Integer codes are defined by error-correcting codes over integers modulo a fixed positive integer. In this paper, we show that the construction of integer codes can be reduced into the cases of prime-power moduli. We can efficiently search integer codes with small prime-power moduli and can construct target integer codes with a large composite-number modulus. Moreover, we also show that this prime-factorization reduction is useful for the construction of self-orthogonal and self-dual integer codes, i.e., these properties in the prime-power moduli are preserved in the composite-number modulus. Numerical examples of integer codes and generator matrices demonstrate these facts and processes.

We developed and applied a new circuit, called the “Self-controllable Voltage Level (SVL)” circuit, not only to expand both “write” and “read” stabilities, but also to achieve a low stand-by power and data holding capability in a single low power supply, 90-nm, 2-kbit, six-transistor CMOS SRAM. The SVL circuit can adaptively lower and higher the word-line voltages for a “read” and “write” operation, respectively. It can also adaptively lower and higher the memory cell supply voltages for the “write” and “hold” operations, and “read” operation, respectively. This paper focuses on the “hold” characteristics and the standby power dissipations (PST) of the developed SRAM. The average PST of the developed SRAM is only 0.984µW, namely, 9.57% of that (10.28µW) of the conventional SRAM at a supply voltage (VDD) of 1.0V. The data hold margin of the developed SRAM is 0.1839V and that of the conventional SRAM is 0.343V at the supply voltage of 1.0V. An area overhead of the SVL circuit is only 1.383% of the conventional SRAM.

In this paper, we study self-dual cyclic codes of length n over the ring R=Z4[u]/, where n is an odd positive integer. We define a new Gray map φ from R to Z42. It is a bijective map and maintains the self-duality. Furthermore, we give the structures of the generators of cyclic codes and self-dual cyclic codes of odd length n over the ring R. As an application, some self-dual codes of length 2n over Z4 are obtained.

We discuss Nash equilibria in combinatorial auctions with item bidding. Specifically, we give a characterization for the existence of a Nash equilibrium in a combinatorial auction with item bidding when valuations by n bidders satisfy symmetric and subadditive properties. By this characterization, we can obtain an algorithm for deciding whether a Nash equilibrium exists in such a combinatorial auction.

Multi-task joint sparse representation (MTJSR) is one kind of efficient multi-task learning (MTL) method for solving different problems together using a shared sparse representation. Based on the learning mechanism in human, which is a self-paced learning by gradually training the tasks from easy to difficult, I apply this mechanism into MTJSR, and propose a multi-task joint sparse representation with self-paced learning (MTJSR-SP) algorithm. In MTJSR-SP, the self-paced learning mechanism is considered as a regularizer of optimization function, and an iterative optimization is applied to solve it. Comparing with the traditional MTL methods, MTJSR-SP has more robustness to the noise and outliers. The experimental results on some datasets, i.e. two synthesized datasets, four datasets from UCI machine learning repository, an oxford flower dataset and a Caltech-256 image categorization dataset, are used to validate the efficiency of MTJSR-SP.

Although Wolf Pack Algorithm (WPA) is a novel optimal algorithm with good performance, there is still room for improvement with respect to its convergence. In order to speed up its convergence and strengthen the search ability, we improve WPA with the Differential Evolution (DE) elite set strategy. The new proposed algorithm is called the WPADEES for short. WPADEES is faster than WPA in convergence, and it has a more feasible adaptability for various optimizations. Six standard benchmark functions are applied to verify the effects of these improvements. Our experiments show that the performance of WPADEES is superior to the standard WPA and other intelligence optimal algorithms, such as GA, DE, PSO, and ABC, in several situations.

Conceptual data modeling is an important activity in database design. However, it is difficult for novice learners to master its skills. In the conceptual data modeling, learners are required to detect and correct errors of their artifacts by themselves because modeling tools do not assist these activities. We call such activities self checking, which is also an important process. However, the previous research did not focus on it and/or the data collection of self checks. The data collection of self checks is difficult because self checking is an internal activity and self checks are not usually expressed. Therefore, we developed a method to help learners express their self checks by reflecting on their artifact making processes. In addition, we developed a system, KIfU3, which implements this method. We conducted an evaluation experiment and showed the effectiveness of the method. From the experimental results, we found out that (1) the novice learners conduct self checks during their conceptual data modeling tasks; (2) it is difficult for them to detect errors in their artifacts; (3) they cannot necessarily correct the errors even if they could identify them; and (4) there is no relationship between the numbers of self checks by the learners and the quality of their artifacts.

In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.

