An interpretation method of inversion phenomena is newly proposed for backward transient scattered field components for both E- and H-polarizations when an ultra-wideband (UWB) pulse wave radiated from a line source is incident on a two-dimensional metal cylinder covered with a lossless dielectric medium layer (coated metal cylinder). A time-domain (TD) asymptotic solution, which is referred to as a TD saddle point technique (TD-SPT), is derived by applying the SPT in evaluating a backward transient scattered field which is expressed by an integral form. The TD-SPT is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series, thereby being able to extract and calculate any backward transient scattered field component from a response waveform. The TD-SPT is useful in understanding the response waveform of a backward transient scattered field by a coated metal cylinder because it can give us the peak value and arrival time of any field component, namely DGO and RGO components, and interpret analytically inversion phenomenon of any field component. The accuracy, validity, and practicality of the TD-SPT are clarified by comparing it with two kinds of reference solutions.

This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.

Deep unfolding is a promising deep-learning technique, whose network architecture is based on expanding the recursive structure of existing iterative algorithms. Although deep unfolding realizes convergence acceleration, its theoretical aspects have not been revealed yet. This study details the theoretical analysis of the convergence acceleration in deep-unfolded gradient descent (DUGD) whose trainable parameters are step sizes. We propose a plausible interpretation of the learned step-size parameters in DUGD by introducing the principle of Chebyshev steps derived from Chebyshev polynomials. The use of Chebyshev steps in gradient descent (GD) enables us to bound the spectral radius of a matrix governing the convergence speed of GD, leading to a tight upper bound on the convergence rate. Numerical results show that Chebyshev steps numerically explain the learned step-size parameters in DUGD well.

Sparse code division multiple access (SCMA) is a non-orthogonal multiple access (NOMA) technology that can improve frequency band utilization and allow many users to share quite a few resource elements (REs). This paper uses the modulation of lattice theory to develop a systematic construction procedure for the design of SCMA codebooks under Gaussian channel environments that can achieve near-optimal designs, especially for cases that consider large-scale SCMA parameters. However, under the condition of large-scale SCMA parameters, the mother constellation (MC) points will overlap, which can be solved by the method of the partial dimensions transformation (PDT). More importantly, we consider the upper bounded error probability of the signal transmission in the AWGN channels, and design a codeword allocation method to reduce the inter symbol interference (ISI) on the same RE. Simulation results show that under different codebook sizes and different overload rates, using two different message passing algorithms (MPA) to verify, the codebook proposed in this paper has a bit error rate (BER) significantly better than the reference codebooks, moreover the convergence time does not exceed that of the reference codebooks.

Currently, wireless power transmission technology is being developed for capsule endoscopes. By removing the battery, the capsule endoscope is miniaturized, the number of images that can be taken increases, and the risk of harmful substances leaking from the battery when it is damaged inside the body is avoided. Furthermore, diagnostic accuracy is improved by adjusting the directivity of radio waves according to the position of the capsule endoscope to improve efficiency and adjusting the number of images to be taken according to position by real-time position estimation. In this study, we report the result of position estimation in a high-definition numerical human body model and in an experiment on an electromagnetic phantom.

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.

In Internet of Things (IoT) applications, system-on-chip (SoCs) with embedded processors are widely used. As an embedded processor, RISC-V, which is license-free and has an extensible instruction set, is receiving attention. However, designing such embedded processors requires an enormous effort to achieve a highly efficient microarchitecture in terms of performance, power consumption, and circuit area, as well as the design verification of running complex software, including modern operating systems such as Linux. In this paper, we propose a method for directly describing the RTL structure of a pipelined RISC-V processor with cache memories, a memory management unit (MMU), and an AXI bus interface using the C++ language. This pipelined processor C++ model serves as a functional simulator of the complete RISC-V core, whereas our C2RTL framework translates the processor C++ model into a cycle-accurate RTL description in the Verilog-HDL and RTL-equivalent C model. Our processor design methodology using the C2RTL framework is unique compared to other existing methodologies because both the simulation and RTL models are derived from the same C++ source, which greatly simplifies the design verification and optimization processes. The effectiveness of our design methodology is demonstrated on a RISC-V processor that runs Linux OS on an FPGA board, achieving a significantly short simulation time of the original C++ processor model and RTL-equivalent C model in comparison to a commercial RTL simulator.

Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.

A Josephson parametric oscillator (JPO) is an interesting system from the viewpoint of quantum optics because it has two stable self-oscillating states and can deterministically generate quantum cat states. A theoretical proposal has been made to operate a network of multiple JPOs as a quantum annealer, which can solve adiabatically combinatorial optimization problems at high speed. Proof-of-concept experiments have been actively conducted for application to quantum computations. This article provides a review of the mechanism of JPOs and their application as a quantum annealer.

When collecting images, owing to the influence of shooting equipment, shooting environment, and other factors, often low-illumination images with insufficient exposure are obtained. For low-illumination images, it is necessary to improve the contrast. In this paper, a digital color image contrast enhancement method based on luminance weight adjustment is proposed. This method improves the contrast of the image and maintains the detail and nature of the image. In the proposed method, the illumination of the histogram equalization image and the adaptive gamma correction with weighted distribution image are adjusted by the luminance weight of w1 to obtain a detailed image of the bright areas. Thereafter, the suppressed multi-scale retinex (MSR) is used to process the input image and obtain a detailed image of the dark areas. Finally, the luminance weight w2 is used to adjust the illumination component of the detailed images of the bright and dark areas, respectively, to obtain the output image. The experimental results show that the proposed method can enhance the details of the input image and avoid excessive enhancement of contrast, which maintains the naturalness of the input image well. Furthermore, we used the discrete entropy and lightness order error function to perform a numerical evaluation to verify the effectiveness of the proposed method.

As modern CMOS circuits fabricated with advanced technology nodes are becoming more and more susceptible to soft-errors, many hardened latches have been proposed for reliable LSI designs. We reveal for the first time that production defects in such hardened latches can cause two serious problems: (1) these production defects are difficult to detect with conventional scan test and (2) these production defects can reduce the reliability of hardened latches. This paper systematically addresses these two problems with three major contributions: (1) Post-Test Vulnerability Factor (PTVF), a first-of-its-kind metric for quantifying the impact of production defects on hardened latches, (2) a novel Scan-Test-Aware Hardened Latch (STAHL) design that has the highest defect coverage compared to state-of-the-art hardened latch designs, and (3) an STAHL-based scan test procedure. Comprehensive simulation results demonstrate the accuracy of the proposed PTVF metric and the effectiveness of the STAHL-based scan test. As the first comprehensive study bridging the gap between hardened latch design and LSI testing, the findings of this paper will significantly improve the soft-error-related reliability of LSI designs for safety-critical applications.

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

Convolutional Neural Network (CNN) has made extraordinary progress in image classification tasks. However, it is less effective to use CNN directly to detect image manipulation. To address this problem, we propose an image filtering layer and a multi-scale feature fusion module which can guide the model more accurately and effectively to perform image manipulation detection. Through a series of experiments, it is shown that our model achieves improvements on image manipulation detection compared with the previous researches.

Use case modeling is popular to represent the functionality of the system to be developed, and it consists of two parts: a use case diagram and use case descriptions. Use case descriptions are structured text written in natural language, and the usage of natural language can lead to poor descriptions such as ambiguous, inconsistent, and/or incomplete descriptions. Poor descriptions lead to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of the produced use case model. This paper proposes a technique to automate detecting bad smells of use case descriptions, i.e., symptoms of poor descriptions. At first, to clarify bad smells, we analyzed existing use case models to discover poor use case descriptions concretely and developed the list of bad smells, i.e., a catalog of bad smells. Some of the bad smells can be refined into measures using the Goal-Question-Metric paradigm to automate their detection. The main contributions of this paper are the developed catalog of bad smells and the automated detection of these bad smells. We have implemented an automated smell detector for 22 bad smells at first and assessed its usefulness by an experiment. As a result, the first version of our tool got a precision ratio of 0.591 and a recall ratio of 0.981. Through evaluating our catalog and the automated tool, we found additional six bad smells and two metrics. Then, we obtained the precision of 0.596 and the recall of 1.000 by our final version of the automated tool.

