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This paper presents a high-performance and synthesizable asynchronous pipeline (HPSAP). First, a 4-phase pipeline controlled by the relative-timing (RT) controller is designed. The controller is small (7 gates) and its handshake protocol is highly concurrent, resulting in fewer component delays in cycle time. However, the RT pipeline’s throughput is limited by the inherent reset phase in the 4-phase protocol. Thus, the variant HPSAP pipeline is proposed with the quasi-2phase conversion method. Unlike other existing solutions which aimed at reducing the reset time of the 4-phase protocol, this method imitates the behavior of the 2-phase pipeline and re-activates the reset edge by two steps: 1) replacing all delay-matched units with the maximum delay; 2) adding a small pulse generator on each RT controller. The post-layout HSPICE simulation of a 4-bit 10-stage HPSAP first-in-first-out (FIFO) pipeline indicated a throughput of 5.382 giga data item per second (GDI/s) under SMIC 55nm CMOS technology, which was 77.5% and 14.65% higher than the Click pipeline and Mousetrap pipeline. In the pipeline with processing, a $32\times 32$ bits multiplier was built, and the maximum working frequency of the HPSAP multiplier was faster than Mousetrap and the synchronous counterparts.

The goal of privacy-preserving social graph publishing is to protect individual privacy while preserving graph utility. Node nearest neighbor structure is a crucial social graph utility as it is the basis for many graph analysis tasks. Most existing methods with differential privacy focus on preserving degree distribution yet neglect the maintenance of connections between nodes’ neighbors. Moreover, they require massive noise added to mask the change of a single edge, thereby rendering poor utility on the neighbor structure. As a result, it is tough to preserve high utility on the neighbor structure under differential privacy. To tackle these problems, we propose Priv-NNS, a private graph publishing algorithm to preserve the neighbor structure while guaranteeing individual privacy. This algorithm first decomposes a graph into subgraphs via extracting a nearest neighbor structure around each node. Then to yield node vectors satisfying differential privacy while preserving the neighbor structure, we design a private graph encoding approach with structure-awareness, which learns topological features of the subgraph by maximizing the co-occurrence probability among nodes. During this process, a novel objective perturbation approach with a random term, only requiring a scalar noise rather than a vector noise, is devised to balance the neighbor structure retained against the noise injected. Through formal privacy analysis, we prove that the yielded synthetic graph obeys $\varepsilon $ -edge differential privacy. Experimental results demonstrate that Priv-NNS preserves high utility on the neighbor structure.

An object tracking method based on a minimum-spanning-tree one-class classifier is proposed where the background is regarded as a collection of all other classes except the target. First, two kinds of different target class examples and outlier examples are collected in the recent few frames. Meanwhile, the candidates are sampled by a particle filter in the current frame. Second, using the sparsity-based local discriminant analysis, all examples and candidates are projected into a local discriminative subspace to improve the discriminative ability of one-class classifier. Then, a graph is built on all the target examples by adopting one-to-all sparse reconstruction coefficients rather than pair-wise Euclidean distance and a minimum-spanning-tree one-class classifier is trained on the graph. Finally, the similarity scores of the candidates are evaluated by the trained classifier. The one with the highest score is determined as the tracking result and used to update the training set. The experimental results demonstrate the superior performance of our method in robustness and accuracy to the state-of-the-art methods.

The earliest deadline first (EDF) scheduling algorithm is a typical representative of the dynamic priority scheduling algorithm. However, once the system is overloaded, the deadline miss rate increases and the scheduling performance deteriorates sharply, which causes a reduction in system resource utilization. To overcome this problem, we proposed an improved dynamic priority scheduling algorithm based on heap sorting. The task deadline, task value, energy consumption, and other parameters were introduced. The fuzzy analytic hierarchy process (FAHP) and the value density method were then used to determine the comprehensive priority of tasks. A heapsort algorithm with a lower time complexity was used to sort the comprehensive priority index so as to reduce the sorting overhead of the system. The system sorting overhead was then introduced to improve the decision condition of the priority scheduling subset and expand the schedulable range of the algorithm. The experimental results showed that the improved method reduced the deadline miss rate by an average of 0.1789, which improved the scheduling performance of the algorithm. The optimized scheduling algorithm can be applied to industrial control to improve system efficiency, and reasonable resource scheduling can reduce data center costs.

