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Despite the advantages of scalability and flexibility, Security Function Virtualization (SFV) raises concerns about its own security. To enhance the security of SFV, a promising approach is to run critical components of off-the-shelf security software inside SGX enclaves. This idea, however, is hardly practical due to the difficulty of detaching components from the monolithic security function and the unacceptable cost of running them in enclaves. In this work, we propose S-Blocks, an architecture to modularize a virtual security function (VSF) and protect its key modules with SGX in an efficient manner. Through systematically decomposing modules of a VSF into related elements, it is easy to put the key modules and elements of the VSF into an enclave. Furthermore, aiming at addressing state consistency and secure migration issues of security function scaling, we design a fine-grained state synchronization and migration mechanism to ensure lose-free, order-preserving, and state security for VSFs. To demonstrate the effectiveness of our approach, we prototype S-Blocks using Fast-Click on a real Skylake platform and implement three main types of virtual security functions based on the S-Blocks architecture. Our evaluation results show that S-Blocks only imposes a manageable performance overhead, and low latency and resource consumption when protecting VSFs.

Recently, hypergraph learning has shown great potential in a variety of classification tasks. However, existing hypergraph neural networks lack flexibility in modeling and extracting high-order relationships among data. To solve this problem, we propose a novel framework called hypergraph wavelet neural networks (HGWNN) to explore the high-order correlation in 3D data. Firstly, considering the non-uniformity of most data sets in the real world, we propose a “data-driven” hypergraph construction scheme, which is more efficient than some commonly used hypergraph construction methods. Secondly, in order to efficiently learn deep embeddings from the constructed hypergraph, we propose a hypergraph wavelet convolution operator. It enables efficient information aggregation by fully exploiting the localization property of wavelets. This convolution operator is suitable for both non-uniform and uniform hypergraphs. Finally, we design a new hypergraph regularizer based on the sparse prior of wavelet coefficients to promote local smoothness and avoid network overfitting. We have conducted experiments on object classification tasks on two 3D benchmark datasets: the National Taiwan University (NTU) 3D model dataset and the ModelNet40 dataset. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.

Real-time three-dimensional reconstruction is increasingly important in many fields. There are mainly two ways to improve real-time performance: increasing hardware rate and reducing captured frames count. For increasing hardware rate, the binary defocusing technique can effectively break the limitation of speed. For reducing captured frames count, the fusion technique is typically introduced. However, the simultaneous work of binary defocusing technique and fusion technique is hard to achieve. To this end, we establish a novel system framework with a camera and dual projectors. The dual projectors can control defocusing level independently, i.e. enhance the flexibility of projector. Based on this, our paper exhibits two decoding phase methods: the strict-alignment method and the loose-alignment method. The strict-alignment method requires the projection chips to be strictly aligned, and a virtual example demonstrated effectiveness with three captured frames. In order to further improve practicality, the loose-alignment method, which relaxes the restriction, is proposed. Finally, experiments verify the performance of our proposed method.

A leading vehicle’s sudden deceleration can lead to a rear-end collision. Due to a lack of driving experience, novice drivers have a greater tendency to be involved in these accidents. Most previous studies have examined driver response time and braking behaviors, but few researchers have focused on what experienced and novice drivers did after their feet touched the braking pedal and their hands turned the steering wheel. These braking and steering parameters are essential in understanding driver avoidance behavior during emergencies. We programmed rear-end crash risk scenarios to examine experienced and novice drivers’ behaviors thoroughly using a driving simulator. Twenty experienced and twenty five novice subjects participated in our experiments, and their braking and steering maneuvers were recorded when leading vehicles ran at 60 km/h, 80 km/h and 100 km/h. The results showed that the two groups of subjects tended to execute two kinds of maneuvers to avoid crashes: braking only (novice 33%, experienced 19%) and the combination of braking with steering (novice 22%, experienced 26%). When the novice drivers executed braking with steering, their response time and steering duration were significantly longer than those of the experienced drivers who executed braking with steering. As the speed increased, the novice drivers’ response time, maximum braking force and maximum steering angle were significantly affected. These results showed that novice drivers should brake only when the leading vehicle suddenly decelerates. The experienced drivers executed steadier maneuvers. Their risk perception time was shorter, and their maximum braking force and the maximum steering angles were smaller. The response time, braking intensity and steering wheel angle should be considered when developing rear-end collision warning systems.

Surveillance cameras are ubiquitous around us. Emerging full-feature object-detection models can analyze surveillance videos with high accuracy but consume much computation. Directly applying these models for practical scenarios with large-scale cameras is prohibitively expensive. This, however, is wasteful and unnecessary considering that user-defined anomalies occur rarely among these videos. Therefore, we propose FFS-VA, a multi-stage Fast Filtering Mechanism for Video Analytics, to make video analytics much cost-effective. FFS-VA filters out the frames without the user-defined events by two stream-specialized filters and a cheap full-function model, to reduce the number of frames reaching the full-feature model. FFS-VA presents a global feedback-queue approach to balance the processing speeds of different filters in intra-stream and inter-stream processes. FFS-VA designs a dynamic batch technique to achieve a trade-off between throughput and latency. FFS-VA can also efficiently scale to multiple GPUs. We evaluate FFS-VA against the state-of-the-art YOLOv3 under the same hardware and video workloads. The experimental results show that under a 12.88 percent target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (15× more than YOLOv3) in the online case, and obtain 10× speedup when offline analyzing a stream, with an accuracy loss of less than 2 percent.

Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact, lightweight imaging systems. However, a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio. Current implementations require multiple masks or multiple shots to denoise the reconstruction. We propose single-shot lensless imaging with a Fresnel zone aperture and incoherent illumination. By using the Fresnel zone aperture to encode the incoherent rays in wavefront-like form, the captured pattern has the same form as the inline hologram. Since conventional backpropagation reconstruction is troubled by the twin-image problem, we show that the compressive sensing algorithm is effective in removing this twin-image artifact due to the sparsity in natural scenes. The reconstruction with a significantly improved signal-to-noise ratio from a single-shot image promotes a camera architecture that is flat and reliable in its structure and free of the need for strict calibration., Lensless imaging: reduced noise Two simple innovations have been found to significantly improve the quality of lensless imaging. Jiachen Wu and coworkers from Tsinghua University in China and MIT in the US report that introducing a Fresnel optical element and applying a compressive sensing algorithm can significantly reduce the level of noise in reconstructed images. Their simple lensless system consists of a Fresnel Zone Aperture (FZA) placed 3 mm in front of a CMOS image sensor. The FZA is a mask of patterned chrome that consists of a series of concentric rings that alternatingly transmit and block light. Images on an LCD screen placed ~30 cm from the mask are used as a test object. The signal recorded by the CMOS sensor is then reconstructed by a compressive sensing algorithm with total variation denoising to generate an improved image of the object.

TLC flash has three types of pages to accommodate the three bits in each TLC physical cell exhibiting very different program latencies. This paper proposes PA-SSD to effectively improve the overall performance by exploiting the dissimilarity of TLC pages on program latency throughout the write request handling workflow. The main idea behind PA-SSD is to coordinately allocate the same type of pages for sub-requests of any given user write request, to mitigate the potential program latency imbalance among the sub-requests, and to schedule sub-requests according to their page-types. We achieve the PA-SSD design goal by answering three key research questions: (1) how to properly determine page-type for each user write request? (2) how to actually allocate a physical page for each sub-request with an assigned page-type from (1)? (3) how to effectively schedule the sub-requests in the chips queues when their page-types are judiciously allocated from (2)? To answer the first question, we propose seven page-type specifying schemes to investigate their effects under different workloads. We answer the second question by redesigning the page allocation strategy in TLC SSD to uniformly and sequentially determine physical pages for allocation following the internal programming process of TLC flash. Lastly, a page-type aware scheduling policy is presented to reorder the sub-requests within chips’ queues. Our experiments show that PA-SSD can accelerate both the write and read performance. Particularly, our proposed queue-depth based page-type specifying scheme improves write performance by 2.6 times and read performance by 1.5 times over the conventional TLC SSD.

With the popularity of Android, malware detection and family classification have also become a research focus. Many excellent methods have been proposed by previous authors, but static and dynamic analyses inevitably require complex processes. A hybrid analysis method for detecting Android malware and classifying malware families is presented in this paper, and is partially optimized for multiple-feature data. For static analysis, we use permissions and intent as static features and use three feature selection methods to form a subset of three candidate features. Compared with various models, including k-nearest neighbors and random forest, random forest is the best, with a detection rate of 95.04%, while the chi-square test is the best feature selection method. After using feature selection to explore the critical static features contained in this dataset, we analyzed a subset of important features to gain more insight into the malware. In a dynamic analysis based on network traffic, unlike those that focus on a one-way flow of traffic and work on HTTP protocols and transport layer protocols, we focused on sessions and retained protocol layers. The Res7LSTM model is then used to further classify the malicious and partially benign samples detected in the static detection. The experimental results show that our approach can not only work with fewer static features and guarantee sufficient accuracy, but also improve the detection rate of Android malware family classification from 71.48% in previous work to 99% when cutting the traffic in terms of the sessions and protocols of all layers.

Objectives: Version of the acetabular component on standing position is an important factor influencing the risk of adverse outcomes during functional activities after total hip arthroplasty (THA). The main objective of this study was to evaluate the reliability and validity of a method for measuring cup version on standing lateral (SL) radiograph.Methods: 87 consecutive patients who underwent primary THA were included. SL radiograph and computed tomography (CT) scan were taken 7 days after THA to measure the cup version. The measurement method was based on the cross-table lateral (CL) radiograph. The tangential line of the cup opening face was determined through two intersecting points of the cup outer edge circle and the opening face elliptical arc. The version was measured between the horizonal line and the tangent line by two independent examiners. Radiographic measurements were compared with CT measurements after the uniformization of pelvic tilt (PT).Results: All measurements had excellent intra- and inter-observer reliabilities with an intraclass correlation coefficient (ICC) >0.90. The mean version was 15.56° (SD 11.22°, -19.82° to 38.74°) for SL radiographs and 16.19° (SD 12.09°, -23.00° to 47.20°) for PT-matched CT. There was a positive correlation (r = 0.901, p < 0.001) between radiographic versions and CT versions. The mean difference was -0.63° (SD 5.25°, -14.92° to 12.72°) and the differences were almost within the 95% limits of agreement. Conclusion: SL radiograph allows accurate and practicable evaluation of post-operative functional acetabular cup version, with an advantage to identify the pelvic tilt simultaneously.

