In recent years, the demand for low-power design has remained undiminished. In this paper, a pseudo power gating (SPG) structure using a normal logic cell is proposed to extend the power gating to an ultrafine grained region at the gate level. In the proposed method, the controlling value of a logic element is used to control the switching activity of modules computing other inputs of the element. For each element, there exists a submodule controlled by an input to the element. Power reduction is maximized by controlling the order of the submodule selection. A basic algorithm and a switching activity first algorithm have been developed to optimize the power. In this application, a steady maximum depth constraint is added to prevent the depth increase caused by the insertion of the control signal. In this work, various factors affecting the power consumption of library level circuits with the SPG are determined. In such factors, the occurrence of glitches increases the power consumption and a method to reduce the occurrence of glitches is proposed by considering the parity of inverters. The proposed SPG method was evaluated through the simulation of the netlist extracted from the layout using the VDEC Rohm 0.18 µm process. Experiments on ISCAS'85 benchmarks show that the reduction in total power consumption achieved is 13% on average with a 2.5% circuit delay degradation. Finally, the effectiveness of the proposed method under different primary input statistics is considered.

In orthogonal frequency division multiplexing (OFDM) based systems, intercarrier interference (ICI) created by the time varying property of wireless fading channels, degrades the data detection performance. This degradation strengthens especially when the mobile speed is relatively high or the number of subcarriers is large. Here, we interpret the time varying channel as the linear transformation of the time invariant channel impulse response. By using this new channel model, the time varying component can be reduced in the time domain by applying the inverse transformation to the received sequence. Then, the remaining time invariant channel component is removed by the equalization in the frequency domain. Some complexity reduction schemes are also proposed to make the proposed method feasible for practical implementation. The simulation results show that the new method offers a significant improvement in terms of bit error rate performance, especially when the number of subcarriers is larger than about 500.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

Fast and accurate estimation of missing relations, e.g., similarity, distance and kernel, among objects is now one of the most important techniques required by major data mining tasks, because the missing information of the relations is needed in many applications such as economics, psychology, and social network communities. Though some approaches have been proposed in the last several years, the practical balance between their required computation amount and obtained accuracy are insufficient for some class of the relation estimation. The objective of this paper is to formalize a problem to quickly and efficiently estimate missing relations among objects from the other known relations among the objects and to propose techniques called “PSD Estimation” and “Row Reduction” for the estimation problem. This technique uses a characteristic of the relations named “Positive Semi-Definiteness (PSD)” and a special assumption for known relations in a matrix. The superior performance of our approach in both efficiency and accuracy is demonstrated through an evaluation based on artificial and real-world data sets.

In this paper, a single-channel adaptive noise canceller (SCANC) is proposed to enhance heart sounds during auscultation. Heart sounds provide important information about the condition of the heart, but other sounds interfere with heart sounds during auscultation. The adaptive noise canceller (ANC) is widely used to reduce noises from biomedical signals, but it is not suitable for enhancing auscultatory sounds acquired by a stethoscope. While the ANC needs two inputs, a stethoscope provides only one input. Other approaches, such as ECG gating and wavelet de-noising, are rather complex and difficult to implement as real-time systems. The proposed SCANC uses a single-channel input based on Heart Sound Inherency Indicator and reference generator. The architecture is simple, so it can be easily implemented in real-time systems. It was experimentally confirmed that the proposed SCANC is efficient for heart sound enhancement and is robust against the heart rate variations.

Ground antennas are suitable for use in mobile electronic devices due to their compactness. These ground antennas incorporate two capacitors for controlling the resonance frequency and a shorting loop for impedance matching. In this work, we compare the performance of a ground antenna with that of a meandered inverted-F antenna (IFA). It is numerically and experimentally shown that a ground antenna can yield simultaneous improvements in both the antenna size and radiation efficiency when compared to the meandered IFA. The bandwidth of the ground antenna for a voltage standing wave ratio (VSWR) of 3:1 is 240 MHz from 2350 MHz to 2590 MHz, while the minimum total antenna efficiency is 62% within the 2.4 GHz ISM band.

