Millisecond Level Research Articles

A NEW METHOD OF FAST REGISTRATION OF UNMANNED AERIAL VEHICLE REMOTE SENSING IMAGES BASED-ON AN IMPROVED SURF ALGORITHM

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

Maintenance vs. Manipulation in Auditory Verbal Working Memory in the Elderly: New Insights Based on Temporal Dynamics of Information Processing in the Millisecond Time Range.

Working memory (WM) is a limited-capacity cognitive system that allows the storage and use of a limited amount of information for a short period of time. Two WM processes can be distinguished: maintenance (i.e., storing, monitoring, and matching information) and manipulation (i.e., reordering and updating information). A number of studies have reported an age-related decline in WM, but the mechanisms underlying this deterioration need to be investigated. Previous research, including studies conducted in our laboratory, revealed that age-related cognitive deficits are related to decreased millisecond timing, i.e., the ability to perceive and organize incoming events in time. The aim of the current study was: (1) to identify in the elderly the brain network involved in the maintenance and manipulation WM processes; and (2) to use an fMRI task to investigate the relation between the brain activity associated with these two processes and the efficiency of temporal information processing (TIP) on a millisecond level reflected by psychophysical indices. Subjects were 41 normal healthy elderly people aged from 62 to 78 years. They performed: (1) an auditory verbal n-back task for assessing WM efficiency in an MRI scanner; and (2) a psychophysical auditory temporal-order judgment (TOJ) task for assessing temporal resolution in the millisecond domain outside the scanner. The n-back task comprised three conditions (0-, 1-, and 2-back), which allowed maintenance (1- vs. 0-back comparisons) and manipulation (2- vs. 1-back comparisons) processes to be distinguished. Results revealed the involvement of a similar brain network in the elderly to that found in previous studies. However, during maintenance processes, we found relatively limited and focused activations, which were significantly extended during manipulation. A novel result of our study, never reported before, is an indication of significant moderate correlations between the efficiency of WM and TIP. These correlations were found only for manipulation but not for maintenance. Our results confirmed the hypothesis that manipulation in the elderly is a dynamic process requiring skilled millisecond timing with high temporal resolution. We conclude that millisecond timing contributes to WM manipulation in the elderly, but not to maintenance.

IoT-Praetor: Undesired Behaviors Detection for IoT Devices

Due to insecure design and configuration, the Internet-of-Things (IoT) devices are vulnerable to various security issues. In most attacks against IoT, e.g., Mirai, attackers control devices to perform malicious behaviors that are not expected by owners and administrators. Therefore, how to effectively detect malicious behaviors is crucial to protect the security of IoT devices. Different from powerful PCs and servers, resource-constrained IoT devices are generally used to execute the specific function and their behaviors are limited. Based on this observation, we propose IoT-Praetor, an undesired behavior security detection system for IoT devices. In IoT-Praetor, a new device usage description (DUD) model is proposed to construct an IoT device behavior specification, including communication and interaction behaviors. Furthermore, automatic behavior extraction approaches are presented. We also design a behavior rule engine to detect device behaviors in real time. To evaluate the effectiveness of IoT-Praetor, we implemented our methods on Samsung SmartThings and performed a security test. The evaluation results show that the successful detection rate of malicious interaction behavior is 94.5% on average, and the detection rate of malicious communication behavior is above 98%, and system running time delay is only in millisecond level.