In this paper, we present self-interference (SI) cancellation techniques in the digital domain for in-band full-duplex systems employing orthogonal frequency division multiple access (OFDMA) in the downlink (DL) and single-carrier frequency division multiple access (SC-FDMA) in the uplink (UL), as in the long-term evolution (LTE) system. The proposed techniques use UL subcarrier nulling to accurately estimate SI channels without any UL interference. In addition, by exploiting the structures of the transmitter imperfection and the known or estimated parameters associated with the imperfection, the techniques can further improve the accuracy of SI channel estimation. We also analytically derive the lower bound of the mean square error (MSE) performance and the upper bound of the signal-to-interference-plus-noise ratio (SINR) performance for the techniques, and show that the performance of the techniques are close to the bounds. Furthermore, by utilizing the SI channel estimates and the nonlinear signal components of the SI caused by the imperfection to effectively eliminate the SI, the proposed techniques can achieve SINR performance very close to the one in perfect SI cancellation. Finally, because the SI channel estimation of the proposed techniques is performed in the time domain, the techniques do not require symbol time alignment between SI and UL symbols.

This paper presents a self-calibrating dynamic latched comparator with a stochastic offset voltage detector that can be realized by using simple digital circuitry. An offset voltage of the comparator is compensated by using a statistical calibration scheme, and the offset voltage detector uses the uncertainty in the comparator output. Thanks to the simple offset detection technique, all the calibration circuitry can be synthesized using only standard logic cells. This paper also gives a design methodology that can provide the optimal design parameters for the detector on the basis of fundamental statistics, and the correctness of the design methodology was statistically validated through measurement. The proposed self-calibrating comparator system was fabricated in a 180 nm 1P6M CMOS process. The prototype achieved a 38 times improvement in the three-sigma of the offset voltage from 6.01 mV to 158 µV.

Self-paced learning (SPL) gradually trains the data from easy to hard, and includes more data into the training process in a self-paced manner. The advantage of SPL is that it has an ability to avoid bad local minima, and the system can improve the generalization performance. However, SPL's system needs an expert to judge the complexity of data at the beginning of training. Generally, this expert does not exist in the beginning, and is learned by gradually training the samples. Based on this consideration, we add an uncertainty of complexity judgment into SPL's system, and propose a self-paced learning with uncertainty prior (SPUP). For efficiently solving our system optimization function, an iterative optimization and statistical simulated annealing method are introduced. The final experimental results indicate that our SPUP has more robustness to the outlier and achieves higher accuracy and less error than SPL.

This paper presents a new form of gap waveguide technology - the half-height-pin gap waveguide. The gap waveguide technology is a new transmission line technology introduced recently, which makes use of the stopband of wave propagation created by a pair of parallel plates, one PEC (perfect electric conductor) and one PMC (perfect magnetic conductor), with an air gap in between less than a quarter of the wavelength at operation frequency. Applying this PEC/PMC gap plate structure to ridged waveguides, rectangular hollow waveguides and microstrip lines, we can have the ridged gap waveguides, groove gap waveguides and inverted gap waveguide microstrip lines, respectively, without requiring a conductive or galvanic contact between the upper PEC and the lower PMC plates. This contactless property of the gap waveguide technology relaxes significantly the manufacturing requirements for devices and antennas at millimeter wave frequencies. PMC material does not exist in nature, and an artificial PMC boundary can be made by such as periodic pin array with the pin length about a quarter wavelength. However, the quarter-wavelength pins, referred to as the full-height pins, are often too long for manufacturing. In order to overcome this difficulty, a new half-height-pin gap waveguide is introduced. The working principles and Q factors for the half-height-pin gap waveguides are described, analyzed and verified with measurements in this paper. It is concluded that half-height-pin gap waveguides have similar Q factors and operation bandwidth to the full-height-pin gap waveguides. As an example of the applications, a high gain planar array antenna at V band by using the half-height-pin gap waveguide has been designed and is presented in the paper with a good reflection coefficient and high aperture efficiency.

An FPGA-based hardware hand sign recognition system was proposed in our previous work. The hand sign recognition system consisted of a preprocessing and a self-organizing map (SOM)-Hebb classifier. The training of the SOM-Hebb classifier was carried out by an off-chip computer using training vectors given by the system. The recognition performance was reportedly improved by adding perturbation to the training data. The perturbation was added manually during the process of image capture. This paper proposes a new off-chip training method with automatic performance improvement. To improve the system's recognition performance, the off-chip training system adds artificially generated perturbation to the training feature vectors. Advantage of the proposed method compared to additive scale perturbation to image is its low computational cost because the number of feature vector elements is much less than that of pixels contained in image. The feasibility of the proposed off-chip training was tested in simulations and experiments using American sign language (ASL). Simulation results showed that the proposed perturbation computation alters the feature vector so that it is same as the one obtained by a scaled image. Experimental results revealed that the proposed off-chip training improved the recognition accuracy from 78.9% to 94.3%.

