This paper discusses how to compute the parametric sensitivity function in continuous-time Markov chains (CTMC). The sensitivity function is the first derivative of the steady-state probability vector regarding a CTMC parameter. Since the sensitivity function is given as a solution of linear equations with a sparse matrix, several linear equation solvers are available to obtain it. In this paper, we consider Jacobi and successive-over relaxation as variants of the Gauss-Seidel algorithm. In addition, we develop an algorithm based on the Takahashi method for the sensitivity function. In numerical experiments, we comprehensively evaluate the performance of these algorithms from the viewpoint of computation time and accuracy.

Impact of sampling frequency and the number of quantization bit of analog-to-digital conversion (ADC) in a direct detection lightwave system using Kramers-Kronig (KK) relation, which has been attracting attention in recent years, are numerically investigated. We studied the effect of spectral broadening caused by nonlinear operations (logarithm, square root) of the KK algorithm when the frequency gap (shift frequency) between the modulated signal and the optical tone is varied. We found that reception performances depend on both the ADC bandwidth and the relative positions of the optical tone and the spectrum. Spectral broadening caused by the logarithm operation of the KK algorithm is found to be the dominant factor of signal distortion in an ADC bandwidth limited system. We studied the effect of the number of quantization bit on the error vector magnitude (EVM) of KK relation based reception in a carrier-to-signal power ratio (CSPR) adjustable transmission system. We found that performances of KK relation based receiver can be improved by increasing the number of quantization bits. For minimum-phase-condition satisfied KK receiver, the required number of quantization bit was found to be 5 bits or more for detection of QPSK, 16-QAM and 64-QAM-modulated signal after 20-km transmission.

Massive multiple-input multiple-output (MIMO) is an enabling technology for next-generation wireless systems because it provides significant improvements in data rates compared to existing small-scale MIMO systems. However, the increased number of antennas results in high computational complexity for data detection, and requires more efficient detection algorithms. In this paper, we propose a new data detector based on a box-constrained complex-valued dichotomous coordinate descent (BCC-DCD) algorithm for large-scale MIMO systems. The proposed detector involves two steps. First, a transmitted data vector is detected using the BCC-DCD algorithm with a large number of iterations and high solution precision. Second, an improved soft output is generated by reapplying the BCC-DCD algorithm, but with a considerably smaller number of iterations and 1-bit solution precision. Numerical results demonstrate that the proposed method outperforms existing advanced detectors while possessing lower complexity. Specifically, the proposed method provides significantly better detection performance than a BCC-DCD algorithm with similar complexity. The performance advantage increases as the signal-to-noise ratio and the system size increase.

This paper introduces a novel skin-stretcher device for gently urging head rotation. The device pulls and/or pushes the skin on the user's neck by using servo motors. The user is induced to rotate his/her head based on the sensation caused by the local stretching of skin. This mechanism informs the user when and how much the head rotation is requested; however it does not force head rotation, i.e., it allows the user to ignore the stimuli and to maintain voluntary movements. We implemented a prototype device and analyzed the performance of the skin stretcher as a human-in-the-loop system. Experimental results define its fundamental characteristics, such as input-output gain, settling time, and other dynamic behaviors. Features are analyzed, for example, input-output gain is stable within the same installation condition, but various between users.

We present a card-based protocol for computing a three-input majority using six cards. The protocol essentially consists of performing a simple XOR protocol two times. Compared to the existing protocols, our protocol does not require private operations other than choosing cards.

This paper discusses a static analysis problem, called absolute consistency problem, for relational schema mappings. A given schema mapping is said to be absolutely consistent if every source instance has a corresponding target instance. Absolute consistency is an important property because it guarantees that data exchange never fails for any source instance. Originally, for XML schema mappings, the absolute consistency problem was defined and its complexity was investigated by Amano et al. However, as far as the authors know, there are no known results for relational schema mappings. In this paper, we focus on relational schema mappings such that both the source and the target schemas have functional dependencies, under the assumption that mapping rules are defined by constant-free tuple-generating dependencies. In this setting, we show that the absolute consistency problem is in coNP. We also show that it is solvable in polynomial time if the tuple-generating dependencies are full and the size of the left-hand side of each functional dependency is bounded by some constant. Finally, we show that the absolute consistency problem is coNP-hard even if the source schema has no functional dependency and the target schema has only one; or each of the source and the target schemas has only one functional dependency such that the size of the left-hand side of the functional dependency is at most two.

The communications quality of content delivery networks (CDNs), which are geographically distributed networks that have been optimized for content delivery, deteriorates when interflow congestion conditions are severe. Herein, we propose an adaptive server and path switching scheme that is based on the estimated acquisition throughput of each path. We also provide simulation results that show our proposed method can provide higher throughput performance levels than existing methods.