Linear Discriminant Analysis (LDA) is one of the most popular dimensionality reduction techniques in existing handwritten Chinese character (HCC) recognition systems. However, when used for unconstrained handwritten Chinese character recognition, the traditional LDA algorithm is prone to two problems, namely, the class separation problem and multimodal sample distributions. To deal with these problems,we propose a new locally linear discriminant analysis (LLDA) method for handwritten Chinese character recognition.Our algorithm operates as follows. (1) Using the clustering algorithm, find clusters for the samples of each class. (2) Find the nearest neighboring clusters from the remaining classes for each cluster of one class. Then, use the corresponding cluster means to compute the between-class scatter matrix in LDA while keeping the within-class scatter matrix unchanged. (3) Finally, apply feature vector normalization to further improve the class separation problem. A series of experiments on both the HCL2000 and CASIA Chinese character handwriting databases show that our method can effectively improve recognition performance, with a reduction in error rate of 28.7% (HCL2000) and 16.7% (CASIA) compared with the traditional LDA method.Our algorithm also outperforms DLA (Discriminative Locality Alignment,one of the representative manifold learning-based dimensionality reduction algorithms proposed recently). Large-set handwritten Chinese character recognition experiments also verified the effectiveness of our proposed approach.

Reduction of the intensity noise in semiconductor lasers is an important subject for the higher performance of an application. Simultaneous usage of the superposition of high frequency current and the electric negative feedback loop was proposed to suppress the noise for the higher power operation of semiconductor lasers. Effective noise reduction of more than 25 dB with 80 mW operation was experimentally demonstrated.

Clock gating is supported by commercial tools as a power optimization feature based on the guard signal described in HDL (structural method). However, the identification of control signals for gated registers is hard and designer-intensive work. Besides, since the clock gating cells also consume power, it is imperative to minimize the number of inserted clock gating cells and their switching activities for power optimization. In this paper, we propose an automatic multi-stage clock gating algorithm with ILP (Integer Linear Programming) formulation, including clock gating control candidate extraction, constraints construction and optimum control signal selection. By multi-stage clock gating, unnecessary clock pulses to clock gating cells can be avoided by other clock gating cells, so that the switching activity of clock gating cells can be reduced. We find that any multi-stage control signals are also single-stage control signals, and any combination of signals can be selected from single-stage candidates. The proposed method can be applied to 3 or more cascaded stages. The multi-stage clock gating optimization problem is formulated as constraints in LP format for the selection of cascaded clock-gating order of multi-stage candidate combinations, and a commercial ILP solver (IBM CPLEX) is applied to obtain the control signals for each register with minimum switching activity. Those signals are used to generate a gate level description with guarded registers from original design, and a commercial synthesis and layout tools are applied to obtain the circuit with multi-stage clock gating. For a set of benchmark circuits and a Low Density Parity Check (LDPC) Decoder (6.6k gates, 212 F.F.s), the proposed method is applied and actual power consumption is estimated using Synopsys NanoSim after layout. On average, 31% actual power reduction has been obtained compared with original designs with structural clock gating, and more than 10% improvement has been achieved for some circuits compared with single-stage optimization method. CPU time for optimum multi-stage control selection is several seconds for up to 25k variables in LP format. By applying the proposed clock gating, area can also be reduced since the multiplexors controlling register inputs are eliminated.

Distributed applications and services have become pervasive in our society due to the widespread use of internet and mobile devices. There are urgent demands to efficiently ensure safety and correctness of such software. A session-type system is a framework to statically check whether communication descriptions conform to certain protocols. They are shown to be effective yet simple enough to fit in harmony with existing programming languages. In the original session type system, the subject reduction property does not hold. This paper establishes a conservative extension of the original session type system with the subject reduction property. Finally, it is also shown that our typing rule properly extends the set of typeable processes.