A long-wavelength turn-on fluorescent probe for intracellular nanomolar level peroxynitrite sensing with second-level response

As a typical kind of endogenous reactive nitrogen species, peroxynitrite (ONOO−) is believed heavily involved in the pathogenesis of many diseases such as inflammation, neurodegenerative conditions, and cardiovascular disorders. Precisely estimating ONOO− level in cell compartments is crucial for unraveling the biological relevance of ONOO− and enabling effective control of ONOO−-associated pathogenicity but suffers from serious difficulty owing to the daunting elusive features of ONOO−, namely nanomolar level physiological concentration and millisecond level biological half-life. A new fluorescent probe capable of detecting ONOO− with limit of detection down to 1.2 nM, response time less than 1s, and high recognition specificity over other similarly interfering species was developed in this work. For the probe constructed by conjugating an isatin moiety with an electron-withdrawing tricyanofuran (TCF) moiety, the former enabled a highly selective ONOO−-mediated oxidative decarbonylation reaction while the latter significantly improved the electrophilicity of the 3-position carbonyl group of isatin moiety and therefore accelerate the ONOO−-mediated nucleophilic attack, which eventually enabled prompt and efficient recognition reaction. For the decarbonylated product featured with a released primary aniline moiety, TCF acted as an acceptor for enabling an intramolecular charge transfer (ICT) process and the remarkable change in electronic feature upon reaction with ONOO−, which generated turn-on fluorescence with large contrast and therefore the basis for ONOO− sensing. The cell fluorescence imaging performed in this work definitely verified the capability of the probe for mapping intracellular ONOO−, despite the daunting elusive features of such physiological species.

SpotLight on Dynamics of Individual Learning.

Averaging performance over a group of individuals implicitly assumes that there is only one set of methods for accomplishing the task and that all learners acquire those methods in the same sequence. We maintain that the average subject is a mythical beast and, rather than profiling a mythical beast, we ask "how do different people each learn the same complex task?" To answer our question, we use SpotLight-a tool for analyzing changes in individual performance as skill is acquired. Specifically, in this report, we use the SpotLight on 40 scores and measures of performance (some collected at millisecond level, others collected once per game), to investigate the skill acquisition of nine individuals each of whom devoted 31h to becoming skilled players of the classic cognitive psychology game of Space Fortress (Mané & Donchin, 1989). Turning the SpotLight on our best and worst players reveals patterns of performance plateaus, dips, and leaps. Examining these patterns reveals differences as well as commonalities in the evolution of methods discovered and used by each individual across 31 hours of skill acquisition.

Variation across Scales: Measurement Fidelity under Twitter Data Sampling

A comprehensive understanding of data quality is the cornerstone of measurement studies in social media research. This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing complete tweet streams, we show that Twitter rate limit message is an accurate indicator for the volume of missing tweets. Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find the Bernoulli process with a uniform rate approximates the empirical distributions well. It also allows us to estimate the true ranking with the observed sample data. For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features. This work calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.

Spin-orbit coupling included all-electron MRCI+Q calculation on excited states of the SbCl molecule

In this paper, all-electron relativistic MRCI+Q calculation has been carried out on electronic and spectroscopic properties of SbCl. In present calculation, total 34 Λ-S and 74 Ω have been reliably characterized for the first time. The lowest three Λ-S states X3Σ−, a1Δ, and b1Σ+ possess a rather deep potential well, and resulting spectroscopic constants are consistent with experiments. The spin-orbit coupling (SOC) effect is considered with the state interaction method, and then the accuracy of computation is further improved. The SOC induced predissociations of a1Δ and b1Σ+ are illuminated by using spin-orbit matrix elements. It is found that the SOC effect is substantial for SbCl. The effect gives rise to a more complicated structure of electronic states. Transition properties of SbCl are estimated. R-dependent transition dipole moments (TDMs) are obtained at the MRCI level. Both a1Δ-X3Σ− and b1Σ+-X3Σ− borrow TDMs from spin-allowed Λ-S transitions via SOC. The lifetimes of a1Δ and b1Σ+ are determined at a millisecond (ms) level.