Model checking is considered one of the most practical applications of theoretical computer science in the verification of concurrent systems, and model checking tools are very important for such applications. This paper presents the model checking tool VERDS with the theoretical background, basic functionalities and modeling techniques with various examples. In particular, the tool includes an implementation of a bounded correctness checking approach which can be seen as an extension of bounded model checking and complementary to the traditional symbolic model checking. VERDS is also flexible for extension. We show how it is extended to handle discrete time models. We carry out case-studies of a kind of task scheduling problems, in which time specification is essential. The experimental results show that VERDS is not only feasible for solving practical problems but also with good performance in solving such problems.

Large deployable membrane diffraction antenna plays a significant role in achieving high-precision remote sensing detection in the future owing to its super large aperture. In order to accurately adjust and maintain the primary mirror surface of the deployable diffraction antenna, a novel scheme of surface shape control has been proposed in this paper. In the scheme, the large deployable antenna is divided into several mirror units and each mirror unit is spread by braced frames. Besides, the membrane mounting units are controlled by six actuators with 6 degrees of freedom. The actuators which are low cost and applicable in complex environment are developed according to relevant control requirements. The designed actuators have the advantages of wide adjustable range (up to 21mm) and high precision (better than $0.16\mu m$ ), which is much better than the traditional piezoelectric actuator scheme, and the cost performance is higher than the adjustment scheme of optical telescope. Finally, the simulation results demonstrate the feasibility of the proposed control scheme. The performances of the actuators are displayed by experimental tests.

Binary defocusing technique can effectively break the limitation of hardware speed, which has been widely used in the real-time three-dimensional (3D) reconstruction. In addition, fusion technique can reduce captured images count for a 3D scene, which helps to improve real-time performance. Unfortunately, it is difficult for binary defocusing technique and fusion technique working simultaneously. To this end, our research established a novel system framework consisting of dual projectors and a camera, where the position and posture of the dual projectors are not strictly required. And, the dual projectors can adjust defocusing level independently. Based on this, this paper proposed a complementary decoding method with unconstrained dual projectors. The core idea is that low-resolution information is employed for high-resolution phase unwrapping. For this purpose, we developed the low-resolution depth extraction strategy based on periodic space-time coding patterns and the method from the low-resolution order to high-resolution order of fringe. Finally, experimental results demonstrated the performance of our proposed method, and the proposed method only requires three images for a 3D scene, as well as has strong robustness, expansibility, and implementation.

This article considers global stabilization problem for a kind of uncertain high-order nonlinear systems (HONSs). Two distinct characteristics of this study are that the considered system possesses the input-quantized actuator, and the prescribed time convergence of the system states is wanted. To address these, a novel state-scaling transformation (SST) is firstly introduced to convert the aboriginal prescribed-time stabilization (PTS) to the asymptotic stabilization of the transformed one. Then, under the new framework of equivalent transformation, a quantized state feedback controller that achieves of the performance requirements is developed with the aid of the technique of adding a power integrator (API). Finally, simulation results of a liquid-level system are provided to confirm the efficacy of the proposed approach.

With the rapid increase in the number of Android malware, the image-based analysis method has become an effective way to defend against symmetric encryption and confusing malware. At present, the existing Android malware bytecode image detection method, based on a convolution neural network (CNN), relies on a single DEX file feature and requires a large amount of computation. To solve these problems, we combine the visual features of the XML file with the data section of the DEX file for the first time, and propose a new Android malware detection model, based on a temporal convolution network (TCN). First, four gray-scale image datasets with four different combinations of texture features are created by combining XML files and DEX files. Then the image size is unified and input to the designed neural network with three different convolution methods for experimental validation. The experimental results show that adding XML files is beneficial for Android malware detection. The detection accuracy of the TCN model is 95.44%, precision is 95.45%, recall rate is 95.45%, and F1-Score is 95.44%. Compared with other methods based on the traditional CNN model or lightweight MobileNetV2 model, the method proposed in this paper, based on the TCN model, can effectively utilize bytecode image sequence features, improve the accuracy of detecting Android malware and reduce its computation.

Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing (e.g. 10,684 LOC in Linux v2.6.0 vs. 64,018 LOC in v5.3), and the count of the authorization hooks is nearly doubled (e.g. 122 hooks in v2.6.0 vs. 224 hooks in v5.3). In addition, the computer industry has seen tremendous advancement in hardware (e.g., memory and processor frequency) in the past decade. These make the previous evaluation on LSM, which was done 18 years ago, less relevant nowadays. It is important to provide up-to-date measurement results of LSM for system practitioners so that they can make prudent trade-offs between security and performance. This work evaluates the overhead of LSM for file accesses on Linux v5.3.0. We build a performance evaluation framework for LSM. It has two parts, an extension of LMBench2.5 to evaluate the overhead of file operations for different security modules, and a security module with tunable latency for policy enforcement to study the impact of the latency of policy enforcement on the end-to-end latency of file operations. In our evaluation, we find opening a file would see about 87% (Linux v5.3) performance drop when the kernel is integrated with SELinux hooks (policy enforcement disabled) than without, while the figure was 27% (Linux v2.4.2). We found that the performance of the above downgrade is affected by two parts, policy enforcement, and hook placement. To further investigate the impact of policy enforcement and hook placement respectively, we build a Policy Testing Module, which reuses hook placements of LSM, while alternating latency of policy enforcement. With this module, we are able to quantitatively estimate the impact of the latency of policy enforcement on the end-to-end latency of file operations by using a multiple linear regression model and count policy authorization frequencies for each syscall. We then discuss and justify the evaluation results with static analysis on syscalls' call graphs.