The randomness, volatility, and intermittence of solar power generation make it difficult to achieve the desired accuracy of PV output-power prediction. Therefore, the time learning weight (TLW) proposed in this paper is used to improve the time correlation of the LSTM network. The Fusion Activation Function (FAF) is used to resolve gradient disappearance. Learning Factor Adaptation (LFA) and Momentum Resistance Weight Estimation (MRWE) are used to accelerate weight convergence and improve global search capabilities. Finally, this paper synthesizes the improvement and proposes the AHPA-LSTM model to stabilize the convergence domain. Using actual data verification, the δMAPE indicator of the improved model is only 2.85% on a sunny day, 5.92% on a cloudy day, 7.71% on a rainy day, and only 5.8% on average. Therefore, the AHPA-LSTM model under full climate and climatic conditions has a good predictive effect which is generally applicable to the prediction of ultra-short-term PV power generation.

Nowadays, various new communication technologies operating in different frequency bands emerge constantly. There is a trend to integrate more than one of them into single system, for optimizing whole system performance and volume. As an important circuit component for frequency selection, the research on multiband bandpass filter (MB-BPF) receives much attention. This paper proposes a novel synthesis method for MB-BPF. It applies two frequency transformations on the specifications of the MB-BPF to be synthesized, so that a rough prototype containing the information of transmission and reflection zeros is constructed first. Then, optimization is applied for fine tuning until equal ripples are achieved in each passband. For simplicity, it is called the Rough Prototype and Fine Tuning (RP-FT) method, which demonstrate great advantage in flexibly controlling the performance of a MB-BPF such as passband number, performance of each passband, transmission zeros, etc. Furthermore, the filtering polynomials derived by the RP-FT method could be transformed into specific coupling matrixes for physical realization. For illustration, this paper derives a specific coupling matrix form, and applies it in the design of a microstrip quad-band bandpass filter. The measurement well verifies the validity of design process.

In order to equip more high-energy pulse loads and improve power supply reliability, the vessel integrated power system (IPS) shows an increasing demand for high-voltage and large-capacity energy storage systems. Based on this background, this paper focuses on a super capacitor energy storage system based on a cascaded DC-DC converter composed of modular multilevel converter (MMC) and dual active bridges (DAB). The cascaded converter is called MMC-DAB for short. This paper analyzes the topology and modulation strategy of the system in detail. Taking into account the shortcomings of the traditional bidirectional power control strategy, this paper proposes a control strategy where the DAB module of each branch independently controls the voltage of the sub-module capacitor. The mathematical models of the traditional and the proposed control strategy are established. The stability of the two control strategies is analyzed and compared. Finally, a MW-level engineering prototype of the MMC-DAB energy storage system is designed and manufactured, and the effectiveness of the proposed control strategy was verified through experiments.

Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one dimension or separate the dimensions in analyses and applications, which could lead to discoveries that are not representative of the overall data or are dificult to interpret. To simultaneously reveal the spatial and temporal patterns of urban mobility datasets, we propose an analytical framework that is based on co-clustering and enables mobility behaviors to be distinguished in spatial and temporal dimensions. We use one month of taxi GPS data from the Manhattan area to explore spatio-temporal co-occurrence patterns. The spatial and temporal dimensions of taxi trip data were co-clustered by using the Bregman Block Average co-clustering algorithm with I-divergence (BBAC_I). We performed this process on weekdays and holidays and compared the mobility differences between these two periods. The experimental results demonstrated the effectiveness of this analytical framework, with which we can reveal the spatial patterns and their temporal dynamics as well as temporal patterns and their spatial dynamics in mobility data.

