This paper reports on the ambipolar conduction for the λ-Deoxyribonucleic Acid (DNA) field effect transistor (FET) with 450, 400 and 250 base pair experimentally and theoretically. It was found that the drain current of the p-type DNA/Si FET increased as the ratio of the guanine-cytosine (GC) pair increased and that of the n-type DNA/Si FET decreased as the ratio of the adenine-thymine (AT) pair decreased, and the ratio of the GC pair and AT pair was controlled by the total number of the base pair. In addition, it was found that the hole conduction mechanism of the 400 bp DNA/Si FET was polaron hopping and its activation energy was 0.13eV. By considering the electron affinity of the adenine, thymine, guanine, and cytosine, the ambipolar characteristics of the DNA/Si FET was understood. The holes are injected to the guanine base for the negative gate voltage, and the electrons are injected to the adenine, thymine, and cytosine for the positive gate voltage.

We investigate a problem of finding the minimum, in which each user has a real value, and we want to estimate the minimum of these values under the local differential privacy constraint. We reveal that this problem is fundamentally difficult, and we cannot construct a consistent mechanism in the worst case. Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum. As a measure of easiness, we introduce a parameter α that characterizes the fatness of the minimum-side tail of the user data distribution. As a result, we reveal that the mechanism can achieve O((ln6N/ε2N)1/2α) error without knowledge of α and the error rate is near-optimal in the sense that any mechanism incurs Ω((1/ε2N)1/2α) error. Furthermore, we demonstrate that our mechanism outperforms a naive mechanism by empirical evaluations on synthetic datasets. Also, we conducted experiments on the MovieLens dataset and a purchase history dataset and demonstrate that our algorithm achieves Õ((1/N)1/2α) error adaptively to α.

We propose a novel synthetic-benchmarks generation model using partial time-series regression, called Partial-Regression-Integrated Generic Model (PRIGM). PRIGM abstracts the unique characteristics of the input sensor data into generic time-series data confirming the generation similarity and evaluating the correctness of the synthetic benchmarks. The experimental results obtained by the proposed model with its formula verify that PRIGM preserves the time-series characteristics of empirical data in complex time-series data within 10.4% on an average difference in terms of descriptive statistics accuracy.

At present, the application of different types of memristors in electronics is being deeply studied. Given the nonlinearity characterizing memristors, a circuit with memristors cannot be treated by classical circuit analysis. In this paper, memristor is equivalent to a nonlinear dynamic system composed of linear dynamic system and nonlinear static system by Volterra series. The nonlinear transfer function of memristor is derived. In the complex frequency domain, the n-order complex frequency response of memristor is established by multiple Laplace transform, and the response of MLC parallel circuit is taken as an example to verify. Theoretical analysis shows that the complex frequency domain analysis method of memristor transforms the problem of solving nonlinear circuit in time domain into n times complex frequency domain analysis of linear circuit, which provides an idea for nonlinear dynamic system analysis.

Side-channel analysis is one of the most investigated hardware Trojan detection approaches. However, nearly all the side-channel analysis approaches require golden chips for reference, which are hard to obtain actually. Besides, majority of existing Trojan detection algorithms focus on the data similarity and ignore the Trojan misclassification during the detection. In this paper, we propose a cost-sensitive golden chip-free hardware Trojan detection framework, which aims to minimize the probability of Trojan misclassification during the detection. The post-layout simulation data of voltage variations at different process corners is utilized as a golden reference. Further, a classification algorithm based on the combination of principal component analysis and Naïve bayes is exploited to identify the existence of hardware Trojan with a minimum misclassification risk. Experimental results on ASIC demonstrate that the proposed approach improves the detection accuracy ratio compared with the three detection algorithms and distinguishes the Trojan with only 0.27% area occupies even under ±15% process variations.

Platform-mounted small antennas increase dielectric loss and conductive loss and decrease the radiation efficiency. This paper proposes a novel antenna design method to improve radiation efficiency for platform-mounted small antennas by characteristic mode analysis. The proposed method uses mapping of modal weighting coefficient (MWC) and infinitesimal dipole and evaluate the metal casing with 100mm × 55mm × 23mm as a platform excited by an inverted-F antenna. The simulation and measurement results show that the radiation efficiency of 5% is improved with the whole system from 2.5% of the single antenna.