The integration of unmanned aircraft system into nonsegregated airspace raises safety concerns of midair encounters in the hybrid airspace where manned and unmanned aircraft coexist. This paper aims at developing a new modeling framework with improved modeling capability and feasibility for analyzing the midair encounters in hybrid airspace. This modeling framework includes models of different components (airspace, human pilot, manned aircraft, unmanned aircraft, etc.) involved in midair encounters. As a key component, human pilot is modeled to contain different information processing stages which are perception, decision making and response. Meanwhile, the modeling framework is designed to provide quantitative measures of midair encounters in hybrid airspace, and further uses “Equal Level of Safety” criterion to evaluate the validity of the integration of the unmanned aircraft system. Finally, the feasibility and effectiveness of the proposed modeling framework are demonstrated through simulation studies.

w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array (SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. Data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: the hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) method based on multicore Central Processing Units (CPUs) and the hybrid MPI and Compute Unified Device Architecture (CUDA) method based on Graphics Processing Units (GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI + OpenMP and MPI + CUDA methods is significantly shorter than that of single-thread processing. MPI + CUDA generally shows faster performance when running on multiple nodes than MPI + OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing; while the single- and doubleprecision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2,048 pixels. The present research offers useful guidance for developing SKA imaging pipelines., 9 pages. Published in Science Bulletin, 64, 586-594 (2019)

Multilevel cell (MLC) states with lower threshold voltage endure less cell damage, lower retention error, and less current consumption. Based on these characteristics, it is opportunistic to strengthen MLC flash by introducing bit-flipping that reshapes state proportions on MLC pages. In this paper, we present a holistic study of bit-flipping schemes upon MLC flash in theory and practice. Specifically, we systematically investigate effective bit-flipping schemes and propose four new schemes on manipulating MLC states. We further design a generic implementation framework, named MLC bit-flipping framework, to implement bit-flipping schemes within solid state drives controllers, nicely integrating with existing system-level optimizations to further improve overall performance. The experimental results demonstrate that our proposed bit-flipping schemes standalone can reduce up to 28% cell damages and 53% retention errors. Our circuit-level simulation manifests that the bit-flipping latency on a page is less than 4 $\mu \text{s}$ when using 8K logic gates.

Depth difference, as a popularly used feature for characterizing pairwise pixels of range images, fails to precisely capture skeleton joints when human body possesses a wild and complicated articulation. As the geodesic distance of pairwise pixels is able to present a global connected property and adjacent pixels often belong to the same body component, we propose an effective and efficient framework for pose estimation from range images. Firstly, all the pixels of a range image are grouped into superpixels using an improved Simple Linear Iterative Clustering algorithm. Secondly, those superpixels are labelled as the components of a human body using the hybrid feature. Thirdly, componentwise cluster feature extraction is undertaken on skeleton joints of body components with K-means clustering algorithm. Finally, the feature points of each component are then stacked as a compact representation of human poses and mapped to the skeleton joints of a human body. Experimental results demonstrate that the proposed framework outperforms several state-of-the-art pose estimation methods.

Previous studies established traffic demand equilibrium with an assumption that all the traffic information on road is easily accessed by the in-need traffic participants, which is not true in the real applications (due to data collections difficulty). The newly emerging smart portable devices (e.g., smart phones) generate massive on-site traffic data (speed, density, etc.), which stimulates us re-consider designing the traffic demand equilibrium. For the purpose of analyzing mobile internet service influence on traffic demand, we build up a Probit-based model with consideration of multiple traffic constraints (i.e., traveler type, actual travelling time, perceiving travelling time). The Monte Carlo method is introduced to simulating initial route selection probability distributions, and the Method of Successive Averages (MSA) is developed to help the Monte Carlo algorithm converge at optimal solution. We have implemented our model on typical traffic travelling scenario with very complex traffic network demands. The experimental results suggested that larger mobility service coverage can significantly reduce the overall traffic time in the free flow state, and the mobility service coverage rate ranging from 0.6 to 0.8 is supposed to provide minimum travelling time for the overall traffic network, while larger coverage rate at congested state may reduce the traffic network efficiency.