This paper reports the development of a landmine visualization system based on complex-valued self-organizing map (CSOM) by employing one-dimensional (1-D) array of taper-walled tapered slot antennas (TSAs). Previously we constructed a high-density two-dimensional array system to observe and classify complex-amplitude texture of scattered wave. The system has superiority in its adaptive distinction ability between landmines and other clutters. However, it used so many (144) antenna elements with many mechanical radio-frequency (RF) switches and cables that it has difficulty in its maintenance and also requires long measurement time. The 1-D array system proposed here uses only 12 antennas and adopts electronic RF switches, resulting in easy maintenance and 1/4 measurement time. Though we observe stripe noise specific to this 1-D system, we succeed in visualization with effective solutions.

Wireless video sensor networks address problems, such as low power consumption of sensor nodes, low computing capacity of nodes, and unstable channel bandwidth. To transmit video of distributed video coding in wireless video sensor networks, we propose an efficient scalable distributed video coding scheme. In this scheme, the scalable Wyner-Ziv frame is based on transmission of different wavelet information, while the Key frame is based on transmission of different residual information. A successive refinement of side information for the Wyner-Ziv and Key frames are proposed in this scheme. Test results show that both the Wyner-Ziv and Key frames have four layers in quality and bit-rate scalable, but no increase in complexity of the encoder.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

The Werewolf game is a kind of role-playing game in which players have to guess other players' roles from their speech acts (what they say). In this game, players have to estimate other players' beliefs and intentions, and try to modify others' intentions. The BDI model is a suitable one for this game, because it explicitly has notions of mental states, i.e. beliefs, desires and intentions. On the other hand, in this game, players' beliefs are not completely known. Consequently, in many cases it is difficult for players to choose a unique strategy; in other words, players frequently have to maintain probabilistic intentions. However, the conventional BDI model does not have the notion of probabilistic mental states. In this paper, we propose an extension of BDI logic that can handle probabilistic mental states and use it to model some situations in the Werewolf game. We also show examples of deductions concerning those situations. We expect that this study will serve as a basis for developing a Werewolf game agent based on BDI logic in the future.

Let $mathbb{F}_q$ be a finite field of q elements, $R=mathbb{F}_q+umathbb{F}_q$ (u2=0) and D2n= be a dihedral group of order n. Left ideals of the group ring R[D2n] are called left dihedral codes over R of length 2n, and abbreviated as left D2n-codes over R. Let n be a positive factor of qe+1 for some positive integer e. In this paper, any left D2n-code over R is uniquely decomposed into a direct sum of concatenated codes with inner codes Ai and outer codes Ci, where Ai is a cyclic code over R of length n and Ci is a linear code of length 2 over a Galois extension ring of R. More precisely, a generator matrix for each outer code Ci is given. Moreover, a formula to count the number of these codes is obtained, the dual code for each left D2n-code is determined and all self-dual left D2n-codes over R are presented, respectively.

Group signature (GS) schemes guarantee anonymity of the actual signer among group members. Previous GS schemes assume that randomness in signing is never exposed. However, in the real world, full randomness exposure can be caused by implementation problems (e.g., using a bad random number generator). In this paper, we study (im)possibility of achieving anonymity against full randomness exposure. First, we formulate a new security model for GS schemes capturing full randomness exposure. Next, we clarify that it is impossible to achieve full-anonymity against full randomness exposure without any secure component (e.g., a tamper-proof module or a trusted outside storage). Finally, we show a possibility result that selfless-anonymity can be achieved against full randomness exposure. While selfless-anonymity is weaker than full-anonymity, it is strong enough in practice. Our transformation is quite simple; and thus, previous GS schemes used in real-world systems can be easily replaced by a slight modification to strengthen the security.

In this paper, we propose a novel design method of two channel critically sampled compactly supported biorthogonal graph wavelet filter banks with half-band kernels. First of all, we use the polynomial half-band kernels to construct a class of biorthogonal graph wavelet filter banks, which exactly satisfy the PR (perfect reconstruction) condition. We then present a design method of the polynomial half-band kernels with the specified degree of flatness. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernels have the specified zeros at λ=2. Therefore the constraints of flatness are satisfied at both of λ=0 and λ=2, and then the resulting graph wavelet filters have the flat spectral responses in passband and stopband. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error of lowpass (highpass) filter in the band of interest by using the remaining degree of freedom. Finally, several examples are designed to demonstrate the effectiveness of the proposed design method.