LSTM network have shown to outperform in facial expression recognition of video sequence. In view of limited representation ability of single-layer LSTM, a hierarchical attention model with enhanced feature branch is proposed. This new network architecture consists of traditional VGG-16-FACE with enhanced feature branch followed by a cross-layer LSTM. The VGG-16-FACE with enhanced branch extracts the spatial features as well as the cross-layer LSTM extracts the temporal relations between different frames in the video. The proposed method is evaluated on the public emotion databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

Sketch face recognition is to match sketch face images to photo face images. The main challenge of sketch face recognition is learning discriminative feature representations to ensure intra-class compactness and inter-class separability. However, traditional sketch face recognition methods encouraged samples with the same identity to get closer, and samples with different identities to be further, and these methods did not consider the intra-class compactness of samples. In this paper, we propose triplet-margin-center loss to cope with the above problem by combining the triplet loss and center loss. The triplet-margin-center loss can enlarge the distance of inter-class samples and reduce intra-class sample variations simultaneously, and improve intra-class compactness. Moreover, the triplet-margin-center loss applies a hard triplet sample selection strategy. It aims to effectively select hard samples to avoid unstable training phase and slow converges. With our approach, the samples from photos and from sketches taken from the same identity are closer, and samples from photos and sketches come from different identities are further in the projected space. In extensive experiments and comparisons with the state-of-the-art methods, our approach achieves marked improvements in most cases.

Cross-project defect prediction (CPDP) is a feasible solution to build an accurate prediction model without enough historical data. Although existing methods for CPDP that use only labeled data to build the prediction model achieve great results, there are much room left to further improve on prediction performance. In this paper we propose a Semi-Supervised Discriminative Feature Learning (SSDFL) approach for CPDP. SSDFL first transfers knowledge of source and target data into the common space by using a fully-connected neural network to mine potential similarities of source and target data. Next, we reduce the differences of both marginal distributions and conditional distributions between mapped source and target data. We also introduce the discriminative feature learning to make full use of label information, which is that the instances from the same class are close to each other and the instances from different classes are distant from each other. Extensive experiments are conducted on 10 projects from AEEEM and NASA datasets, and the experimental results indicate that our approach obtains better prediction performance than baselines.

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.

In this paper, we propose a decentralized probabilistic frequency-block activation control method for the cellular downlink. The aim of the proposed method is to increase the downlink system throughput within the system coverage by adaptively controlling the individual activation of each frequency block at all base stations (BSs) to achieve inter-cell interference coordination (ICIC) and traffic load balancing. The proposed method does not rely on complicated inter-BS cooperation. It uses only the inter-BS information exchange regarding the observed system throughput levels with the neighboring BSs. Based on the shared temporal system throughput information, each BS independently controls online the activation of their respective frequency blocks in a probabilistic manner, which autonomously achieves ICIC and load balancing among BSs. Simulation results show that the proposed method achieves greater system throughput and a faster convergence rate than the conventional online probabilistic activation/deactivation control method. We also show that the proposed method successfully tracks dynamic changes in the user distribution generated due to mobility.

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.

This paper presents a weighted spatial filter (WSF) design method based on direction of arrival (DOA) estimates for a novel array configuration called a sum and difference composite co-array. A sum and difference composite co-array is basically a combination of sum and difference co-arrays. Our configuration can realize higher degrees of freedom (DOF) with the sum co-array part at a calculation cost lower than those of the other sparse arrays. To further enhance the robustness of our proposed sum and difference composite co-array we design an optimal beam pattern by WSF based on the information of estimated DOAs. Performance of the proposed system and the DOA estimation accuracy of close-impinging waves are evaluated through computer simulations.

Recently, we demonstrated a rapid-single-flux-quantum NOT gate comprising a toggle storage loop. In this paper, we present our design and operation of a NOR gate that is a straightforward extension of the NOT gate by attaching a confluence buffer. Parameter margins wider than ±28% were confirmed in simulation. Functional tests using Nb integrated circuits demonstrated correct NOR operation with a bias margin of ±21%.

This paper presents two ultra-low voltage and high performance VCO ICs with two novel transformer-based harmonic tuned tanks. The first proposed harmonic tuned tank effectively shapes the pseudo-square drain-node voltage waveform for close-in phase noise reduction. To compensate the voltage drop caused by the transformer, an improved second tank is proposed. It not only has tuned harmonic impedance but also provides a voltage gain to enlarge the output voltage swing over supply voltage limitation. The VCO with second tank exhibits over 3 dB better phase noise performance in 1/f2 region among all tuning range. The two VCO ICs are designed, fabricated and measured on wafer in 45-nm SOI CMOS technology. With only 0.3 V supply voltage, the proposed two VCO ICs exhibit best phase noise of -123.3 and -127.2 dBc/Hz at 10 MHz offset and related FoMs of -191.7 and -192.2 dBc/Hz, respectively. The frequency tuning ranges of them are from 14.05 to 15.14 GHz and from 14.23 to 15.68 GHz, respectively.

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.

This paper presents a design of CMOS transimpedance amplifier (TIA) and peaking inductor for high speed, low power and small area. To realize high density integration of optical I/O, area reduction is an important figure as well as bandwidth, power and so on. To determine design parameters of multi-stage inverter-type TIA (INV-TIA) with peaking inductors, we derive a simplified model of the bandwidth and the energy per bit. Multi-layered on-chip inductors are designed for area-effective inductive peaking. A 5-stage INV-TIA with 3 peaking inductors is fabricated in a 65-nm CMOS. By using multi-layered inductors, 0.02 mm2 area is achieved. Measurement results show 45 Gb/s operation with 49 dBΩ transimpedance gain and 4.4 mW power consumption. The TIA achieves 98 fJ/bit energy efficiency.

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.

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.