Programmable Logic Controllers (PLCs) are essential parts in industrial manufacturing plants. With the emerging Industry 4.0 environment, legacy PLCs are now connected to the Internet to be better automated. However, these PLCs are especially vulnerable when connected to a network, since there is limited inherent security mechanisms built in. In this paper, we discuss various vulnerabilities in these PLCs. We describe threat models, detection and protection techniques. We consider vulnerabilities as compromised PLC logic, which is introduced by over-the-network malicious data injection. We leverage Host-Based Intrusion Detection System (HIDS) techniques, such as output value comparison using majority voting, timing comparison and using known I/O values for detecting such attacks for our Network-Based Intrusion Detection System for PLCs. We mimic functionalities of PLCs, through virtualization of PLCs’ ladder logic on OpenPLC [7]. We use a record & replay technique for attack mitigation and system restoration. The record & replay system captures pin values of a Pulse Width Modulated (PWM) signal with sensitivity of 50 microseconds. We implement the attacks and our proposed security solution on the control flow logic of a sample industrial gas pipeline PLC network. We achieve a false positive rate of 1% along with a latency of 25 milliseconds in our abnormal detection with setting of 4 virtual PLCs (using OpenPLC [7]), and generated receiver operating characteristic results on different attack rates and ST file logic settings.

Background: The short stator linear induction motor (LIM) is normally used in medium-low speed maglev train. The restriction by mounting space on bogie and motor input voltage from the third power supply rail lead that the maximum speed of medium-low speed maglev train can reach no more than 120 km/h. Aim: In this paper, by means of the LIM design optimization, improvement of the LIM force characteristic in high speed range, the maximum speed of medium-low speed maglev train can reach 160 km/h. Methods: After comparing the LIM theoretical calculation and actual test data, it shows that the new designed LIM is effective. Conclusion: Afterwards, by installing the new designed LIMs, the traditional medium-low speed maglev train becomes a fast-speed maglev train, and it has a bright future in transportation applications.

The emergence of Intel's Optane DC persistent memory (Optane Pmem) draws much interest in building persistent key-value (KV) stores to take advantage of its high throughput and low latency. A major challenge in the efforts stems from the fact that Optane Pmem is essentially a hybrid storage device with two distinct properties. On one hand, it is a high-speed byte-addressable device similar to DRAM. On the other hand, the write to the Optane media is conducted at the unit of 256 bytes, much like a block storage device. Existing KV store designs for persistent memory do not take into account of the latter property, leading to high write amplification and constraining both write and read throughput. In the meantime, a direct re-use of a KV store design intended for block devices, such as LSM-based ones, would cause much higher read latency due to the former property. In this paper, we propose ChameleonDB, a KV store design specifically for this important hybrid memory/storage device by considering and exploiting these two properties in one design. It uses LSM tree structure to efficiently admit writes with low write amplification. It uses an in-DRAM hash table to bypass LSM-tree's multiple levels for fast reads. In the meantime, ChameleonDB may choose to opportunistically maintain the LSM multi-level structure in the background to achieve short recovery time after a system crash. ChameleonDB's hybrid structure is designed to be able to absorb sudden bursts of a write workload, which helps avoid long-tail read latency. Our experiment results show that ChameleonDB improves write throughput by 3.3× and reduces read latency by around 60% compared with a legacy LSM-tree based KV store design. ChameleonDB provides performance competitive even with KV stores using fully in-DRAM index by using much less DRAM space. Compared with CCEH, a persistent hash table design, ChameleonDB provides 6.4× higher write throughput.

High resolution and wide field of view (FOV) are always the goals of imaging, which are related to the space-bandwidth product (SBP) of the system. Currently, most methods focus on either resolution enhancement or FOV extension. Few works pay attention on both. There lacks a generalized framework for the joint space-frequency SBP expansion. We propose such a holographic imaging method, termed phase-space synthesized digital holography (PSH), which can improve and adjust resolution and FOV simultaneously, based on a phase-space analysis. Through a controllable SBP expansion in the phase space by multiangle divergent spherical wave illumination, a synthesized hologram is obtained to reconstruct a resolution-enhanced and FOV-extended image. As a general methodology of SBP expansion, the proposed method could open new insights for the imaging community.

In this letter, we optimize the channel estimator of the cascaded channel in an intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multi-antenna system. In this system, the receiver is equipped with a hybrid architecture adopting quantized beamforming. Different from traditional multiple-input multiple-output (MIMO) systems, the design of channel estimation is challenging since the IRS is usually a passive array with limited signal processing capability. We derive the optimized channel estimator in a closed form by reformulating the problem of cascaded channel estimation in this system, leveraging the typical mean-squared error (MSE) criterion. Considering the presence of possible channel sparsity in mmWave channels, we generalize the proposed method by exploiting the channel sparsity for further performance enhancement and computational complexity reduction. Simulation results verify that the proposed estimator significantly outperforms the existing ones.

Objective. Our study aims to investigate the feasibility of in-ear sensing for human–computer interface. Approach. We first measured the agreement between in-ear biopotential and scalp-electroencephalogram (EEG) signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological signals. Main results. The best performance using in-ear biopotential with global reference reached an average accuracy of 70.22% (cf 92.61% accuracy using scalp-EEG signals), but the performance in-ear biopotential with near-ear reference was poor. Significance. Our results suggest in-ear sensing would be a viable human–computer interface for movement prediction, but careful consideration should be given to the position of the reference electrode.

Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker&rsquo, s performance on typical maritime traffic scenarios through four maritime surveillance videos.