The brake-by-wire actuator was considered to be the next generation of vehicle brake, which uses cables to transmit signals and energy, and uses electric motor instead of hydraulic element or pneumatic element. This paper designs a new type of brake-by-wire actuator based on the special linear motor. First, the structure, composition and working principle of the linear motor are explained. Secondly, the caliper structure and amplification mechanism of the actuator are designed. Thirdly, the controller and control strategy based on dSPACE platform are introduced in detail. Finally, the electromechanical parameters of actuator were identified, and the experiment of the linear motor and brake-by-wire actuator were completed. The experiment results show that the new actuator has the advantages of fast braking response, low control difficulty and high control accuracy, etc. In conclusion, this paper provides a new design scheme for the brake-by-wire actuator, and has the potential to further research and replace the traditional hydraulic braking system.

With the development of society and the improvement of living standards, people pay more and more attention to their own living environment and life safety, and more people are gradually paying attention to the impact of the quality of the living environment on their health and work efficiency, and to meet this demand of people, it is necessary to effectively monitor and control the living environment. Based on this, this article applies cloud computing and Internet of things technologies that have developed rapidly in recent years, and proposes to design a residential environment intelligent monitoring system based on cloud computing and Internet of things, and use Internet of things and sensor technologies to achieve target connection and communication. It then uses distributed computing, a cloud computing technology, to implement integrated, standardized management of this system for truly intelligent monitoring. In this article, we first gathered a large amount of information through literature search methods, systematically introduced cloud computing and Internet of Things technologies, and introduced two applications in environmental intelligent monitoring. Next, we propose a design experiment of a residential environment intelligent monitoring system based on cloud computing and the Internet of Things, and propose an overall concept of system design, system hardware and software design requirements, and device selection and comfort evaluation. And living environment data integration is introduced in detail. We then tested the performance of the system in a specific application of the system in a real residential environment, using data parameters collected at different points in time as experimental data. Finally, it is concluded that the fuzzy close fusion algorithm used in the experiment can obtain data parameter values that are in good agreement with the real values, and the error range is controlled within 0–0.01; the indoor environment comfort is judged by 1 as the standard. $PMV>1$ indicates that the comfort level is excellent, and in the test experiment, each parameter value at 5 time points is all > 1, indicating that the indoor environment comfort level detected by the system is excellent.

High-Resolution Range Profile (HRRP) sequence has attracted academic attentions in the field of radar automatic target recognition (RATR) owing to its abundant spatial-temporal correlation between adjacent samples. However, it is difficult in the working state of radar to obtain complete HRRP sequence samples due to various internal and external factors such as ground clutter and systematic error, which poses an enormous challenge to radar target recognition. Therefore, it is crucial to repair the missing HRRPs based on the adjacent samples in the previous frames. In this paper, we discuss the extrapolate method of incomplete samples and propose an improved neural network algorithm named as Vanishing Gradient Mitigation Recurrent Neural Network (VGM-RNN). The lost samples in the sequence can be extrapolated by VGM-RNN, and the problem of vanishing gradient which is possessed in classical RNN can be effectively mitigated. The proposed method in this paper can be divided into two parts, as sample extrapolation and sequence recognition, in which sample extrapolation is the core method. Experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model exhibits higher accuracy and efficiency, as well as excellent anti-noise performance, compared with traditional methods. It is suggested that our proposed model can be effectively applied to radar system.

Unlike conventional conception that power amplifier and filter are regarded as two separate components and designed separately, this paper presents a high efficiency power amplifier with filtering capability, which is called filtering power amplifier. The proposed power amplifier integrates a bandpass filter as output matching network so that entire size is reduced to some extent. The filter will play the role of filtering and impedance matching simultaneously, and thus high-order harmonics are suppressed for output of the filtering power amplifier. For demonstration, an example of filtering power amplifier was designed, fabricated and measured. The measurement shows that it is featured by highly-selective bandpass response and good out-of-band performance. As high as 46.2 dB suppression can be achieved and extended over the forth-order harmonic. The example also retains the advantages of high efficiency and output power. The maximum output power can reach 40.5 dBm, and the maximum power-added efficiency is as high as 62%.

