Differential code biases (DCBs) are important parameters that must be estimated accurately for precise positioning and Satellite Based Augmentation Systems (SBAS) ionospheric related parameter generation. In this paper, in order to solve the performance degradation problem of the traditional minimum STD searching algorithm in disturbed ionosphere status and in geomagnetic low latitudes, we propose a linear planar based minimum STD searching algorithm. Firstly, we demonstrate the linear planar trend of the local vertical TEC and introduce the linear planar model based minimum standard variance searching method. Secondly, we validate the correctness of our proposed method through theoretical analysis and propose bias detection to avoid large estimation bias. At last, we show the performance of our proposed method under different geomagnetic latitudes, different seasons and different ionosphere status. The experimental results show that for the traditional minimum STD searching algorithm based on constant model, latitude difference is the key factor affecting the performance of DCB estimation. The DCB estimation performance in geomagnetic mid latitudes is the best, followed by the high latitudes and the worst is for the low latitudes. While the algorithm proposed in this paper can effectively solve the performance degradation problem of DCB estimation in geomagnetic low latitudes by using the linear planar model which is with a higher degree of freedom to model the local ionosphere characteristics and design dJ to screen the epochs. Through the analysis of the DCB estimation results of a large number of stations, it can be found that the probability of large estimation deviation of the traditional method will increase obviously under the disturb ionosphere conditions, but the algorithm we proposed can effectively control the amplitude of the maximum deviation and alleviate the probability of large estimation deviation in disturb ionosphere status.

Nearest-neighbor-search classifiers are attractive but they have high intrinsic computational demands which limit their practical application. In this paper, we propose a coprocessor for k (k with k≥1) nearest neighbor (kNN) classification in which squared Euclidean distances (SEDs) are mapped into the clock domain for realizing high search speed and energy efficiency. The minimal SED searching is carried out by weighted frequency dividers that drastically reduce the normally exponential increase of the worst-case search-clock number with the bit width of vector components to only a linear increase. This also results in low power dissipation and high area-efficiency in comparison to the traditional method using large numbers of adders and comparators. The kNN classifier determines the class of an unknown input sample with a majority decision among the k nearest reference samples. The required majority-decision circuit is integrated with the clock-mapping-based minimal-SED searching architecture and proceeds with the classification immediately after identification of each of the k nearest references. A test chip in 180 nm CMOS technology, which can process 8 dimensions of 32 reference vectors in parallel, achieves low power dissipation of 40.32 mW (at 51.21 MHz clock frequency and 1.8 V supply voltage). Significantly, the distance search circuit consumes only 5.99 mW. Feature vectors with different dimensionality up to 2048 dimensions can be handled by the designed coprocessor due to a dimension extension circuit, enabling large flexibility for usage in different application.

This study investigates the effect of frame intervals on the accuracy of the Lucas–Kanade optical flow estimates for high-frame-rate (HFR) videos, with a view to realizing accurate HFR-video-based optical flow estimation. For 512 512 pixels videos of patterned objects moving at different speeds and captured at 1000 frames per second, the averages and standard deviations of the estimated optical flows were determined as accuracy measures for frame intervals of 1–40 ms. The results showed that the accuracy was highest when the displacement between frames was around 0.6 pixel/frame. This common property indicates that accurate optical flow estimation for HFR videos can be realized by varying frame intervals according to the motion field: a small frame interval for high-speed objects and a large frame interval for low-speed objects.

In this paper, we report the design of an organic thin-film transistor (OTFT) driver circuit for the actuator of an organic fluid pump, which can be integrated in a portable-size fully-organic artificial lung. Compared to traditional pump designs, lightness, compactness and scalability are achieved by adopting a creative pumping mechanism with a completely organic-material-based system concept. The transportable fluid volume is verified to be flexibly adjustable, enabling on-demand controllability and scalability of the pump's fluid-flow rate. The simulations, based on an accurate surface-potential OTFT compact model, demonstrate that the necessary driving waveforms can be efficiently generated and adjusted to the actuator requirements. At the actuator-driving-circuit frequency of 0.98Hz, an all-organic fluid pump with 40cm length and 0.2cm height is able to achieve a flow rate of 0.847L/min, which satisfies the requirements for artificial-lung assist systems to a weakened normal lung.

Nowadays, with the extensive use of smart devices, the amount of mobile data is experiencing an exponential growth. As a result, accommodating the large amount of traffic is important for the future 5G mobile communication. Millimeter-wave band, which has a lot of spectrum resources to meet the demand brought by the growth of mobile data, is becoming an important part of 5G technology. In order to mitigate the high path loss brought by the high frequency band, beamforming is often used to enhance the gain of a link. In this paper, we propose an iteration-based beamforming method for planar phased array. When compared to a linear array, a planar phased array points a smaller area which ensures a better link performance. We deduce that different paths of millimeter-wave channel are approximately orthogonal when the antenna array is large, which forms the basis of our iterative approach. We also discuss the development of the important millimeter-wave device-phase shifter, and its effect on the performance of the proposed beamforming method. From the simulation, we prove that our method has a performance close to the singular vector decomposition (SVD) method and is superior to the method in IEEE802.15.3c. Moreover, the channel capacity of the proposed method is at most 0.41bps/Hz less than the SVD method. We also show that the convergence of the proposed method could be achieved within 4 iterations and a 3-bit phase shifter is enough for practical implementation.