We propose a new coding scheme for lossless bit rate reduction of the MPEG Surround module in unified speech and audio coding (USAC). The proposed scheme is based on context-adaptive arithmetic coding for efficient bit stream composition of spatial parameters. Experiments show that it achieves the significant lossless bit reduction of 9.93% to 12.14% for spatial parameters and 8.64% to 8.96% for the overall MPEG Surround bit streams compared to the original scheme. The proposed scheme, which is not currently included in USAC, can be used for the improved coding efficiency of MPEG Surround in USAC, where the saved bits can be utilized by the other modules in USAC.

High frequency bands such as the 3-GHz band have received much attention as frequency resources for broadband mobile communication systems. Radio Frequency (RF) integrated antennas are considered to be useful as base station antennas in decreasing the feeding loss that is otherwise inevitable in high frequency bands and they ensure sufficient power for broadband transmission. One problem in actualizing RF integrated antennas is miniaturizing the duplexer, which is generally large, among the RF circuitry components. To downsize the duplexer, we consider separately locating the transmitter (Tx) and receiver (Rx) antennas. To suppress further the mutual coupling between the Tx and Rx antennas, we investigate a filter integrated antenna configuration. In this paper, we consider an aperture coupled patch antenna as the base antenna configuration and propose a new filter integrated antenna that comprises multiple rectangular elements installed between the coupling slot and radiation element of the Rx antenna. The simulation and measurement results confirm that the new antenna reduces the mutual coupling in the transmission frequency band up to 5.7 dB compared to the conventional slot coupled patch antenna configuration.

Lazy-task creation is an efficient method of overcoming the overhead of the grain-size problem in parallel computing. Work stealing is an effective load balancing strategy for parallel computing. In this paper, we present dynamic work stealing strategies in a lazy-task creation technique for efficient fine-grain task scheduling. The basic idea is to control load balancing granularity depending on the number of task parents in a stack. The dynamic-length strategy of work stealing uses run-time information, which is information on the load of the victim, to determine the number of tasks that a thief is allowed to steal. We compare it with the bottommost first work stealing strategy used in StackThread/MP, and the fixed-length strategy of work stealing, where a thief requests to steal a fixed number of tasks, as well as other multithreaded frameworks such as Cilk and OpenMP task implementations. The experiments show that the dynamic-length strategy of work stealing performs well in irregular workloads such as in UTS benchmarks, as well as in regular workloads such as Fibonacci, Strassen's matrix multiplication, FFT, and Sparse-LU factorization. The dynamic-length strategy works better than the fixed-length strategy because it is more flexible than the latter; this strategy can avoid load imbalance due to overstealing.

In this paper, to reduce the memory size requirements of IFFT for OFDM-based applications, we propose a new IFFT design technique based on a combined integer mapping of three IFFT input signals: modulated data, pilot and null signals. The proposed method focuses on reducing the size of memory cells in the first two stages of the single-path delay feedback (SDF) IFFT architectures since the first two stages require 75% of the total memory cells. By simulations of 2048-point IFFT design for cognitive radio systems, it is shown that the proposed IFFT design method achieves more than 13% reduction in gate count and 11% reduction in power consumption compared with conventional IFFT design.

The subset sum problem, which is often called as the knapsack problem, is known as an NP-hard problem, and there are several cryptosystems based on the problem. Assuming an oracle for shortest vector problem of lattice, the low-density attack algorithm by Lagarias and Odlyzko and its variants solve the subset sum problem efficiently, when the “density” of the given problem is smaller than some threshold. When we define the density in the context of knapsack-type cryptosystems, weights are usually assumed to be chosen uniformly at random from the same interval. In this paper, we focus on general subset sum problems, where this assumption may not hold. We assume that weights are chosen from different intervals, and make analysis of the effect on the success probability of above algorithms both theoretically and experimentally. Possible application of our result in the context of knapsack cryptosystems is the security analysis when we reduce the data size of public keys.