Because processes run concurrently in multitask systems, the size of the state space grows exponentially. Therefore, it is not straightforward to formally verify that such systems enjoy desired properties. Real-time constrains make the formal verification more challenging. In this paper, we propose the following to address the challenge: (1) a way to model multitask real-time systems as observational transition systems (OTSs), a kind of state transition systems, (2) a way to describe their specifications in CafeOBJ, an algebraic specification language, and (3) a way to verify that such systems enjoy desired properties based on such formal specifications by writing proof scores, proof plans, in CafeOBJ. As a case study, we model Fischer's protocol, a well-known real-time mutual exclusion protocol, as an OTS, describe its specification in CafeOBJ, and verify that the protocol enjoys the mutual exclusion property when an arbitrary number of processes participates in the protocol*.

We consider a reliable decentralized supervisory control problem for discrete event systems in the inference-based framework. This problem requires us to synthesize local supervisors such that the controlled system achieves the specification and is nonblocking, even if local control decisions of some local supervisors are not available for making the global control decision. In the case of single-level inference, we introduce a notion of reliable 1-inference-observability and show that reliable 1-inference-observability together with controllability and Lm(G)-closedness is a necessary and sufficient condition for the existence of a solution to the reliable decentralized supervisory control problem.

We propose a technique based on Brillouin optical time domain analysis for measuring loss and crosstalk in few-mode fibers (FMFs). The proposed technique extracts the loss and crosstalk of a specific mode in FMFs from the Brillouin gains and Brillouin gain coefficients measured under two different conditions in terms of the frequency difference between the pump and probe lights. The technique yields the maximum loss and crosstalk at a splice point by changing the electrical field injected into an FMF as the pump light. Experiments demonstrate that the proposed technique can measure the maximum loss and crosstalk of the LP11 mode at a splice point in a two-mode fiber.

Real-time and scalable multicast services are of paramount importance to Industrial Internet of Things (IIoT) applications. To realize these services, the multicast algorithm should, on the one hand, ensure the maximum delay of a multicast session not exceeding its upper delay bound. On the other hand, the algorithm should minimize session costs. As an emerging networking paradigm, Software-defined Networking (SDN) can provide a global view of the network to multicast algorithms, thereby bringing new opportunities for realizing the desired multicast services in IIoT environments. Unfortunately, existing SDN-based multicast (SDM) algorithms cannot meet the real-time and scalable requirements simultaneously. Therefore, in this paper, we focus on SDM algorithm design for IIoT environments. To be specific, the paper first converts the multicast tree construction problem for SDM in IIoT environments into a delay-bounded least-cost shared tree problem and proves that it is an NP-complete problem. Then, the paper puts forward a shared tree (ST) algorithm called SDM4IIoT to compute suboptimal solutions to the problem. The algorithm consists of five steps: 1) construct a delay-optimal shared tree; 2) divide the tree into a set of subpaths and a subtree; 3) optimize the cost of each subpath by relaxing the delay constraint; 4) optimize the subtree cost in the same manner; 5) recombine them into a shared tree. Simulation results show that the algorithm can provide real-time support that other ST algorithms cannot. In addition, it can achieve good scalability. Its cost is only 20.56% higher than the cost-optimal ST algorithm. Furthermore, its computation time is also acceptable. The algorithm can help to realize real-time and scalable multicast services for IIoT applications.

Translation of long and complex sentence has always been a challenge for machine translation. In recent years, neural machine translation (NMT) has achieved substantial progress in modeling the semantic connection between words in a sentence, but it is still insufficient in capturing discourse structure information between clauses within complex sentences, which often leads to poor discourse coherence when translating long and complex sentences. On the other hand, the hypotactic structure, a main component of the discourse structure, plays an important role in the coherence of discourse translation, but it is not specifically studied. To tackle this problem, we propose a novel Chinese-English NMT approach that incorporates the hypotactic structure knowledge of complex sentences. Specifically, we first annotate and build a hypotactic structure aligned parallel corpus to provide explicit hypotactic structure knowledge of complex sentences for NMT. Then we propose three hypotactic structure-aware NMT models with three different fusion strategies, including source-side fusion, target-side fusion, and both-side fusion, to integrate the annotated structure knowledge into NMT. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that the proposed method can significantly improve the translation performance and enhance the discourse coherence of machine translation.