Human action recognition in videos draws huge research interests in computer vision. The Bag-of-Word model is quite commonly used to obtain the video level representations, however, BoW model roughly assigns each feature vector to its nearest visual word and the collection of unordered words ignores the interest points' spatial information, inevitably causing nontrivial quantization errors and impairing improvements on classification rates. To address these drawbacks, we propose an approach for action recognition by encoding spatio-temporal log Euclidean covariance matrix (ST-LECM) features within the low-rank and sparse representation framework. Motivated by low rank matrix recovery, local descriptors in a spatial temporal neighborhood have similar representation and should be approximately low rank. The learned coefficients can not only capture the global data structures, but also preserve consistent. Experimental results showed that the proposed approach yields excellent recognition performance on synthetic video datasets and are robust to action variability, view variations and partial occlusion.

The rapid development of a global navigation satellite system (GNSS) has raised the demand for spacecraft navigation, particularly for lunar spacecraft (LS). First, instead of the traditional approach of combining the united X-band system (UXB) with very-long-baseline interferometry (VLBI) by a terrestrial-based observing station in Chinese deep-space exploration, the spacecraft navigation based on inter-satellite link (ISL) is proposed because the spatial coverage of the GNSS downstream signals is too narrow to be used for LS navigation. Second, the feasibility of LS navigation by using ISL wide beam signals has been analyzed with the following receiving parameters: the geometrical dilution of precision (GDOP) and the carrier-to-noise ratio (C/N0) for satellites autonomously navigation of ISL and LS respectively; the weighting distance root-mean-square (wdrms) for the combination of both navigation modes. Third, to be different from all existing spacecraft ISL and GNSS navigation methods, an ISL annular beam transmitting antenna has been simulated to minimize the wdrms (1.138m) to obtain the optimal beam coverage: 16°-47° on elevation angle. Theoretical calculations and simulations have demonstrated that both ISL autonomous navigation and LS navigation can be satisfied at the same time. Furthermore, an onboard annular wide beam ISL antenna with optimized parameters has been designed to provide a larger channel capacity with a simpler structure than that of the existing GPS ISL spot beam antenna, a better anti-jamming performance than that of the former GPS ISL UHF-band wide band antenna, and a wider effectively operating area than the traditional terrestrial-based measurement. Lastly, the possibility and availability of applying an ISL receiver with an annular wide beam antenna on the Chinese Chang'E-5T (CE-5T) reentry experiment for autonomous navigation are analyzed and verified by simulating and comparing the ISL receiver with the practiced GNSS receiver.

Leakage power consumption of logic elements has become a serious problem, especially in the sub-100-nanometer process. In this paper, a novel power gating approach by using the controlling value of logic elements is proposed. In the proposed method, sleep signals of the power-gated blocks are extracted completely from the original circuits without any extra logic element. A basic algorithm and a probability-based heuristic algorithm have been developed to implement the basic idea. The steady maximum delay constraint has also been introduced to handle the delay issues. Experiments on the ISCAS'85 benchmarks show that averagely 15-36% of logic elements could be power gated at a time for random input patterns, and 3-31% of elements could be stopped under the steady maximum delay constraints. We also show a power optimization method for AND/OR tree circuits, in which more than 80% of gates can be power-gated.

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.

In this paper, a morpheme-based weighting and its integration method are proposed as a smoothing method to alleviate the data sparseness in Chinese-Mongolian statistical machine translation (SMT). Besides, we present source-side reordering as the pre-processing model to verify the extensibility of our method. Experi-mental results show that the morpheme-based weighting can substantially improve the translation quality.

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data locality and data skew on the reduce side are two essential issues in MapReduce. Improving data locality can decrease network traffic by moving reduce tasks to the nodes where the reducer input data is located. Data skew will lead to load imbalance among reducer nodes. Partitioning is an important feature of MapReduce because it determines the reducer nodes to which map output results will be sent. Therefore, an effective partitioner can improve MapReduce performance by increasing data locality and decreasing data skew on the reduce side. Previous studies considering both essential issues can be divided into two categories: those that preferentially improve data locality, such as LEEN, and those that preferentially improve load balance, such as CLP. However, all these studies ignore the fact that for different types of jobs, the priority of data locality and data skew on the reduce side may produce different effects on the execution time. In this paper, we propose a naive Bayes classifier based partitioner, namely, BAPM, which achieves better performance because it can automatically choose the proper algorithm (LEEN or CLP) by leveraging the naive Bayes classifier, i.e., considering job type and bandwidth as classification attributes. Our experiments are performed in a Hadoop cluster, and the results show that BAPM boosts the computing performance of MapReduce. The selection accuracy reaches 95.15%. Further, compared with other popular algorithms, under specific bandwidths, the improvement BAPM achieved is up to 31.31%.

As characterizing videos simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation. With the development of attention mechanisms in natural language processing, in this work, we present a novel model with VLAD following spatio-temporal self-attention operations, named spatio-temporal self-attention weighted VLAD (ST-SAWVLAD). In particular, sequential convolutional feature maps extracted from two modalities i.e., RGB and Flow are receptively fed into the self-attention module to learn soft spatio-temporal assignments parameters, which enabling aggregate not only detailed spatial information but also fine motion information from successive video frames. In experiments, we evaluate ST-SAWVLAD by using competitive action recognition datasets, UCF101 and HMDB51, the results shcoutstanding performance. The source code is available at:https://github.com/badstones/st-sawvlad.