This paper proposes a novel generation method of binary patterns based on the space-time combination with projector defocusing technique. The linear superposition theory of the fringe projection profilometry system is described. In terms of the principle and the digital binary defocusing technique, through the accurate sequential control of hardware, a sinusoidal function with wavelength is sampled in “time” and “space” directions to calculate the widths of the projected binary patterns. Then, a series of corresponding square fringe patterns are generated and projected to form a high-quality sinusoidal fringe pattern in the defocused projection volume (i.e., the measured volume). Finally, the performance of our proposed approach is demonstrated by the simulations and experiments, e.g., the phase root-mean-square error of a simulation with three-step phase-shifting method can reach about 0.0286 Rad. The precision level is similar to that of the compared optimization method. Fortunately, our proposed method is more convenient and less time-consuming because the optimization process is avoided.

In this paper, Ant Colony Optimization algorithm is updated with multiple rules to solve the Time-limited tour planning problem. The precision of entertaining time in each sight can significantly increase the time complexity of an algorithm. With tourist attractions opening hour varies from one another, the shortest path will no longer suit the maximum entertaining time. In order to obtain the comprehensive optimal result of both travel time and travel length, the problem is divided into two major steps. The idea of crossover in Genetic Algorithm is introduced here to jumping out of the local loop of solution. Then, the local optimization of time planning is carried out for reducing the time complexity. Time optimizing values are then embedded into an overall travel distance optimization algorithm to acquire final results. By using the method of traversal, the results are verified which are identical to the traversal answer. To test our model, take Pan An Lake scenic spot, Xuzhou, China as an example to give specific results.

Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.

The Belief-Desire-Intention (BDI) architecture is a framework for studying computational agents capable of rational behaviors. The behaviors of such agents may be modeled by possible world structures, for the specification of the behaviors, CTLBDI may be used. As multi-agent systems are increasingly complex, the problem of their verification is acquiring importance. This work develops a symbolic model checking approach for the verification of CTLBDI properties within the BDI-architecture. In addition, we develop a symbolic approach for checking whether a model satisfies the weak and strong realism constraints. The approaches for model checking and realism checking have been implemented, and the experimental data show that the approaches are able to handle models with a fairly large number of possible worlds.

Multi-rotor UAVs have important applications in many fields such as military and civilian life, among which quad-rotor UAVs are the most widely used. Because of its non-manner characteristics, the ground staff needs to monitor and guide the flight operations of the quad-rotor UAV through the ground monitoring system. Therefore, how to improve the visualization and interactivity of the ground system and realistically display the flight path is one of the hot and difficult spots in related researches. This paper proposes that using the Unity3D virtual reality development platform to apply virtual reality technology to the quad-rotor UAV flight control system, which has important theoretical significance and practical value.

To address the write performance problem suffered by MLC/TLC flash, researchers have proposed hybrid SSD that aims to combine the strengths of SLC flash, used as the write-buffer zone for its superior write performance, and MLC/TLC flash, as the capacity zone for its high storage density. While leveraging SLC as a physical write-buffer zone is proven effective in traditional 2D hybrid SSDs, how to effectively incorporate SLC into a 3D-stacked TLC to form a hybrid SSD has not been studied to the best of our knowledge. Yet this is a timely and important performance issue for 3D-stacked TLC given its one-shot programming scheme that results in much worse write performance than the programming scheme in 2D TLC where pages are associated with different bits of a cell and programmed in sequence separately. We believe that naively adopting the two-physical-zone approach to 3D hybrid SSD will miss a great opportunity for performance optimization because it ignores the inherent four-level parallelism (channel/chip/die/plane) of the flash chip array. To this end, we propose in this paper an SLC and Parallelism Aware hybrid SSD (SPA-SSD) to take full advantages of SLC's superior write performance, the internal multi-level parallelism of SSD, and the high storage density of 3D-stacked TLC flash. Two novel techniques enable SPA-SSD to be highly effective: (1) Type-Parallelism Joint Page Allocation (TPJ-PA), which allocates pages for write transactions according to not only available SLC pages but also parallelism to maximize resource utilization within the hybrid SSD, and (2) Queue-length and Parallelism Constrained Data Migration (QPC-DM), which triggers data migration without degrading user write performance by analyzing the device queue length and available flash resources. To evaluate performance of SPA-SSD, a hybrid SSD simulator, called HybridSim, is developed based on MQSim. Experimental results on HybridSim show that TPJ-PA improves write throughput by 60%, while QPC-DM improves write throughput by up to 10 times. Besides, trace-driven experiments on HybridSSD demonstrate that SPA-SSD improves the write latency to the flash by up to two orders of magnitude over the state-of-the-art designs.

This work is on methodologies for checking multi-agent systems against temporal-epistemic specifications. Since behaviors that comply with temporal-epistemic specifications in general (or theoretically) involve infinite sequences of actions of the involved agents, we must avoid checking such specifications based on such infinite sequences. This work at the theoretical side provides a bounded semantics for the temporal-epistemic specification formalism CTLK such that checking an infinite number of steps can be reduced to checking a finite number of steps, and at the practical side develops an approach based on the semantics and QBF-solving techniques for the verification purpose. The approach has been implemented and experimental data show that there exist verification problems that can be verified by this approach more efficiently than BDD based symbolic model checking.

