Power analysis exploits the leaked information gained from cryptographic devices including, but not limited to, power consumption generated during cryptographic operations. If a number of power traces are given to an attacker, it is possible to reveal a cryptographic key efficiently, sometimes within a few minutes, using various statistical methods. In this sense, software countermeasures including higher-order masking or software dual-rail with precharge logic have been proposed to produce randomized or constant power consumption during the key-dependent operations. However, they have critical disadvantages in terms of computational time and security. In this paper, we propose a new solution called “one-bit to four-bit dual conversion” for enhanced security against power analysis. For an exemplary embodiment of the proposed scheme, we apply it to an AES implementation and demonstrate its security and performance. The overall costs are approximately 148KB memory space for the lookup tables and about a 3-fold increase in execution time than the straightforward implementation of AES.

The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new end-to-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionist temporal classification (CTC) objective function, is proposed and achieves comparable results with the hybrid HMM/DNN system. In this paper, we investigate per-dimensional learning rate methods, ADAGRAD and ADADELTA included, to improve the recognition of the end-to-end system, based on the fact that the blank symbol used in CTC technique dominates the output and these methods give frequent features small learning rates. Experiment results show that more than 4% relative reduction of word error rate (WER) as well as 5% absolute improvement of label accuracy on the training set are achieved when using ADADELTA, and fewer epochs of training are needed.

Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.

Automated biometrics identification using finger vein images has increasingly generated interest among researchers with emerging applications in human biometrics. The traditional feature-level fusion strategy is limited and expensive. To solve the problem, this paper investigates the possible use of infrared hybrid finger patterns on the back side of a finger, which includes both the information of finger vein and finger dorsal textures in original image, and a database using the proposed hybrid pattern is established. Accordingly, an Intersection enhanced Gabor based Direction Coding (IGDC) method is proposed. The Experiment achieves a recognition ratio of 98.4127% and an equal error rate of 0.00819 on our newly established database, which is fairly competitive.

Magnet-less non-reciprocal metamaterial (MNM) synthesise artificial magnetic gyrotropy by metal ring resonator with unilateral component insertion. Clear advantage to natural magnetic material is full integrated circuit ingredient compatibility but still suffers from drawbacks of consumption power in active component and footprint of ring resonator. A new MNM structure by a varactor inserted figure of eight resonator is introduced, which enables reduction of active components by half and even smaller footprint to the original simple ring resonator structure in addition to frequency tunability.

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a low-dimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition.

Two new authenticated key agreement protocols in the certificateless setting are presented in this paper. Both are proved secure in the extended Canetti-Krawczyk model, under the BDH assumption. The first one is more efficient than the Lippold et al.'s (LBG) protocol, and is proved secure in the same security model. The second protocol is proved secure under the Swanson et al.'s security model, a weaker model. As far as we know, our second proposed protocol is the first one proved secure in the Swanson et al.'s security model. If no pre-computations are done, the first protocol is about 26% faster than LBG, and the second protocol is about 49% faster than LBG, and about 31% faster than the first one. If pre-computations of some operations are done, our two protocols remain faster.

The online interval coloring problem has been extensively studied for many years. Kierstead and Trotter (Congressus Numerantium 33, 1981) proved that their algorithm is an optimal online algorithm for this problem. The number of colors used by the algorithm is at most 3ω(G)-2, where ω(G) is the size of the maximum clique in a given graph G. Also, they presented an instance for which the number of colors used by any online algorithm is at least 3ω(G)-2. This instance includes intervals with various lengths, which cannot be applied to the case when the lengths of the given intervals are restricted to one, i.e., the online unit interval coloring problem. In this case, the current best upper and lower bounds on the number of colors used by an online algorithm are 2ω(G)-1 and 3ω(G)/2 respectively by Epstein and Levy (ICALP2005). In this letter, we conduct a complete performance analysis of the Kierstead-Trotter algorithm for online unit interval coloring, and prove it is NOT optimal. Specifically, we provide an upper bound of 3ω(G)-3 on the number of colors used by their algorithm. Moreover, the bound is the best possible.

The indexes of the degradation of C/N, ΔT/T and I/N, which can be converted from one to another, are used to evaluate the impact of interference on the satellite link. However, it is not suitable to intuitively understand how these parameters degrade the quality of services. In this paper, we propose to evaluate the impact of interference on the performance of BSS (Broadcasting Satellite Services) in terms of the increase rate of the outage time caused by the rain attenuation. Some calculation results are given for the 12GHz band BSS in Japan.

