This paper describes a programmable differential bandgap reference (PD-BGR) for ultra-low-power IoT (Internet-of-Things) edge node devices. The PD-BGR consists of a current generator (CG) and differential voltage generator (DVG). The CG is based on a bandgap reference (BGR) and generates an operating current and a voltage, while the DVG generates another voltage from the current. A differential voltage reference can be obtained by taking the voltage difference from the voltages. The PD-BGR can produce a programmable differential output voltage by changing the multipliers of MOSFETs in a differential pair and resistance with digital codes. Simulation results showed that the proposed PD-BGR can generate 25- to 200-mV reference voltages with a 25-mV step within a ±0.7% temperature inaccuracy in a temperature range from -20 to 100°C. A Monte Carlo simulation showed that the coefficient of the variation in the reference was within 1.1%. Measurement results demonstrated that our prototype chips can generate stable programmable differential output voltages, almost the same results as those of the simulation. The average power consumption was only 88.4 nW, with a voltage error of -4/+3 mV with 5 samples.

Traditional intuitionistic fuzzy sets and hesitant fuzzy sets will lose some information while representing vague information, to avoid this problem, this paper constructs weighted generalized hesitant fuzzy sets by remaining multiple intuitionistic fuzzy values and giving them corresponding weights. For weighted generalized hesitant fuzzy elements in weighted generalized hesitant fuzzy sets, the paper defines some basic operations and proves their operation properties. On this basis, the paper gives the comparison rules of weighted generalized hesitant fuzzy elements and presents two kinds of aggregation operators. As for weighted generalized hesitant fuzzy preference relation, this paper proposes its definition and computing method of its corresponding consistency index. Furthermore, the paper designs an ensemble learning algorithm based on weighted generalized hesitant fuzzy sets, carries out experiments on 6 datasets in UCI database and compares with various classification algorithms. The experiments show that the ensemble learning algorithm based on weighted generalized hesitant fuzzy sets has better performance in all indicators.

Today's electronic devices must meet many requirements, such as those related to performance, limits to the radiated electromagnetic field, size, etc. For such a design, the requirement is to have a solution that simultaneously meets multiple objectives that sometimes include conflicting requirements. In addition, it is also necessary to consider uncertain parameters. This paper proposes a new combination of statistical analysis using the Polynomial Chaos (PC) method for dealing with the random and multi-objective satisfactory design using the Preference Set-based Design (PSD) method. The application in this paper is an Electromagnetic Interference (EMI) filter for a practical case, which includes plural element parameters and uncertain parameters, which are resistors at the source and load, and the performances of the attenuation characteristics. The PC method generates simulation data with high enough accuracy and good computational efficiency, and these data are used as initial data for the meta-modeling of the PSD method. The design parameters of the EMI filter, which satisfy required performances, are obtained in a range by the PSD method. The authors demonstrate the validity of the proposed method. The results show that applying a multi-objective design method using PSD with a statistical method using PC to handle the uncertain problem can be applied to electromagnetic designs to reduce the time and cost of product development.

Many modern regular expression engines employ various extensions to give more expressive support for real-world usages. Among the major extensions employed by many of the modern regular expression engines are backreferences and lookaheads. A question of interest about these extended regular expressions is their expressive power. Previous works have shown that (i) the extension by lookaheads does not enhance the expressive power, i.e., the expressive power of regular expressions with lookaheads is still regular, and that (ii) the extension by backreferences enhances the expressive power, i.e., the expressive power of regular expressions with backreferences (abbreviated as rewb) is no longer regular. This raises the following natural question: Does the extension of regular expressions with backreferences by lookaheads enhance the expressive power of regular expressions with backreferences? This paper answers the question positively by proving that adding either positive lookaheads or negative lookaheads increases the expressive power of rewb (the former abbreviated as rewblp and the latter as rewbln). A consequence of our result is that neither the class of finite state automata nor that of memory automata (MFA) of Schmid[2] (which corresponds to regular expressions with backreferenes but without lookaheads) corresponds to rewblp or rewbln. To fill the void, as a first step toward building such automata, we propose a new class of automata called memory automata with positive lookaheads (PLMFA) that corresponds to rewblp. The key idea of PLMFA is to extend MFA with a new kind of memories, called positive-lookahead memory, that is used to simulate the backtracking behavior of positive lookaheads. Interestingly, our positive-lookahead memories are almost perfectly symmetric to the capturing-group memories of MFA. Therefore, our PLMFA can be seen as a natural extension of MFA that can be obtained independently of its original intended purpose of simulating rewblp.

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.