Digital manufacturing aims to create highly customizable products with higher quality and lower costs by integrating Industrial Internet of Things, big data analytics, cloud computing, and advanced robots into manufacturing plants. As manufacturing machines are increasingly retrofitted with sensors as well as connected via wireless networks or wired Ethernet, digital manufacturing systems are becoming more accessible than ever. While advancement in sensing, artificial intelligence, and wireless technologies enables a paradigm shift in manufacturing, cyber-attacks pose significant threats to the manufacturing sector. This paper presents a review of cybersecurity in digital manufacturing systems from system characterization, threat and vulnerability identification, control, and risk determination aspects as well as identifies challenges and future work.

Lane changing maneuver is one of the most important driving behaviors. Unreasonable lane changes can cause serious collisions and consequent traffic delays. High precision prediction of lane changing intent is helpful for improving driving safety. In this study, by fusing information from vehicle sensors, a lane changing predictor based on Adaptive Fuzzy Neural Network (AFFN) is proposed to predict steering angles. The prediction model includes two parts: fuzzy neural network based on Takagi–Sugeno fuzzy inference, in which an improved Least Squares Estimator (LSE) is adopt to optimize parameters; adaptive learning algorithm to update membership functions and rule base. Experiments are conducted in the driving simulator under scenarios with different speed levels of lead vehicle: 60 km/h, 80 km/h and 100 km/h. Prediction results show that the proposed method is able to accurately follow steering angle patterns. Furthermore, comparison of prediction performance with several machine learning methods further verifies the learning ability of the AFNN. Finally, a sensibility analysis indicates heading angles and acceleration of vehicle are also important factors for predicting lane changing behavior.

The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.

This study explores urban mobility from a network-based perspective. The data samples used in study were collected from more than 1100 taxi drivers during a half year period in the city of Harbin in China. We extract trips from the original dataset and analyze operational efficiency. Then, by constructing travel networks based on occupied and vacant taxi trips, we calculate some statistical properties of the network such as degree, strength, edge weight, betweenness, clustering coefficient and network structure entropy. Analysis of such properties allows for a deep exploration of travel mobility. We also analyze the correlation between strength and betweenness to evaluate the importance of nodes in the network. Furthermore, two traditional community detection algorithms: the Louvain method and the visualization of similarities (VOS) method are applied to divide traffic zones in the mainland area of Harbin city. Two indices, the Rand index (RI) and the Fowkles–Mallows index (FMI) are adopted to evaluate recognition performance, which shows the similarity between administrative division and results from the algorithms. Finally, a dilatation index based on the weighted average distance among trips is applied to analyze the spatial structure of an urban city. Furthermore, hotspots are identified from local density of locations with different thresholds as determined by the Lorenz curve.

For many ground robots, the environment is basically unknown, which requires us to build an environment map so that the robot can start from an unknown location in the unknown environment and locate itself by repeatedly observing the map features during the move. And then gradually build a map according to your location, to achieve the purpose of simultaneous positioning and map construction. This paper mainly introduces the selection of environmental map experimental equipment for the construction of laser radar tracking system and its reasons, analyzes the basic principle of laser radar operation, designs the corresponding experimental scheme, conducts preliminary communication experiments, and finally displays the environment diagram drawn. On the computer, this paper basically completed the work of graphically drawing the scanned data and drawing the environmental map, which is helpful for further research on the tracking system of the laser radar.

Modal logic S5 has found various applications in artificial intelligence. With the advances in modern SAT solvers, SAT-based approach has shown great potential in solving the satisfiability problem of S5. The scale of the SAT encoding for S5 is strongly influenced by the upper bound on the number of possible worlds. In this paper, we present a novel SAT-based approach for S5 satisfiability problem. We show a normal form for S5 formulas. Based on this normal form, a conflict graph can be derived whose chromatic number provides an upper bound of the possible worlds and a lot of unnecessary search spaces can be eliminated in this process. A heuristic graph coloring algorithm is adopted to balance the efficiency and optimality. The number of possible worlds can be significantly reduced for many practical instances. Extensive experiments demonstrate that our approach outperforms state-of-the-art S5-SAT solvers.

Correctness is an important issue in computer science and software engineering. For concurrent systems, the definition of correctness is usually based on properties of infinite execution paths. Bounded correctness is a kind of correctness defined on finite paths, and provides a different view on the issue of correctness. This talk focuses on the concept of bounded correctness and the relation between correctness and bounded correctness. For the purpose of verification, the definition of correctness based on infinite paths is not directly applicable as a means for verification, and the approaches for such a purpose include those based on the analysis of strongly connected components and on the computation of fixed points. On the other hand, correctness may be verified in terms of bounded correctness by an approach derived from the definition of bounded correctness. The complementariness of these verification approaches is explained.

Existing energy distribution strategies of AFL and its variants have two limitations. (1) They focus on increasing coverage but ignore the fact that some code regions are more likely to be vulnerable. (2) They randomly select mutators and deterministically specify the number to mutator, therefore lack insights regarding which granularity of mutators are more helpful at that particular stage. We improve the two limitations of AFL's fuzzing energy distribution in a principled way. We direct the fuzzer to strengthen fuzzing toward regions that have a higher probability to contain vulnerabilities based on static semantic metrics of the target program. Furthermore, granularity-aware scheduling of mutators is proposed, which dynamically assigns ratios to different mutation operators. We implemented these improvements as an extension to AFL. Large-scale experimental evaluations showed the effectiveness of each improvement and performance of integration. The proposed tool has helped us find 12 new bugs and expose three new CVEs.

