The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the l1 norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the l1 norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than l1 for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.

In this paper, an experimental method is proposed for extracting the primary and secondary parameters of transmission lines with frequency dispersion. So far, there is no report of these methods being applied to transmission lines with frequency dispersion. This paper provides an experimental evaluation means of transmission lines with frequency dispersion and clarifies the issues when applying the proposed method. In the proposed experimental method, unnecessary components such as connectors are removed by using a simple de-embedding method. The frequency response of the primary and secondary parameters extracted by using the method reproduced all dispersion characteristics of a transmission line with frequency dispersion successfully. It is demonstrated that an accurate RLGC equivalent-circuit model is obtained experimentally, which can be used to quantitatively evaluate the frequency/time responses of shielded-FPC with frequency dispersion and to validate RLGC equivalent-circuit models extracted by using electromagnetic field analysis.

In this letter, firstly, a novel adaptive beamformer using independent component analysis (ICA) algorithm is proposed. By this algorithm, the ambiguity of amplitude and phase resulted from blind source separation is removed utilizing the special structure of array manifolds matrix. However, there might exist great calibration error when the powers of interferences are far larger than that of desired signal at many applications such as sonar, radio astronomy, biomedical engineering and earthquake detection. As a result, this will lead to a significant reduction in separation performance. Then, a new method based on the combination of ICA and primary component analysis (PCA) is proposed to recover the desired signal's amplitude under strong interference. Finally, computer simulation is carried out to indicate the effectiveness of our methods. The simulation results show that the proposed methods can obtain higher SNR and more accurate power estimation of desired signal than diagonal loading sample matrix inversion (LSMI) and worst-case performance optimization (WCPO) method.

Convolutional neural networks (CNNs) have dominated a range of applications, from advanced manufacturing to autonomous cars. For energy cost-efficiency, developing low-power hardware for CNNs is a research trend. Due to the large input size, the first few convolutional layers generally consume most latency and hardware resources on hardware design. To address these challenges, this paper proposes an innovative architecture named SLIT to extract feature maps and reconstruct the first few layers on CNNs. In this reconstruction approach, total multiply-accumulate operations are eliminated on the first layers. We evaluate new topology with MNIST, CIFAR, SVHN, and ImageNet datasets on image classification application. Latency and hardware resources of the inference step are evaluated on the chip ZC7Z020-1CLG484C FPGA with Lenet-5 and VGG schemes. On the Lenet-5 scheme, our architecture reduces 39% of latency and 70% of hardware resources with a 0.456 W power consumption compared to previous works. Even though the VGG models perform with a 10% reduction in hardware resources and latency, we hope our overall results will potentially give a new impetus for future studies to reach a higher optimization on hardware design. Notably, the SLIT architecture efficiently merges with most popular CNNs at a slightly sacrificing accuracy of a factor of 0.27% on MNIST, ranging from 0.5% to 1.5% on CIFAR, approximately 2.2% on ImageNet, and remaining the same on SVHN databases.

A ring-resonator type of electrode (RRTE) has been proposed to detect the circulating tumor cell (CTC) for evaluation of the current cancer progression and malignancy in clinical applications. Main emphasis is placed on the identification sensitivity for the lossy materials that can be found in biomedical fields. At first, the possibility of the CTC detection was numerically considered to calculate the resonant frequency of the RRTE catching the CTC, and it was evident that the RRTE with the cell has the resonant frequency inherent in the cell featured by its complex permittivity. To confirm the numerical consideration, the BaTiO3 particle, whose size was similar to that of the CTC, was inserted in the RRTE instead of the CTC as a preliminary experiment. Next, the resonant frequencies of the RRTE with internal organs of the beef cattle such as liver, lung, and kidney were measured for evaluation of the lossy materials such as the CTC, and degraded Q curves were observed because the Q-factors inherent in the internal organs were usually low due to the poor loss tangents. To overcome such difficulty, the RRTE, the oscillator circuit consisting of the FET being added, was proposed to improve the identification sensitivity. Comparing the identification sensitivity of the conventional RRTE, it has been improved because the oscillation frequency spectrum inherent in an internal organ could be easily observed thanks to the oscillation condition with negative resistance. Thus, the validity of the proposed technique has been confirmed.