For the terahertz harmonic mixer, a design model based on Half-Subdivision and Half-Global Design Method (HS-HGDM) with a single functional unit is proposed. The single functional unit circuit of the mixer, such as LO or IF Low Pass Filter (LPF), is designed by Subdivision Design Method (SDM), and the rest circuits of the mixer are designed by Global Design Method (GDM). The harmonic mixer based on this model is characterized by simple circuit structure, low conversion loss, small circuit size and high stability. In this paper, an improved Compact Suspended Micro-strip Resonators (CSMRs) filter is developed for the IF part of sub-harmonic mixer. The remaining circuits consist of some basic transmission units, such as the High-Z Low-Z impedance Transmission Lines (H-Z L-Z TLs) applied for main circuit, and full/reduced height WR4.3/WR8 applied for providing RF/LO signal. In RF frequency range 200-240 GHz, a Single Sideband (SSB) conversion loss of 7.36±0.76 dB operating at IF frequency 1.8 GHz, and a Double Sideband (DSB) noise temperature below 1034 K with the LO power of 2-5 mW and the LO frequency of 107 GHz were achieved. This broadband sub-harmonic mixer operates at room temperature, which makes it applicable to sub-millimeter wave or terahertz wireless communication systems and imaging inspection systems.

A terahertz mixer is one of terahertz key components and widely used on terahertz wireless communication, remote sensing, aerospace exploration, and biomedical system. The performance of the terahertz mixer has a great effect on the entire terahertz transceiver system performance. This paper proposes a 220 GHz broadband sub-harmonic mixer based on global design method (GDM), which is different from the traditional subdivision design method of which the mixer circuit is composed of functional unit circuits. Instead, the GDM adopts the overall performance of the mixer (conversion loss) as an optimization goal. The continuous structures of the circuit are described by ideal transmission line models and waveguide models, meanwhile, the S-parameters of the de-embedded model are used to describe the discontinuous structures. The measured single-sideband (SSB) conversion loss is basically consistent with the simulation results, which verifies the accuracy and reliability of the GDM. When local oscillator (LO) frequency is fixed at 110 GHz, the measured SSB conversion loss is less than 10 dB with the average value of 8.1 dB from 198 to 238 GHz. The minimum conversion loss of the mixer is 6.35 dB at 207 GHz. The measured double-sideband noise temperature of the mixer is less than 924 K at an intermediate frequency of 1.6 GHz. The GDM has the characteristics with a wide frequency band, small circuit size, simple structure, low LO power, and stable performance, and can also be applied to other terahertz components.

In this paper, a novel wideband filtering power divider with third-order quasi-Elliptic bandpass response is proposed. Its physical mechanism is revealed by the lumped-element equivalent circuit, such as how its transmission poles and zeros are formed. The design of the proposed filtering power divider can be simplified as its even-mode model can be synthesized by conventional filter synthesis technique, and then the initial structure parameters can be analytically determined. Owing to the fact that as many as three resistors are joined, high isolation can be achieved between two output ports. To demonstrate its good performance, an example was designed, fabricated, and measured, whose center frequency is 1.99 GHz and 3-dB fractional bandwidth is 43.7%. Moreover, as great as 34 and 28 dB out-of-band suppression can be achieved in the lower and upper stopbands, respectively. The isolation between two output ports is better than 20 dB for a wide frequency range.