Ramp metering is the most effective and direct method to control a vehicle entering a freeway. This study proposes a novel density-based ramp metering method. Existing methods typically use flow data that has low reliability, and they suffer from various problems. Furthermore, when ramp metering is performed based on freeway congestion, additional congestion and over-capacity can occur in the ramp. To solve these problems faced with existing methods, the proposed method uses the density and acceleration data of vehicles on the freeway and considers the ramp status. The experimental environment was simulated using PTV Corporation's VISSIM simulator. The Traffic Information and Condition Analysis System was developed to control the VISSIM simulator. The experiment was conducted between 2:00 PM and 7:00 PM on October 5, 2014, during severe traffic congestion. The simulation results showed that total travel time was reduced by 10% compared to existing metering system during the peak time. Thus, we solved the problem of ramp congestion and over-capacity.

We propose a speech analysis method based on the source-filter model using multivariate empirical mode decomposition (MEMD). The proposed method takes multiple adjacent frames of a speech signal into account by combining their log spectra into multivariate signals. The multivariate signals are then decomposed into intrinsic mode functions (IMFs). The IMFs are divided into two groups using the peak of the autocorrelation function (ACF) of an IMF. The first group characterized by a spectral fine structure is used to estimate the fundamental frequency F0 by using the ACF, whereas the second group characterized by the frequency response of the vocal-tract filter is used to estimate formant frequencies by using a peak picking technique. There are two advantages of using MEMD: (i) the variation in the number of IMFs is eliminated in contrast with single-frame based empirical mode decomposition and (ii) the common information of the adjacent frames aligns in the same order of IMFs because of the common mode alignment property of MEMD. These advantages make the analysis more accurate than with other methods. As opposed to the conventional linear prediction (LP) and cepstrum methods, which rely on the LP order and cut-off frequency, respectively, the proposed method automatically separates the glottal-source and vocal-tract filter. The results showed that the proposed method exhibits the highest accuracy of F0 estimation and correctly estimates the formant frequencies of the vocal-tract filter.

Collaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

NAND flash memory has been widely used in storage systems. Aiming to design an efficient buffer policy for NAND flash memory, a life-aware buffer management algorithm named LAB-LRU is proposed, which manages the buffer by three LRU lists. A life value is defined for every page and the active pages with higher life value can stay longer in the buffer. The definition of life value considers the effect of access frequency, recency and the cost of flash read and write operations. A series of trace-driven simulations are carried out and the experimental results show that the proposed LAB-LRU algorithm outperforms the previous best-known algorithms significantly in terms of the buffer hit ratio, the numbers of flash write and read operations and overall runtime.

The 3-D channel routing is a fundamental problem on the physical design of 3-D integrated circuits. The 3-D channel is a 3-D grid G and the terminals are vertices of G located in the top and bottom layers. A net is a set of terminals to be connected. The objective of the 3-D channel routing problem is to connect the terminals in each net with a Steiner tree (wire) in G using as few layers as possible and as short wires as possible in such a way that wires for distinct nets are disjoint. This paper shows that the problem is intractable. We also show that a sparse set of ν 2-terminal nets can be routed in a 3-D channel with O(√ν) layers using wires of length O(√ν).

Internal power losses in lumped-element impedance matching circuits are formulated by means of Q factors of the elements and port impedances to be matched. Assuming that Q factors are relatively high, the above mentioned loss is expressed by a simple formula containing only the tangents of the impedances. The formula is a powerful tool for such applications that put emphasis on power efficiency as wireless power transfer. As well as the formulation, we illustrate some design examples with the derived formula: design of the least lossy L-section circuit and two-stage low-pass ladder. The examples provide ready-to-use knowledge for low-loss matching design.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

This paper introduces a novel Direct Digital Frequency Synthesizer based on Complementary Dual-Phase Latch-Based sequencing method. Compared to conventional Direct Digital Frequency Synthesizer using Flip-Flop as synchronizing element, the proposed architecture allows to double the data sampling rate while trading-off area and Power Efficiency. Digital domain modulations can be easily implemented by using a Direct Digital Frequency Synthesizer. However, due to performance limitations, CMOS-based applications have been almost exclusively restricted to VHF, UHF and L bands. This work aims to increase the operation speed and extend the applicability of this technology to Multi-band Multi-standard wireless systems operating up to 2.7 GHz. The design features a 24 bits pipelined Phase Accumulator and a 14x10 bits Phase to Amplitude Converter. The Phase to Amplitude Converter module is compressed by using Quarter Wave Symmetry technique and is entirely made up of combinational logic inserted into 12 Complementary Dual-Phase Latch-Based pipeline stages. The logic is represented in the form of Sum of Product terms obtained from a 14x10 bits sinusoidal Look-Up-Table. The proposed Direct Digital Frequency Synthesizer is designed and simulated based on 65nm CMOS standard-cell technology. A maximum data sampling rate of 6.8 GS/s is expected. Estimated Spurious Free Dynamic Range and Power Efficiency are 61 dBc and 22 mW/(GS/s) respectively.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.