This paper proposes a wide-band noise reduction method using a zero phase (ZP) signal which is defined as the IDFT of a spectral amplitude. When a speech signal has periodicity in a short observation, the corresponding ZP signal becomes also periodic. On the other hand, when a noise spectral amplitude is approximately flat, its ZP signal takes nonzero values only around the origin. Hence, when a periodic speech signal is embedded in a flat spectral noise in an analysis frame, its ZP signal becomes a periodic signal except around the origin. In the proposed noise reduction method, we replace the ZP signal around the origin with the ZP signal in the second or latter period. Then, we get an estimated speech ZP signal. The major advantages of this method are that it can reduce not only stationary wide-band noises but also non-stationary wide-band noises and does not require a prior estimation of the noise spectral amplitude. Simulation results show that the proposed noise reduction method improves the SNR more than 5 dB for a tunnel noise and 13 dB for a clap noise in a low SNR environment.

We propose a new method, called Subclass-oriented Dimensionality Reduction with Pairwise Constraints (SODRPaC), for dimensionality reduction. In a high dimensional space, it is common that a group of data points with one class may scatter in several different groups. Current linear semi-supervised dimensionality reduction methods would fail to achieve fair performances, as they assume two data points linked by a must-link constraint are close each other, while they are likely to be located in different groups. Inspired by the above observation, we classify the must-link constraint into two categories, which are the inter-subclass must-link constraint and the intra-subclass must-link constraint, respectively. We carefully generate cannot-link constraints by using must-link constraints, and then propose a new discriminant criterion by employing the cannot-link constraints and the compactness of shared nearest neighbors. The manifold regularization is also incorporated in our dimensionality reduction framework. Extensive experiments on both synthetic and practical data sets illustrate the effectiveness of our method.

Multi-user multi-input multi-output (MIMO) system has been attracting much attention due to its high spectrum efficiency. Non-linear MIMO signal detection methods with less computational complexity have been widely studied for single-user MIMO systems. In this paper, we investigate how a lattice reduction (LR)-aided detection and a maximum likelihood detection (MLD) employing the QR decomposition and M-algorithm (QRM-MLD), which are commonly known as non-linear MIMO signal detection methods, improve the uplink capacity of a multi-user MIMO-OFDM cellular system, compared to simple linear detection methods such as zero-forcing detection (ZFD) and minimum mean square error detection (MMSED). We show that both LR-aided linear detection and QRM-MLD can achieve higher uplink capacity than simple linear detection at the cost of moderate increase of computational complexity. Furthermore, QRM-MLD can obtain the same uplink capacity as MLD.

This paper proposes a new lightweight 256-bit hash function Lesamnta-LW. The security of Lesamnta-LW is reduced to that of the underlying AES-based block cipher and it is theoretically analyzed for an important application, namely the key-prefix mode. While most of recently proposed lightweight primitives are hardware-oriented with very small footprints, our main target with Lesamnta-LW is to achieve compact and fast hashing for lightweight application on a wider variety of environments ranging from inexpensive devices to high-end severs at the 2120 security level. As for performance, our primary target CPUs are 8-bit and it is shown that, for short message hashing, Lesamnta-LW offers better tradeoffs between speed and cost on an 8-bit CPU than SHA-256.

In this letter, we propose a novel speech enhancement algorithm based on data-driven residual gain estimation. The entire system consists of two stages. At the first stage, a conventional speech enhancement algorithm enhances the input signal while estimating several signal-to-noise ratio (SNR)-related parameters. The residual gain, which is estimated by a data-driven method, is applied to further enhance the signal at the second stage. A number of experimental results show that the proposed speech enhancement algorithm outperforms the conventional speech enhancement technique based on soft decision and the data-driven approach using SNR grid look-up table.