High-speed flash ADCs are useful in high-speed applications such as communication receivers. Due to offset voltage variation in the sub-micron processes, the power consumption and the area increase significantly to suppress variation. As an alternative to suppressing the variation, we have developed a flash ADC architecture that selects the comparators based on offset voltage ranking for reference generation. Specifically, with the order statistics as a basis, our method selects the minimum number of comparators to obtain equally spaced reference values. Because the proposed ADC utilizes offset voltages as references, no resistor ladder is required. We also developed a time-domain sorting mechanism for the offset voltages to achieve on-chip comparator selection. We first perform a detailed analysis of the order statistics based selection method and then design a 4-bit ADC in a commercial 65-nm process and perform transistor-level simulation. When using 127 comparators, INLs of 20 virtual chips are in the range of -0.34LSB/+0.29LSB to -0.83LSB/+0.74LSB, and DNLs are in the range of -0.33LSB/+0.24LSB to -0.77LSB/+1.18LSB at 1-GS/s operation. Our ADC achieves the SNDR of 20.9dB at Nyquist-frequency input and the power consumption of 0.84mW.

To support secure database management, a number of value-added encryption schemes have been studied including order-revealing encryption (ORE) schemes. One of outstanding features of ORE schemes is the efficiency of range queries in an encrypted form. Compared to existing encryption methods, ORE leads to an increase in the length of ciphertexts. To improve the efficiency of ORE schemes in terms of the length of ciphertext, a new ORE scheme with shorter ciphertext has been proposed by Kim. In this paper, we revisit Kim's ORE scheme and show that the length of ciphertexts is not as short as analyzed in their paper. We also introduce a simple modification reducing the memory requirement than existing ORE schemes.

In spatially distributed systems such as smart buildings and intelligent transportation systems, control of spatio-temporal patterns is an important issue. In this paper, we consider a finite-horizon optimal spatio-temporal pattern control problem where the pattern is specified by a signal spatio-temporal logic formula over finite traces, which will be called an SSTLf formula. We give the syntax and Boolean semantics of SSTLf. Then, we show linear encodings of the temporal and spatial operators used in SSTLf and we convert the problem into a mixed integer programming problem. We illustrate the effectiveness of this proposed approach through an example of a heat system in a room.

In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with a joint linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence — the channel exploration cycle — by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

This paper proposes a reference-clock-less quick-start-up CDR that resumes from a stand-by state only with a 4-bit preamble utilizing a phase generator with an embedded Time-to-Digital Converter (TDC). The phase generator detects 1-UI time interval by using its internal TDC and works as a self-tunable digitally-controlled delay line. Once the phase generator coarsely tunes the recovered clock period, then the residual time difference is finely tuned by a fine Digital-to-Time Converter (DTC). Since the tuning resolution of the fine DTC is matched by design with the time resolution of the TDC that is used as a phase detector, the fine tuning completes instantaneously. After the initial coarse and fine delay tuning, the feedback loop for frequency tracking is activated in order to improve Consecutive Identical Digits (CID) tolerance of the CDR. By applying the frequency tracking architecture, the proposed CDR achieves more than 100bits of CID tolerance. A prototype implemented in a 65nm bulk CMOS process operates at a 0.9-2.15Gbps continuous rate. It consumes 5.1-8.4mA in its active state and 42μA leakage current in its stand-by state from a 1.0V supply.

Image quality assessment (IQA) is a fundamental metric for image processing tasks (e.g., compression). With full-reference IQAs, traditional IQAs, such as PSNR and SSIM, have been used. Recently, IQAs based on deep neural networks (deep IQAs), such as LPIPS and DISTS, have also been used. It is known that image scaling is inconsistent among deep IQAs, as some perform down-scaling as pre-processing, whereas others instead use the original image size. In this paper, we show that the image scale is an influential factor that affects deep IQA performance. We comprehensively evaluate four deep IQAs on the same five datasets, and the experimental results show that image scale significantly influences IQA performance. We found that the most appropriate image scale is often neither the default nor the original size, and the choice differs depending on the methods and datasets used. We visualized the stability and found that PieAPP is the most stable among the four deep IQAs.

To facilitate the reuse of environmental waste heat in our society, we have developed high-efficiency flexible thermoelectric power generators (TEPGs). In this study, we investigated the thermoelectromotive force (TEMF) and output power of a prototype device with 50 pairs of Π-type structures using a homemade measurement system for flexible TEPGs in order to evaluate their characteristics along the thickness direction. The prototype device consisted of C fabrics (CAFs) used as p-type materials, NiCu fabrics (NCFs) used as n-type materials, and Ag fabrics (AGFs) used as metal electrodes. Applying a temperature difference of 5K, we obtained a TEMF of 150μV and maximum output power of 6.4pW. The obtained TEMF was smaller than that expected from the Seebeck coefficients of each fabric, which is considered to be mainly because of the influence of contact thermal resistance at the semiconductor-fabric/AGF interfaces.

The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.

This paper focuses on verification for reactive system specifications. A reactive system is an open system that continuously interacts with an uncontrollable external environment, and it must often be highly safe and reliable. However, realizability checking for a given specification is very costly, so we need effective methods to detect and analyze defects in unrealizable specifications to refine them efficiently. We introduce a systematic characterization on necessary conditions of realizability. This characterization is based on quantifications for inputs and outputs in early and late behaviors and reveals four essential aspects of realizability: exhaustivity, strategizability, preservability and stability. Additionally, the characterization derives new necessary conditions, which enable us to classify unrealizable specifications systematically and hierarchically.