The resistor variation can severely affect current reference sources, which may vary up to ±40% in scaled CMOS processes. In addition, such variations make the opamp design challenging and increase the design margin, impacting power consumption. This paper proposes a Time-Based Current Source (TBCS): a robust and process-scalable reference current source suitable for switched-capacitor (SC) circuits. We construct a delay-locked-loop (DLL) to lock the current-starved inverter with the reference clock, enabling the use of the settled current directly as a reference current. Since the load capacitors determine the delay, the generated current is decoupled from resistor values and enables a robust reference current source. The prototype TBCS fabricated in 28nm CMOS achieved a minimal area of 1200um2. The current variation is suppressed to half compared to BGR based current sources, confirmed in extensive PVT variation simulations. Moreover, when used as the opamp's bias, TBCS achieves comparable opamp GBW to an ideal current source.

This paper proposes individual computation processes of the partial demodulation reference signal (DM-RS) sequence in a synchronization signal (SS)/physical broadcast channel (PBCH) block to be used to detect the radio frame timing based on SS/PBCH block index detection for New Radio (NR) initial access. We present the radio frame timing detection probability using the proposed partial DM-RS sequence detection method that is applied subsequent to the physical-layer cell identity (PCID) detection in five tapped delay line (TDL) models in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. Computer simulation results show that by using the proposed method, the radio frame timing detection probabilities of almost 100% and higher than 90% are achieved for the LOS and NLOS channel models, respectively, at the average received signal-to-noise power ratio (SNR) of 0dB with the frequency stability of a local oscillator in a set of user equipment (UE) of 5ppm at the carrier frequency of 4GHz.

This paper proposes time division multiplexing (TDM) based reference signal (RS) multiplexing for faster-than-Nyquist (FTN) signaling using orthogonal frequency division multiplexing (OFDM). We also propose a subframe structure in which a cyclic prefix (CP) is appended to only the TDM based RS block and the first FTN symbol to achieve accurate estimation of the channel response in a multipath fading channel with low CP overhead. Computer simulation results show that the loss in the required average received SNR satisfying the average block error rate (BLER) of 10-2 using the proposed TDM based RS multiplexing from that with ideal channel estimation is suppressed to within approximately 1.2dB and 1.7dB for QPSK and 16QAM, respectively. This is compared to when the improvement ratio of the spectral efficiency from CP-OFDM is 1.31 with the rate-1/2 turbo code. We conclude that the TDM based RS multiplexing with the associated CP multiplexing is effective in achieving accurate channel estimation for FTN signaling using OFDM.

Since the quality of video streaming services is degraded due to the encoding, network congestion, and lack of a playout buffer, the normality of services needs to be monitored by gathering the quality measured at the end clients. When measuring quality at the end clients, the computational power should be sufficiently low, the bitstream information cannot be accessed for the quality estimation due to the encryption, and reference video cannot be used at the end clients. Therefore, metadata-based models have been developed and standardized that take metadata such as the resolution, framerate, and bitrate, and stalling information as input and calculate the quality. However, calculated quality for linear TV and video on demand (VoD) services cannot be compared because metadata-based models cannot calculate the impacts of codec strategies (e.g., H.264/AVC, H.265/HEVC, and AV1) and configurations (e.g., 1-pass encoding for linear TV or 2-pass encoding for VoD) on the quality. To take into account the impact of codec strategies and configurations, coefficients of metadata-based model need to be optimized per codec strategy and configuration using subjective quality. However, extensive subjective assessment tests are difficult to frequently conduct because they are expensive and time consuming and need to be conducted by video quality experts. Therefore, to monitor the quality of several types of video streaming services (e.g., linear TV and VoD) and to compare these qualities, a derivation procedure is proposed for obtaining coefficients of metadata-based models using a full-reference model. To validate the procedure, extensive subjective assessment tests were conducted. Finally, it is shown that three metadata-based models (i.e., the P.1203.1 mode 0 model, extended P.1203.1 mode 0 model, and model proposed by Yamagishi et al.) based on the proposed procedure using the video multimethod assessment fusion (VMAF) algorithm estimate quality accurately in terms of root mean squared error.

This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method.

In the design of electrical contacts, it is required to pursue a solution which satisfies simultaneously multi-objective (electrical, mechanical, and thermal) performances including conflicting requirements. Preference Set-Based Design (PSD) has been proposed as practical procedure of the fuzzy set-based design method. This brief paper newly attempts to propose a concurrent design method by PSD to electrical contact, specifically a design of a shape of cantilever in relay contacts. In order to reduce the calculation (and/or experimental) cost, this paper newly attempt to apply Design of Experiments (DoE) for meta-modeling to PSD. The number of the calculation for the meta-modeling can be reduced to $rac{1}{729}$ by using DoE. The design parameters (width and length) of a cantilever for drive an electrical contact, which satisfy required performance (target deflection), are obtained in ranges successfully by PSD. The validity of the design parameters is demonstrated by numerical modeling.