Recently, machine learning approaches and user movement history analysis on mobile devices have attracted much attention. Generally, we need to apply text data into the word embedding tool for acquiring word vectors as the preprocessing of machine learning approaches. However, it is difficult for mobile devices to afford the huge cost of high-dimensional vector calculation. Thus, a low-cost user behavior and user movement history analysis approach should be considered. To address this issue, firstly, we convert the zip code and street house number into vectors instead of textual address information to reduce the cost of spatial vector calculation. Secondly, we propose a low-cost high-performance semantic and physical distance (real distance) calculation method that applied zip-code-based vectors. Finally, to verify the validity of our proposed method, we utilize the US zip code data to calculate both semantic and physical distances and compare their results with the previous method. The experimental results showed that our proposed method could significantly improve the performance of distance calculation and effectively control the cost to a low level.

Traditional safety analysis techniques have shown difficulties in incorporating dynamically changing structures of CPSs (Cyber-Physical Systems). STPA (System-Theoretic Process Analysis), one of the widely used, needs to unfold and arrange all hidden structures before beginning a full-fledged analysis. This paper proposes an intermediate model “Information Unfolding Model (IUM)” and a process “Information Unfolding Process (IUP)” to unfold dynamic structures which are hidden in CPSs and so help analysts construct control structures in STPA thoroughly.

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.

In response to fast charging systems, Silicon Carbide (SiC) power semiconductor devices are of great interest of the automotive power electronics applications as the next generation of fast charging systems require high voltage batteries. For high voltage battery EVs (Electric Vehicles) over 800V, SiC power semiconductor devices are suitable for 3-phase inverters, battery chargers, and isolated DC-DC converters due to their high voltage rating and high efficiency performance. However, SiC-MOSFETs have two characteristics that interfere with high-speed switching and high efficiency performance operations for SiC MOS-FET applications in automotive power electronics systems. One characteristic is the low voltage rating of the gate-source terminal, and the other is the large internal gate-resistance of SiC MOS-FET. The purpose of this work was to evaluate a proposed hybrid gate drive circuit that could ignore the internal gate-resistance and maintain the gate-source terminal stability of the SiC-MOSFET applications. It has been found that the proposed hybrid gate drive circuit can achieve faster and lower loss switching performance than conventional gate drive circuits by using the current source gate drive characteristics. In addition, the proposed gate drive circuit can use the voltage source gate drive characteristics to protect the gate-source terminals despite the low voltage rating of the SiC MOS-FET gate-source terminals.

The narrow bandwidth limitation of 300-3400Hz on the public switching telephone network results in speech quality deterioration. In this paper, we propose an artificial bandwidth extension approach that reconstructs the missing lower bandwidth of 50-300Hz using sinusoidal synthesis based on the first formant location. Sinusoidal synthesis generates sinusoidal waves with a harmonic structure. The proposed method detects the fundamental frequency using an autocorrelation method based on YIN algorithm, where a threshold processing avoids the false fundamental frequency detection on unvoiced sounds. The amplitude of the sinusoidal waves is calculated in the time domain from the weighted energy of 300-600Hz. In this case, since the first formant location corresponds to the first peak of the spectral envelope, we reconstruct the harmonic structure to avoid attenuating and overemphasizing by increasing the weight when the first formant location is lower, and vice versa. Consequently, the subjective and objective evaluations show that the proposed method reduces the speech quality difference between the original speech signal and the bandwidth extended speech signal.

Computer networks tend to be subjected to the proliferation of mobile demands, therefore it poses a great challenge to guarantee the quality of network service. For real-time systems, the QoS performance bound analysis for the complex network topology and background traffic in modern networks is often difficult. Network calculus, nevertheless, converts a complex non-linear network system into an analyzable linear system to accomplish more accurate delay bound analysis. The existing network environment contains complex network resource allocation schemes, and delay bound analysis is generally pessimistic, hence it is essential to modify the analysis model to improve the bound accuracy. In this paper, the main research approach is to obtain the measurement results of an actual network by building a measurement environment and the corresponding theoretical results by network calculus. A comparison between measurement data and theoretical results is made for the purpose of clarifying the scheme of bandwidth scheduling. The measurement results and theoretical analysis results are verified and corrected, in order to propose an accurate per-flow end-to-end delay bound analytic model for a large-scale scheduling network. On this basis, the instructional significance of the analysis results for the engineering construction is discussed.