An application programming interface (API) is an excellent feature since it is a procedure call interface to an operating system resource. Behavior features based on API play an important role in analyzing malware variants. However, the existing malware detection approaches have a lot of complex operations on construction and matching. Graph matching is an NP-complete problem and is time-consuming because of computational complexity. To address these issues, a promising approach is proposed to construct the classified behavior features from different malware families. In the proposed approach, a classified behavior feature consists of a kernel object (an API call parameter) and a series of operations (an API trace). Besides, a classified behavior graph (CBG) is represented as a number by hash to reduce workload and matching time. Subsequently, multiple machine learning classifiers are used for system classification. In particular, to verify the efficiency of our approach, we perform a series of experiments with different families. The experiments on 1220 malware samples show that the true positive rate is up to 88.3% and the false positive rate keeps within 3.9% by the support vector machine (SVM).

In this paper, we present a novel gain-enhanced sub-harmonic mixer based on 0.5-μm emitter width InGaAs/InP double heterojunction bipolar transistors (InP DHBTs). The proposed mixer consists of a transconductance stage and a gain-enhanced stage. A common-emitter transistor is used in the first stage to realize the sub-harmonic mixing while another common-emitter transistor is used in the second stage to remix the fLO+IF and fIF and also amplify the f2LO+IF. For further verification, a transconductance mixer and a gain-enhanced mixer were designed and fabricated. Compared with the transconductance mixer, the gain-enhanced mixer exhibits a 6.8-dB higher conversion gain with 2-dB lower LO input power and a peak up-conversion gain of 9 dB at 213 GHz with fIF = 1 GHz, fLO = 106 GHz, and PIF = -26 dBm PLO = 3 dBm. To our best knowledge, the gain-enhanced mixing structure is proposed for the first time.

Existing algorithms experience difficulty resisting additive and subtractive attacks because the embedded watermarks are independent of the carrier programs. A dynamic software watermarking algorithm based on exception handling is proposed in this paper. The algorithm considers the fact that exception handling, which is difficult to remove, commonly exists in the programs to ensure the normal program operation. First, an exception type table is constructed, and the binary watermark to be embedded is mapped to a sequence of exception types by scrambling encoding. Second, corresponding trigger conditions and exception handlers are constructed, and watermark embeddable points are acquired in execution paths under secret inputs. Finally, the code segment of the constructed trigger conditions and exception handlers are inserted into the program with several meticulously designed identifiers. For watermark extraction, the triggered exception types can be obtained with the identifiers by executing the watermarked executable program under secret inputs. The mapping relationship between the exception type and watermark is utilized to decode the binary watermark. The algorithm analysis and experimental results show that the proposed algorithm can embed a watermark in the source code and extract it in an executable program. The algorithm demonstrates good performance against additive and subtractive attacks.

In this paper, a novel microstrip filter is proposed, which can realize a third-order quasi-elliptic bandpass response. In order to reveal the physical mechanism of the microstrip filter, a lumped-element equivalent circuit is set up and the equivalence relations between its circuit elements and the electrical parameters of the microstrip filter are presented. According to filter specifications, the initial structural parameters of the microstrip filter can be easily calculated through these equivalence relations. As demonstration, an actual microstrip filter was designed, fabricated and measured, whose center frequency is at 2.50 GHz and fractional bandwidth is 36%. There are two transmission zeros near to the passband to improve the frequency selectivity, which makes the roll-off rate at each side of the passband as high as 125 and 50 dB/GHz, respectively. The out-of-band suppression from 3.6 to 6.1 GHz is greater than 40 dB. The size of the fabricated filter is 0.21λg × 0.18λg, which is very compact.

In this paper, a new 3-D electromagnetic (EM) simulation approach for heterojunction bipolar transistor (HBT) parameter extraction in terahertz band is presented. We introduce an inter-electrode impedance equivalent-circuit model with the concept of dispersive parameters and a more rigorous 3-D EM simulation parameter extraction method to avoid serious discrepancies between the simulated EM field distribution and the real EM field distribution of the device. Terahertz parasitic effects, including the EM field discretization and multi-finger device field sharing, are observed. Compared with the previous 3-D EM simulation-assisted model, the new model in this paper demonstrates better results for multi-finger indium phosphide HBTs.