Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

With the widespread popularity of a large number of social platforms, an increasing number of new words gradually appear. However, such new words have made some NLP tasks like word segmentation more challenging. Therefore, new word detection is always an important and tough task in NLP. This paper aims to extract new words using the BiLSTM+CRF model which added some features selected by us. These features include word length, part of speech (POS), contextual entropy and degree of word coagulation. Comparing to the traditional new word detection methods, our method can use both the features extracted by the model and the features we select to find new words. Experimental results demonstrate that our model can perform better compared to the benchmark models.

After the work of Impagliazzo and Rudich (STOC, 1989), the black box framework has become one of the main research domain of cryptography. However black box techniques say nothing about non-black box techniques such as making use of zero-knowledge proofs. Brakerski et al. introduced a new black box framework named augmented black box framework, in which they gave a zero-knowledge proof oracle in addition to a base primitive oracle (TCC, 2011). They showed a construction of a non-interactive zero knowledge proof system based on a witness indistinguishable proof system oracle. They presented augmented black box construction of chosen ciphertext secure public key encryption scheme based on chosen plaintext secure public key encryption scheme and augmented black box separation between one-way function and key agreement. In this paper we simplify the work of Brakerski et al. by introducing a proof system oracle without witness indistinguishability, named coin-free proof system oracle, that aims to give the same construction and separation results of previous work. As a result, the augmented black box framework becomes easier to handle. Since our oracle is not witness indistinguishable, our result encompasses the result of previous work.

Automatic disassembly of railway fasteners is of great significance for improving the efficiency of replacing rails. The accurate positioning of fastener is the key factor to realize automatic disassembling. However, most of the existing literature mainly focuses on fastener region positioning and the literature on accurate positioning of fasteners is scarce. Therefore, this paper constructed a visual inspection system for accurate positioning of fastener (VISP). At first, VISP acquires railway image by image acquisition subsystem, and then the subimage of fastener can be obtained by coarse-to-fine method. Subsequently, the accurate positioning of fasteners can be completed by three steps, including contrast enhancement, binarization and spike region extraction. The validity and robustness of the VISP were verified by vast experiments. The results show that VISP has competitive performance for accurate positioning of fasteners. The single positioning time is about 260ms, and the average positioning accuracy is above 90%. Thus, it is with theoretical interest and potential industrial application.

Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.

With the rapid evolution and increase of cyberthreats in recent years, it is necessary to detect and understand it promptly and precisely to reduce the impact of cyberthreats. A darknet, which is an unused IP address space, has a high signal-to-noise ratio, so it is easier to understand the global tendency of malicious traffic in cyberspace than other observation networks. In this paper, we aim to capture global cyberthreats in real time. Since multiple hosts infected with similar malware tend to perform similar behavior, we propose a system that estimates a degree of synchronizations from the patterns of packet transmission time among the source hosts observed in unit time of the darknet and detects anomalies in real time. In our evaluation, we perform our proof-of-concept implementation of the proposed engine to demonstrate its feasibility and effectiveness, and we detect cyberthreats with an accuracy of 97.14%. This work is the first practical trial that detects cyberthreats from in-the-wild darknet traffic regardless of new types and variants in real time, and it quantitatively evaluates the result.

Recently, GaN devices are often adopted in microwave power amplifiers to improve the performances. And many new design methods of microwave power amplifier were proposed. As a result, a high-efficiency and super compact microwave signal source has become easily available. It opens up the way for new microwave heating systems. In this paper, the recent progress on design methods of microwave power amplifier and the applications for microwave heating are described. In the first, a device model of GaN transistor is explained. An equivalent thermal model is introduced into the electrical non-linear equivalent device model. In the second, an active load-pull (ALP) measurement system to design a high-efficiency power amplifier is explained. The principle of the conventional closed-loop ALP system is explained. To avoid the risk of oscillation for the closed-loop ALP system, novel ALP systems are proposed. In the third, a microwave heating system is explained. The heating system monitors the reflection wave. Then, the frequency of the signal source and the phase difference between antennas are controlled to minimize the reflection wave. Absorption efficiency of more than 90% was obtained by the control of frequency and phase. In the last part, applications for a medical instrument is described.

Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although there are some related works for object detection, most of them cannot achieve real-time detection for different scenes. Meanwhile, some real-time detection methods of single-stage have performed poorly in the object detection of small sizes. In order to solve the problem that the training samples are scarce, our work in this paper is improved by constructing the data of a vehicle logo (VLD-45-S), multi-stage pre-training, multi-scale prediction, feature fusion between deeper with shallow layer, dimension clustering of the bounding box, and multi-scale detection training. On the basis of keeping speed, this article improves the detection precision of the vehicle logo. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. Experimental results show that the accuracy and speed of the detection algorithm are improved for the object of small sizes.

Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.

This paper proposes a single coil active shielding method of wireless unmanned aerial vehicle charger for leakage magnetic field reduction. A proposed shielding system eliminates the leakage magnetic field generated from the transmitting and receiving coils by generating the cancelling magnetic field. In order to enhance shielding effectiveness and preserve power transfer efficiency, shielding coil design parameters including radius and turns will analyze. Based on the analysis of coil design, shielding effectiveness and power transfer efficiency will estimate. In addition, shielding current control method corresponding to leakage magnetic field strength and phase will describe. A proposed shielding system has verified by simulations and experiments in terms of the total shielding effectiveness and power transfer efficiency measurements. The simulation and experimental results show that a proposed active shielding system has achieved 68.85% of average leakage magnetic field reduction with 1.92% of power transfer efficiency degradation. The shielding effectiveness and power transfer efficiency variation by coil design has been experimentally verified.

Computation methods using custom circuits are frequently employed to improve the throughput and power efficiency of computing systems. Hardware development, however, can incur significant development costs because designs at the register-transfer level (RTL) with a hardware description language (HDL) are time-consuming. This paper proposes a hardware and software co-design environment, named Mulvery, which is designed for non-professional hardware designer We focus on the similarities between functional reactive programming (FRP) and dataflow in computation. This study provides an idea to design hardware with a dynamic typing language, such as Ruby, using FRP and provides the proof-of-concept of the method. Mulvery, which is a hardware and software co-design tool based on our method, reduces development costs. Mulvery exhibited high performance compared with software processing techniques not equipped with hardware knowledge. According to the experiment, the method allows us to design hardware without degradation of performance. The sample application applied a Laplacian filter to an image with a size of 128×128 and processed a convolution operation within one clock.

This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

Although the classical vector median filter (VMF) has been widely used to suppress the impulse noise in the color image, many thin color curve pixels aligned in arbitrary directions are usually removed out as impulse noise. This serious problem can be solved by the proposed method that can protect the thin curves in arbitrary direction in color image and remove out the impulse noise at the same time. Firstly, samples in the 3x3 filter window are considered to preliminarily detect whether the center pixel is corrupted by impulse noise or not. Then, samples outside a 5x5 filter window are conditionally and partly considered to accurately distinguish the impulse noise and the noise-free pixel. At last, based on the previous outputs, samples on the processed positions in a 3x3 filter window are chosen as the samples of VMF operation to suppress the impulse noise. Extensive experimental results indicate that the proposed algorithm can be used to remove the impulse noise of color image while protecting the thin curves in arbitrary directions.

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

A polyphase sequence set with orthogonality consisting complex elements with unit magnitude, can be expressed by a unitary matrix corresponding to the complex Hadamard matrix or the discrete Fourier transform (DFT) matrix, whose rows are orthogonal to each other. Its matched filter bank (MFB), which can simultaneously output the correlation between a received symbol and any sequence in the set, is effective for constructing communication systems flexibly. This paper discusses the compact design of the MFB of a polyphase sequence set, which can be applied to any sequence set generated by the given logic function. It is primarily focused on a ZCZ code with q-phase or more elements expressed as A(N=qn+s, M=qn-1, Zcz=qs(q-1)), where q, N, M and Zcz respectively denote, a positive integer, sequence period, family size, and a zero correlation zone, since the compact design of the MFB becomes difficult when Zcz is large. It is shown that the given logic function on the ring of integers modulo q generating the ZCZ code gives the matrix representation of the MFB that M-dimensional output vector can be represented by the product of the unitary matrix of order M and an M-dimensional input vector whose elements are written as the sum of elements of an N-dimensional input vector. Since the unitary matrix (complex Hadamard matrix) can be factorized into n-1 unitary matrices of order M with qM nonzero elements corresponding to fast unitary transform, a compact MFB with a minimum number of circuit elements can be designed. Its hardware complexity is reduced from O(MN) to O(qM log q M+N).