A power-scalable wideband distributed amplifier is proposed. For reducing the power consumption of this power-hungry amplifier, it is efficient to lower the supply voltage. However, there is a hurdle owing to the transistor threshold voltage. In this work, a CMOS deeply depleted channel process is employed to overcome the hurdle.

Privacy violations via spy cameras are becoming increasingly serious. With the recent advent of various smart home IoT devices, such as smart TVs and robot vacuum cleaners, spycam attacks that steal users' information are being carried out in more unpredictable ways. In this paper, we introduce a new spycam attack on a mobile WebVR environment. It is performed by a web attacker who maliciously accesses the back-facing cameras of victims' mobile devices while they are browsing the attacker's WebVR site. This has the power to allow the attacker to capture victims' surroundings even at the desired field of view through sophisticated content placement in VR scenes, resulting in serious privacy breaches for mobile VR users. In this letter, we introduce a new threat facing mobile VR and show that it practically works with major browsers in a stealthy manner.

The characteristics of antenna array, like sensor location, gain and phase response are rarely perfectly known in realistic situations. Location errors usually have a serious impact on the DOA (direction of arrival) estimation. In this paper, a novel array location calibration method of MUSIC (multiple signal classification) algorithm based on the virtual interpolated array is proposed. First, the paper introduces the antenna array positioning scheme. Then, the self-calibration algorithm of FIR-Winner filter based on virtual interpolation array is derived, and its application restriction are also analyzed. Finally, by simulating the different location errors of antenna array, the effectiveness of the proposed method is validated.

In order to obtain a feasible schedule of a hard real-time system, heuristic based techniques are the solution of choice. In the last few years, optimization solvers have gained attention from research communities due to their capability of handling large number of constraints. Recently, some works have used integer linear programming (ILP) for solving mono processor scheduling of real-time systems. In fact, ILP is commonly used for static scheduling of multiprocessor systems. However, two main solvers are used to solve the problem indistinctly. But, which one is the best for obtaining a schedulable system for hard real-time systems? This paper makes a comparison of two well-known optimization software packages (CPLEX and GUROBI) for the problem of finding a feasible schedule on monoprocessor hard real-time systems.

S-parameters of InGaAs/InAlAs triple-barrier resonant tunneling diodes (TBRTDs) were measured up to 67 GHz with various mesa areas and various bias voltages. Admittance data of bare TBRTDs are deembedded and evaluated by getting rid of parasitic components with help of electromagnetic simulations for particular fabricated device structures. Admittance spectroscopy up to 67 GHz is applied for bare TBRTDs for the first time and a Kramers-Kronig relation with Lorentzian function is found to be a consistent model for the admittance especially in cases of low bias conditions. Relaxation time included in the Lorentzian function are tentatively evaluated as the order of several pico second.

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop an optimization algorithm for minimizing the empirical risk under the sign constraints. The algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd+d2) computational cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an ε-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

We propose a sampling method that incorporates a normally distributed sampling series for EEG measurements using compressed sensing. We confirmed that the ADC sampling count and amount of wirelessly transmitted data can be reduced by 11% while maintaining a reconstruction accuracy similar to that of the conventional method.

Graphene has been expected as an alternative material for copper interconnects in which resistance increases and reliability deteriorates in nanoscale. While the principle advantages are verified by simulations and experiments, they have not been put into practical use due to the immaturity of the manufacturing process leading to mass production. On the other hand, recent steady progress in the fabrication process has increased the possibility of practical application. In this paper, I will review the recent advances and the latest prospects for conductor applications of graphene centered on interconnects. The possibility of further application utilizing the unique characteristics of graphene is discussed.

In this letter, we give a new construction of a family of sequences of period pk-1 with low correlation value by using additive and multiplicative characters over Galois rings. The new constructed sequence family has family size (M-1)(pk-1)rpkr(e-1) and alphabet size Mpe. Based on the characters sum over Galois rings, an upper bound on the correlation of this sequence family is presented.

In recent years, deep neural networks (DNNs) have made a significant impact on a variety of research fields and applications. One drawback of DNNs is that it requires a huge amount of dataset for training. Since it is very expensive to ask experts to label the data, many non-expert data collection methods such as web crawling have been proposed. However, dataset created by non-experts often contain corrupted labels, and DNNs trained on such dataset are unreliable. Since DNNs have an enormous number of parameters, it tends to overfit to noisy labels, resulting in poor generalization performance. This problem is called Learning with Noisy labels (LNL). Recent studies showed that DNNs are robust to the noisy labels in the early stage of learning before over-fitting to noisy labels because DNNs learn the simple patterns first. Therefore DNNs tend to output true labels for samples with noisy labels in the early stage of learning, and the number of false predictions for samples with noisy labels is higher than for samples with clean labels. Based on these observations, we propose a new sample selection approach for LNL using the number of false predictions. Our method periodically collects the records of false predictions during training, and select samples with a low number of false predictions from the recent records. Then our method iteratively performs sample selection and training a DNNs model using the updated dataset. Since the model is trained with more clean samples and records more accurate false predictions for sample selection, the generalization performance of the model gradually increases. We evaluated our method on two benchmark datasets, CIFAR-10 and CIFAR-100 with synthetically generated noisy labels, and the obtained results which are better than or comparative to the-state-of-the-art approaches.