This paper addresses single image super-resolution (SR) based on convolutional neural networks (CNNs). It is known that recovery of high-frequency components in output SR images of CNNs learned by the least square errors or least absolute errors is insufficient. To generate realistic high-frequency components, SR methods using generative adversarial networks (GANs), composed of one generator and one discriminator, are developed. However, when the generator tries to induce the discriminator's misjudgment, not only realistic high-frequency components but also some artifacts are generated, and objective indices such as PSNR decrease. To reduce the artifacts in the GAN-based SR methods, we consider the set of all SR images whose square errors between downscaling results and the input image are within a certain range, and propose to apply the metric projection onto this consistent set in the output layers of the generators. The proposed technique guarantees the consistency between output SR images and input images, and the generators with the proposed projection can generate high-frequency components with few artifacts while keeping low-frequency ones as appropriate for the known noise level. Numerical experiments show that the proposed technique reduces artifacts included in the original SR images of a GAN-based SR method while generating realistic high-frequency components with better PSNR values in both noise-free and noisy situations. Since the proposed technique can be integrated into various generators if the downscaling process is known, we can give the consistency to existing methods with the input images without degrading other SR performance.

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state where all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

We propose two systematic constructions of deletion-correcting codes for protecting quantum inforomation. The first one works with qudits of any dimension l, which is referred to as l-adic, but only one deletion is corrected and the constructed codes are asymptotically bad. The second one corrects multiple deletions and can construct asymptotically good codes. The second one also allows conversion of stabilizer-based quantum codes to deletion-correcting codes, and entanglement assistance.

Hansen, Kaplan, Zamir and Zwick (STOC 2019) introduced a systematic way to use “bias” for predicting an assignment to a Boolean variable in the process of PPSZ and showed that their biased PPSZ algorithm achieves a relatively large success probability improvement of PPSZ for Unique 3SAT. We propose an additional way to use “bias” and show by numerical analysis that the improvement gets increased further.

The growing threat of Hardware Trojans (HT) in the System-on-Chips (SoC) industry has given way to the embedded systems researchers to propose a series of detection methodologies to identify and detect the presence of Trojan circuits or logics inside a host design in the various stages of the chip design and manufacturing process. Many state of the art works propose different techniques for HT detection among which the popular choice remains the Side-Channel Analysis (SCA) based methods that perform differential analysis targeting the difference in consumption of power, change in electromagnetic emanation or the delay in propagation of logic in various paths of the circuit. Even though the effectiveness of these methods are well established, the evaluation is carried out on simplistic models such as AES coprocessors and the analytical approaches used for these methods are limited by some statistical metrics such as direct comparison of EM traces or the T-test coefficients. In this paper, we propose two new detection methodologies based on Machine Learning algorithms. The first method consists in applying the supervised Machine Learning (ML) algorithms on raw EM traces for the classification and detection of HT. It offers a detection rate close to 90% and false negative smaller than 5%. In the second method, we propose an outlier/novelty algorithms based approach. This method combined with the T-test based signal processing technique, when compared with state-of-the-art, offers a better performance with a detection rate close to 100% and a false positive smaller than 1%. In different experiments, the false negative is nearly the same level than the false positive and for that reason the authors only show the false positive value on the results. We have evaluated the performance of our method on a complex target design: RISC-V generic processor. Three HTs with their corresponding sizes: 0.53%, 0.27% and 0.09% of the RISC-V processors are inserted for the experimentation. In this paper we provide elaborative details of our tests and experimental process for reproducibility. The experimental results show that the inserted HTs, though minimalistic, can be successfully detected using our new methodology.

Recent localization methods for wireless networks cannot be applied to dynamic networks with unknown topology. To solve this problem, we propose a localization method based on partial correlation analysis in this paper. We evaluate our proposed localization method in terms of accuracy, which shows that our proposed method can achieve high accuracy localization for dynamic networks with unknown topology.

Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier. However, such adversarial samples can be generated only within a very small area around the input data point, which limits the adversarial effectiveness of such samples. To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space. LVAT can generate adversarial samples flexibly, resulting in more adverse effect and thus more effective regularization. The latent space is built by a generative model, and in this paper we examine two different type of models: variational auto-encoder and normalizing flow, specifically Glow. We evaluated the performance of our method in both supervised and semi-supervised learning scenarios for an image classification task using SVHN and CIFAR-10 datasets. In our evaluation, we found that our method outperforms VAT and other state-of-the-art methods.