In recent years, agreement technologies have garnered interest among agents in the field of multi-agent systems. Automated negotiation is one of the agreement technologies, in which agents negotiate with each other to make an agreement so that they can solve conflicts between their preferences. Although most agents keep their own preferences private, it is necessary to estimate the opponent's preferences to obtain a better agreement. Therefore, opponent modeling is one of the most important elements in automated negotiating strategy. A frequency model is widely used for opponent modeling because of its robustness against various types of strategy while being easy to implement. However, existing frequency models do not consider the opponent's proposal speed and the transition of offers. This study proposes a novel frequency model that considers the opponent's behavior using two main elements: the offer ratio and the weighting function. The offer ratio stabilizes the model against changes in the opponent's offering speed, whereas the weighting function takes the opponent's concession into account. The two experiments conducted herein show that our proposed model is more accurate than other frequency models. Additionally, we find that the agent with the proposed model performs with a significantly higher utility value in negotiations.

This paper describes designing a new pedestrian navigation system using a comprehensive framework called the pedestrian navigation concept reference model (PNCRM). We implement this system as a publicly-available smartphone application and evaluate its positioning performance near Omiya station's western entrance. We also evaluate users' subjective impressions of the system using a questionnaire. In both cases, promising results are obtained, showing that the PNCRM can be used as a tool for designing pedestrian navigation systems, allowing such systems to be created systematically.

The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.

For high definition (HD) videos, the 3D-High Efficiency Video Coding (3D-HEVC) reference algorithm incurs dramatically highly computation loads. Therefore, with the demands for the real-time processing of HD video, a hardware implementation is necessary. In this paper, a reconfigurable architecture is proposed that can support both median filtering preprocessing and mean filtering preprocessing to satisfy different scene depth maps. The architecture sends different instructions to the corresponding processing elements according to different scenarios. Mean filter is used to process near-range images, and median filter is used to process long-range images. The simulation results show that the designed architecture achieves an averaged PSNR of 34.55dB for the tested images. The hardware design for the proposed virtual view synthesis system operates at a maximum clock frequency of 160MHz on the BEE4 platform which is equipped with four Virtex-6 FF1759 LX550T Field-Programmable Gate Array (FPGA) for outputting 720p (1024×768) video at 124fps.

In this paper, we propose the low voltage CMOS current mode reference circuit using self-regulator with adaptive biasing technique. It drastically reduces the line sensitivity (LS) of the output voltage and the power supply voltage dependence of the temperature coefficient (TC). The self-regulator used in the proposed circuit adaptively generates the minimum voltage required the reference core circuit following the PVT (process, voltage and temperature) conditions. It makes possible to improve circuit performances instead of slightly increasing minimum power supply voltage. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, LS is reduced to 0.0065%/V under 0.8V < VDD < 3.0V. TC is 67.6ppm/°C under the condition that the temperature range is from -40°C to 125°C and VDD range is from 0.8V to 3.0V. The power supply rejection ratio (PSRR) is less than -80.4dB when VDD is higher than 0.8V and the noise frequency is 100Hz. According to the simulation results, we could confirm that the performances of the proposed circuit are improved compared with the conventional circuit.

With the recent advances in e-commerce, it has become important to recommend not only mass-produced daily items, such as books, but also items that are not mass-produced. In this study, we present an algorithm for real estate recommendations. Automatic property recommendations are a highly difficult task because no identical properties exist in the world, occupied properties cannot be recommended, and users rent or buy properties only a few times in their lives. For the first step of property recommendation, we predict users' preferences for properties by combining content-based filtering and Multi-Layer Perceptron (MLP). In the MLP, we use not only attribute data of users and properties, but also deep features extracted from property floor plan images. As a result, we successfully predict users' preference with a Matthews Correlation Coefficient (MCC) of 0.166.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.

The rising systems programming language Rust is fast, efficient and memory safe. However, improperly dereferencing raw pointers in Rust causes new safety problems. In this paper, we present a detailed analysis into these problems and propose a practical hybrid approach to detecting unsafe raw pointer dereferencing behaviors. Our approach employs pattern matching to identify functions that can be used to generate illegal multiple mutable references (We define them as thief function) and instruments the dereferencing operation in order to perform dynamic checking at runtime. We implement a tool named UnsafeFencer and has successfully identified 52 thief functions in 28 real-world crates*, of which 13 public functions are verified to generate multiple mutable references.