Multi-static Primary Surveillance Radar (MSPSR) has recently attracted attention as a new surveillance technology for civil aviation. Using multiple receivers, Primary Surveillance Radar (PSR) detection performance can be improved by synthesizing the reflection characteristics which change due to the aircraft's position. In this paper, we report experimental results from our proposed optical-fiber-connected passive PSR system with transmit signal installed at the Sendai Airport in Japan. The signal-to noise ratio of experimental data is evaluated to verify moving target detection. In addition, we confirm the operation of the proposed system using a two-receiver setup, to resemble a conventional multi-static radar. Finally, after applying time correction, the delay of the reflected signal from a stationary target remains within the expected range.

A primary-side regulation AC--DC constant voltage control chip is designed, which employs a novel cable compensation technique to improve the precision of the output voltage and pursue a wider load range for regulation. In the proposed controller, constant voltage (CV) is achieved by OSC charging current and current-limiting point adjustment. Meantime, according to different cable lengths, the sampled voltage is regulated by injecting current to pull-down resistance of the system to obtain an accurate output voltage. The proposed chip is implemented in TSMC 0.35,$mu $m 5,V/40,V BCD process, and a 12,V/1,A circuit prototype has been built to verify the proposed control method. Experimental results show that the maximum cable compensation current reaches 43,$mu $A, and the precision of the output voltage is within $pm$ 3% in a wide range of output current from 0 to 1,A.

In this paper, the Quality of Experience (QoE) on Dynamic Adaptive Streaming based on HTTP (DASH) is researched. To study users' experience on DASH, extensive subjective tests are firstly designed and conducted, based on which, we research QoE enhancement in DASH and find that DASH ensures more fluent playback (less stall) than constant bitrate (CBR) streaming to promote users' satisfaction especially in mobile networks. Then we adopt two-way analysis of variance (ANOVA) tests in statistics to identify the effect of specific factors (segment bitrate, bitrate fluctuation pattern, and bitrate switching) that impair users' experience on DASH. The impairment functions are then derived for these influence factors based on the Primacy and Recency Effect, a psychological phenomenon that has been proved to exist in users' experience on DASH in this paper. And the final QoE evaluation model is proposed to provide high correlation assessment for QoE of DASH. The good performance of our QoE model is validated by the subjective tests. In addition, our QoE study on DASH is also applied for QoE management to propose a QoE-based bitrate adaptation strategy, which promotes users' experience on DASH more strongly than the strategy based on QoS.

This paper describes an analytical study of performance of a proposed signal detection scheme that will allow coexistence of an additional radio communication system (generally, secondary system) in the service area where the existing communication system (primary system) is operated. Its performance characteristics are derived by an analytical method based on stochastic theory, which is subsequently validated by software simulation. The main purpose of the detection scheme is to protect the primary system from the secondary system. In such a situation, the signals of the primary system and secondary system may be simultaneously received in the signal detector. One application of such a scheme is D-to-D (Device-to-Device) communication, whose system concept including the detection scheme is briefly introduced. For improved secondary signal detection, we propose the signal cancellation method of the primary system and the feature detection method of the secondary system signal. We evaluate the performance characteristics of the detection scheme in terms of “probability of correct detection”. We reveal that an undesired random component is produced in the feature detection procedure when two different signals are simultaneously received, which degrades the detection performance. Such undesired component is included in the analytical equations. We also clarify that the cancellation scheme improves the performance, when the power ratio of the primary signal to secondary signal is higher than 20-22dB.

We propose a primary traffic based multihop relaying algorithm with cooperative transmission (PTBMR-CT). It enlarges the hop transmission distances to reduce the number of cognitive relays on the route from the cognitive source (CS) to the cognitive destination (CD). In each hop, from the cognitive nodes in a specified area depending on whether the primary source (PS) transmits data to the primary destination (PD), the cognitive node that is farthest away from the cognitive relay that sends data is selected as the other one that receives data. However, when the PS is transmitting data to the PD, from the cognitive nodes in a specified area, another cognitive node is also selected and prepared to be the cognitive relay that receives data of cooperative transmission. Cooperative transmission is performed if the PS is still transmitting data to the PD when the cognitive relay that receives data of the next hop transmission is being searched. Simulation results show that the average number of cognitive relays is reduced by PTBMR-CT compared to conventional primary traffic based farthest neighbor relaying (PTBFNR), and PTBMR-CT outperforms conventional PTBFNR in terms of the average end-to-end reliability, the average end-to-end throughput, the average required transmission power of transmitting data from the CS to the CD, and the average end-to-end transmission latency.

Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.

This paper proposes an adaptive sequential cooperative energy detection scheme for primary user detection in cognitive radio to minimize the detection time while guaranteeing the desired detection accuracy. Simulation results are provided to show the effectiveness of the proposed scheme.

This paper investigates primary signal detection by using a quiet period (QP) in cognitive wireless communications. In particular, we provide an analytical model for studying the impact of QPs on the system performance. Our analysis shows that two successive QPs have a significant impact on system performance. Moreover, the analytical results obtained reveal an optimum period of two successive QPs that maximize system performance.

Transmission power control (TPC) is an important aspects of underlay transmission in the cognitive radio (CR) networks since it is able to avoid the extra interference from secondary transmission which can let the CR user coexist with the primary systems around. However, due to the different coverage of the primary signal and CR signal, combined with the complexity of the wireless communication, the scanning CR transmitter may not detect the existence of the primary systems. It will cause inaccurate TPC which will severely disrupt the primary service. In this letter, we propose a dynamic neighbor coordinated power control scheme that can avoid the misdetection of the primary signal and provide relatively accurate TPC related distance estimation. Simulation results show that the proposed scheme greatly reduces interference to the primary systems while enhancing overall CR network throughput.

Cognitive radio systems offer the opportunity to improve the spectrum utilization by detecting unused frequency bands while avoiding interference to primary users. This paper proposes a new algorithm for spectrum sensing, which is an energy detector using a hybrid (adaptive and fixed) threshold, in order to compensate the weak points of the existing energy detector in the distorted communication channel environment. Simulation results are presented which show that the performance of the new proposed scheme is better than the existing scheme using a fixed threshold or an adaptive threshold. Additionally, the performance is investigated in terms of several parameters such as the mobile speed and the probability of false alarms. The simulation results also show that the proposed algorithm makes the detector highly robust against fading, shadowing, and interference.

In this paper, an information distribution model based on human's behavior is proposed. We also propose dynamic parameters to make the model more practical for real life social network. Subsequently, the simulations are conducted based on the small-world network and its characteristics, and the parameters in the model are analyzed to increase efficiently the power of information distribution. Our study and simulation results show that the proposed model can be used to analyze and predict the effectiveness of information distribution. Moreover, the study also shows how to use the model parameters to increase power of the distribution.

An odd composite number n for which an-1 ≡ 1 (mod n) for all integers a coprime to n is called a Carmichael number. This paper shows that some class of Carmichael numbers which have relatively large prime factors can be recognized in deterministic polynomial time under the assumption of the Extended Riemann Hypothesis (ERH). Also some related problems are discussed.

For determining whether an input number is prime, there are two kinds of algorithms, a primality test and a primality proving. A primality test is very efficient but probabilistic, that is, there are certain errors in determining primality. On the other hand, a primality proving always gives a correct answer but it is not so efficient. Grantham proposed a very interesting problem on the Quadratic Frobenius Test (QFT) which is a primality test. If we negatively solve the problem, then we can construct a primality proving more efficient than any other existing primality proving. To solve Grantham's problem, it is important to study when QFT fails. In this paper, as the first step to solve Grantham's problem, we show two conditions on a given odd composite number n and parameters a,b of QFT such that n passes QFT for a,b. Based on these conditions, we made a computational experiment that may suggest the problem will be negatively solved. Moreover, the two conditions give two algorithms computing a pair (a,b) for which a given odd composite number n passes QFT, where n's prime factorization is known.

We address the problem of routing and wavelength assignment (RWA) considering differentiated reliability (DiR) in WDM mesh networks. The backup resource can be shared by the primary lightpaths. However, both the primary-backup sharing and the different levels of fault tolerance requirement have never been considered together in the literature. In the paper, we consider the dynamic character of the link availability, which is caused by primary-backup sharing. Moreover, the priority of traffic is considered in the paper. The paper focuses on routing under dynamic availability of wavelength resource while the requested reliability of traffic has to be met, using the modified shortest path algorithm. A layered availability model is established based on wavelength layered graph model. Using this model, we propose a new algorithm called dynamic layered availability (DLA) algorithm. We evaluate the performance of the proposed algorithm on the NSFNET.