness of features in feature selections. In experimental results for the I-GI algorithm, we obtained unbiased feature values and eliminated many irrelevant general features while retaining many specific features. Furthermore, we could improve the overall classification performances when we used the local DR method. The total averages of the classification performance were increased by 19.4 %, 15.9 %, 3.3 %, 2.8 % and 2.9 % (kNN) in Micro-F1, 14 %, 9.8 %, 9.2 %, 3.5 % and 4.3 % (SVM) in Micro-F1, 20 %, 16.9 %, 2.8 %, 3.6 % and 3.1 % (kNN) in Macro-F1, 16.3 %, 14 %, 7.1 %, 4.4 %, 6.3 % (SVM) in Macro-F1, compared with tf*idf, χ2, Information Gain, Odds Ratio and the existing Gini-Index methods according to each classifier.

In this paper, we present a fault analysis of the original NTRU public key cryptosystem. The fault model in which we analyze the cipher is the one in which the attacker is assumed to be able to fault a small number of coefficients of the polynomial input to (or output from) the second step of the decryption process but cannot control the exact location of injected faults. For this specific original instantiation of the NTRU encryption system with parameters (N,p,q), our attack succeeds with probability≈ and when the number of faulted coefficients is upper bounded by t, it requires O((pN)t) polynomial inversions in Z/p Z[x]/(xN-1).

Mobile operators need to migrate from 2G to 3G networks in a cost-effective manner. Cognitive radio systems are currently being investigated as a promising solution to achieve spectrum efficiency by allowing coexistence of unlicensed (secondary) networks and licensed (primary) networks. However, conventional mechanisms to operate these systems incur additional complexity and fail to maximize network performance. In this paper, we propose a pilot sensing and frequency selection method with low complexity for OFDMA-based cognitive radio systems. Subject to the interference constraints imposed by the primary network, capacity maximization problems involving both up-link and down-link connections are considered for overall network performance improvement. The throughput and outage probability of the proposed method are evaluated by simulations. Our proposed method shows outstanding performance if the channel varies frequently in the primary network and the frequency reuse factor of the primary network is high.

Reliability issues such as a soft error and NBTI (negative bias temperature instability) have become a matter of concern as integrated circuits continue to shrink. It is getting more and more important to take reliability requirements into account even for consumer products. This paper presents a dynamic continuous signature monitoring (DCSM) technique for high reliable computer systems. The DCSM technique dynamically generates reference signatures as well as runtime ones during executing a program. The DCSM technique stores the generated signatures in a signature table, which is a small storage circuit in a microprocessor, unlike the conventional static continuous signature monitoring techniques and contributes to saving program or data memory space that stores the signatures. Our experiments showed that our DCSM technique protected 1.4-100.0% of executed instructions depending on the size of signature tables.

Power-aware scheduling of periodic tasks in real-time systems has been extensively studied to save energy while still meeting the performance requirement. Many previous studies use the probability information of tasks' execution cycles to assist the scheduling. However, most of these approaches adopt heuristic algorithms to cope with realistic CPU models with discrete frequencies and cannot achieve the globally optimal solution. Sometimes they even show worse results than non-stochastic DVS schemes. This paper presents an optimal DVS scheme for frame-based real-time systems under realistic power models in which the processor provides only a limited number of speeds and no assumption is made on power/frequency relation. A suboptimal DVS scheme is also presented in this paper to work out a solution near enough to the optimal one with only polynomial time expense. Experiment results show that the proposed algorithm can save at most 40% more energy compared with previous ones.

To realize transmit diversity for the time domain synchronous OFDM (TDS-OFDM) system, this letter proposes the space-time-frequency orthogonal training sequence and the corresponding flexible channel estimation methods. Simulation results indicate that an significant performance improvement could be achieved for low-density parity-check code (LDPC) coded TDS-OFDM system over multi-path fading channels.

The separation and localization of sound source signals are important techniques for many applications, such as highly realistic communication and speech recognition systems. These systems are expected to work without such prior information as the number of sound sources and the environmental conditions. In this paper, we developed a dodecahedral microphone array and proposed a novel separation method with our developed device. This method refers to human sound localization cues and uses acoustical characteristics obtained by the shape of the dodecahedral microphone array. Moreover, this method includes an estimation method of the number of sound sources that can operate without prior information. The sound source separation performances were evaluated under simulated and actual reverberant conditions, and the results were compared with the conventional method. The experimental results showed that our separation performance outperformed the conventional method.

A regulation of converters connected in parallel is discussed considering their stored energy and passivity characteristics. From the viewpoint of stored energy, a new regulation method to conserve and share the stored energy can be found. The energy stored in inductors and capacitors is transferred to loads so that the load keeps the energy dissipation constant. Though numerical simulation, the method is validated for a parallel converter system.

The extended binary phase shift keying (EBPSK) transmission system with ultra narrow bandwidth has excellent BER performance, which raises many doubts with the researchers. Therefore, on the premise of the existence of a special filter that can transform the modulated phase information into amplitude information, the theoretical BER formula of EBPSK system in Additive White Gaussian Noise (AWGN) channel has been deduced. This paper gives the theoretical values of the parameters in the above BER formula and discusses the effects of parameters on BER firstly. Then the paper shows that the special impacting filter satisfies the above assumption, therefore, in the frame of binary detection theory, the excellent performance of high-efficiency EBPSK system can be explained and the correction of the theoretical BER formula can be validated.

A 1.8 V, 5 GHz low power frequency synthesizer for Wireless Sensor Networks is presented in 0.18 µm CMOS technology. A low power phase-switching prescaler is designed, and the current mode phase rotator is merged into the first divide-by-2 circuit of the prescaler to reduce power and propagation delay. An improved charge pump circuit is proposed to compensate for the dynamic effects with the charge pump. By a divide-by-2 circuit, the frequency synthesizer can provide a 2.324-2.714 GHz quadrature output frequency in 1 MHz steps with a 4 MHz reference frequency. The measured output phase noise is -110 dBc/Hz at 1-MHz offset frequency. The power consumption of the PLL is 11.2 mW at 1.8 V supply voltage.

We propose a novel probing scheme capable of discovering shared bottlenecks among multiple paths between two multihomed hosts simultaneously, without any specific help from the network routers, and a subsequent grouping approach for partitioning these paths into groups. Simulation results show that the probing and grouping have an excellent performance under different network conditions.

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.

A new kind of 3D power divider based on a half-mode substrate integrated circular cavity (HSICC) is proposed. This novel power divider can reduce the size of a power divider based on normal substrate integrated circular cavity (SICC) by nearly a half. To verify the validity of the design method, a two-way X-band HSICC power divider using low temperature co-fired ceramic (LTCC) technology is designed, fabricated and measured.

This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.