Synthetic aperture interferometric radiometer (SAIR) is a powerful sensors for high-resolution imaging. However, because of the observation errors and small number of visibility sampling points, the accuracy of reconstructed images is usually low. To overcome this deficiency, a novel super-resolution imaging (SrI) method based on super-resolution reconstruction idea is proposed in this paper. In SrI method, sparse visibility functions are first measured at different observation locations. Then the sparse visibility functions are utilized to simultaneously construct the fusion visibility function and the fusion imaging model. Finally, the high-resolution image is reconstructed by solving the sparse optimization of fusion imaging model. The simulation results demonstrate that the proposed SrI method has higher reconstruction accuracy and can improve the imaging quality of SAIR effectively.

In IoT (Internet-of-Things) era, the number and variety of hardware devices becomes continuously increasing. Several IoT devices are utilized at infrastructure equipments. How to maintain such IoT devices is a serious concern. Capacitance measurement is one of the powerful ways to detect anomalous states in the structure of the hardware devices. Particularly, measuring capacitance while the hardware device is running is a major challenge but no such researches proposed so far. This paper proposes a capacitance measuring device which measures device capacitance in operation. We firstly combine the AC (alternating current) voltage signal with the DC (direct current) supply voltage signal and generates the fluctuating signal. We supply the fluctuating signal to the target device instead of supplying the DC supply voltage. By effectively filtering the observed current in the target device, the filtered current can be proportional to the capacitance value and thus we can measure the target device capacitance even when it is running. We have implemented the proposed capacitance measuring device on the printed wiring board with the size of 95mm × 70mm and evaluated power consumption and accuracy of the capacitance measurement. The experimental results demonstrate that power consumption of the proposed capacitance measuring device is reduced by 65% in low-power mode from measuring mode and proposed device successfully measured capacitance in 0.002μF resolution.

Plane wave scattering from a circular conducting cylinder and a circular conducting strip has been formulated by equivalent surface currents which are postulated from the scattering geometrical optics (GO) field. Thus derived radiation far fields are found to be the same as those formulated by a conventional physical optics (PO) approximation for both E and H polarizations.

Analyzing aging-induced delay degradations of ring oscillators (ROs) is an effective way to detect recycled field-programmable gate arrays (FPGAs). However, it requires a large number of RO measurements for all FPGAs before shipping, which increases the measurement costs. We propose a cost-efficient recycled FPGA detection method using a statistical performance characterization technique called virtual probe (VP) based on compressed sensing. The VP technique enables the accurate prediction of the spatial process variation of RO frequencies on a die by using a very small number of sample RO measurements. Using the predicted frequency variation as a supervisor, the machine-learning model classifies target FPGAs as either recycled or fresh. Through experiments conducted using 50 commercial FPGAs, we demonstrate that the proposed method achieves 90% cost reduction for RO measurements while preserving the detection accuracy. Furthermore, a one-class support vector machine algorithm was used to classify target FPGAs with around 94% detection accuracy.

Cross-project defect prediction (CPDP) is a research hot recently, which utilizes the data form existing source project to construct prediction model and predicts the defect-prone of software instances from target project. However, it is challenging in bridging the distribution difference between different projects. To minimize the data distribution differences between different projects and predict unlabeled target instances, we present a novel approach called selective pseudo-labeling based subspace learning (SPSL). SPSL learns a common subspace by using both labeled source instances and pseudo-labeled target instances. The accuracy of pseudo-labeling is promoted by iterative selective pseudo-labeling strategy. The pseudo-labeled instances from target project are iteratively updated by selecting the instances with high confidence from two pseudo-labeling technologies. Experiments are conducted on AEEEM dataset and the results show that SPSL is effective for CPDP.