The rapid and stable development of China&rsquo, s economy has driven the increasing demand for express transportation. Based on network operation, China Railway Corporation of High-speed Railway launched high-speed rail products, which have attracted wide attention from all walks of life. With the application of high-speed express trains, the market structure of express transportation in China will change dramatically, from highways as the main mode of transportation to high-speed railway transportation relying on a high-speed railway network, which will effectively reduce the environmental pollution caused by express transportation and further improve the sustainable development of the economy and the logistics industry. At present, the freight Electric Multiple Units (EMU) has been successfully developed and has entered the final test stage. In the last paper, we have introduced the theory and method of the high-speed rail express train operation plan. In addition, a train diagram is an important foundation of railway transportation organization. In order to ensure the sustainable development of high-speed rail express trains after they are put into use, based on the operation plan of high-speed rail express trains, this paper establishes a comprehensive compilation model of a high-speed rail express train diagram, considering train running time, freight flow distribution scheme, and the operation plan of freight multiple units, and an exact solution algorithm based on the Lagrange relaxation algorithm is designed. The computational results are encouraging and demonstrate the effectiveness of the model and solution method.

In recent years, research on the application of UAV agriculture sowing has developed rapidly. This topic combines the flight characteristics of four-rotor UAV to design and develop a four-rotor UAV virtual simulation system applied in agricultural sowing with using Unity3D engine and realistic picture effects, based on virtual reality technology. This system solves the problem that the restriction of UAV flight in real life, and provides example for virtual reality of the four-rotor UAV agricultural sowing, which has great practical significance.

How to locate the illegal radio station quickly and accurately after the area of the illegal radio station is known has been a problem for radio monitors. In this paper, based on the research of radio direction finding and positioning technology, a set of direction finding and positioning system for close range FM radio is realized by using Tektronix RSA503A spectrum analyzer. The system can locate the FM transmitter in the range of about 10 meters through the experiment of direction finding and positioning in the range of 500 meters.

With the development of multimedia and Internet technologies, the number of movie is growing at an unprecedented rate. How to quickly find the desired movie in a massive movie becomes more and more difficult. At present, most movie retrieval is still in the keyword search stage. This paper proposes a new method to retrieve the corresponding movie information based on movie audio. According to the characteristics of movie audio, this paper selects Shazam algorithm to optimize, which has good performance of the algorithm based on the audio retrieval algorithm in the current. After optimization, the average retrieval accuracy of the algorithm was improved by 0.82%, and it was applied to the mobile terminal. The experimental results proved that the actual retrieval effect was good and of great use value.

High-speed three-dimensional (3D) measurement is increasingly important in many fields. Phase measurement profilometry (PMP) based on the binary defocusing technique has been applied to the high-speed 3D measurement scene for its higher measurement resolution and precision, and breaking the speed limitations of projector. However, because the PMP needs three phase-shifting (3-PS) patterns, motion error is inevitable to measuring dynamic objects. In this research, we construct a complete high-speed 3-PS PMP system, and re-derive two clearer motion error models than those in Weise’s research [Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2007), pp. 110.1109/CVPR.2007.383291]. Then, we theoretically analyze the effects of the truncation error on the model accuracy, especially when the motion error is higher. To this end, a polynominal-based motion error model by fitting coefficient matrix of pre-simulation is proposed. Meanwhile, its corresponding error compensation method based on local domain estimation of the Nelder-Mead algorithm is developed. Finally, both simulations and quantitative and qualitative experiments verify the accuracy and effectiveness of the proposed method, as well as demonstrate the proposed method has improvements compared with the Weise’s research.

Accurate traffic flow data is important for traffic flow state estimation, real-time traffic management and control, etc. Raw traffic flow data collected from inductive detectors may be contaminated by different noises (e.g., sharp data increase/decrease, trivial anomaly oscillations) under various unexpected interference (caused by roadway maintenance, loop detector damage, etc.). To address the issue, we introduced data denoising schemes (i.e., Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet (WL)) to suppress the potential data outliers. After that, the Long Short-Term Memory (LSTM) neural network was introduced to fulfill the traffic flow prediction task. We have tested the proposed framework performance on three traffic flow datasets, which were downloaded from Caltrans Performance Measurement System (PeMS). The experimental results showed that the LSTM+EEMD scheme obtained higher accuracy considering that the average Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are 0.79, 0.60 and 2.14.

With the continuous development of society and economy, human demand for petrochemical-related products and derivatives is becoming more and more vigorous. However, the increase in oilfield development time and the expansion of scale easily make it difficult to control energy consumption. In response to this bottleneck problem, this paper relies on computer technology to explore the development of energy optimization software for oil and gas gathering and transportation systems, which provides convenience for grassroots technical managers by realizing control of the operating parameters of the oil gathering system, realizing process management and fine management., Changed the traditional oilfield management model. At the same time, taking the water-mixing oil collection process as an example, explore the application process and effect of the software in the oil field.