S78. TIME PREDICTION AND SENSE OF SELF: LACK OF FLEXIBILITY IN PATIENTS WITH SCHIZOPHRENIA

BackgroundThe sense of time continuity appears to be disturbed in pathologies like schizophrenia, associated with a disruption of the sense of self, and of the feeling of being immersed in the world. Prediction mechanisms have been proposed to be involved in the sense of time continuity by helping to relate discontinuous events, and our previous studies have suggested that these mechanisms may occur even at the millisecond level. Such mechanisms would be involved in our ability to interact with the outer world, by helping to follow events accurately in both space and time. We explored prediction mechanisms and attention shifts based on recently experienced sequences of visual information (sequential effects).Methods26 patients with schizophrenia and 20 matched controls were included in asynchrony discrimination tasks, and assessments of minimal self disorders with the EASE. On each trial, two stimuli are displayed with varying stimulus onset asynchronies (SOA). Subjects decide whether the stimuli are simultaneous or asynchronous and give manual responses. Sequential effects were explored further in several groups of 15 healthy participants.ResultsThe main finding in controls is a strong accuracy advantage for different- as compared to same-order trials, but only when trial t is with an SOA slightly larger than trial t-1 (advantage of 16% in accuracy), or equivalent (advantage of 10% in accuracy). An advantage for same-vs. different-order trials is observed only when the SOA on the previous trial is large and visible. In patients, there is no advantage for same-order trials. There is a clear advantage for different-order trials (advantage of 16% in accuracy), but this effect disappears, contrary to controls, when SOAs are equivalent on successive trials (1% difference in accuracy). The impairment in the trial-to-trial effects in patients correlates with minimal self disorders (the EASE).DiscussionFurther investigations in healthy participants suggest that the sequential effects can be explained in terms of prediction of stimulus sequences from trial to trial, which are accompanied by an attention shift. The first stimulus triggers the onset of the sequence, and attention is then covertly shifted in space and time according to the previous trial, in order to attend to the second stimulus. This explains the advantage for different-order trials: when order is reversed on the present trial, attention ends up in the location of the first stimulus of the present sequence. This first stimulus is perceived as isolated on the screen if the second stimulus occurs later than on the previous trial, thus facilitating the detection of an asynchrony. The asynchrony is then obvious, explaining the large amplitude of the effect. The fact that the effect extends to the condition in which SOAs are equivalent on successive trials suggests that participants shift their attention in advance, as if anticipating the location of the second stimulus. This is impaired in patients, who replay sequences of events, but do not anticipate the successive events flexibly. This would impair their immersion in the world, where events rarely happen twice at the same time exactly.

Intent defined optical network with artificial intelligence-based automated operation and maintenance

Traditionally, the operation and maintenance of optical networks rely on the experience of engineers to configure network parameters, involving command-line interface, middle-ware scripting, and troubleshooting. However, with the emerging of newly B5G applications, the traditional configuration cannot meet the requirement of real-time automatic configuration. Operators need a new configuration way without manual intervention at an underlying optical transport network. To cope with this issue, we propose an intent defined optical network (IDON) architecture toward artificial intelligence-based optical network automated operation and maintenance against service objective, by introducing a self-adapted generation and optimization (SAGO) policy in a customized manner. The IDON platform has three key innovations including intent-orient configuration translation, self-adapted generation and optimization policy, and close-loop intent guarantee operation. Focusing specifically on communication requirements, the IDON uses natural language processing to construct semantic graphs to understand, interact, and create the required network configuration. Then, deep reinforcement learning (DRL) is utilized to find the composition policy that satisfies the requirement of intent through the dynamic integration of fine-grained policies. Finally, the deep neural evolutionary network (DNEN) is introduced to achieve the intent guarantee at the milliseconds level. The feasibility and efficiency are verified on enhanced SDN testbed. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of intent defined optical network.

Joint estimation of DOA and polarisation for radar signals based on dual‐polarised antenna array

To apply polarisation information of radar signal in radar reconnaissance equipment, a joint estimation algorithm of the radar signal direction of arrival (DOA) and polarisation parameters based on dual-polarisation antenna array has been researched in this study. The mathematical model of dual-polarised circular array has been given, and the joint estimation method of DOA and polarisation parameters for radar signal based on multiple signal classification (MUSIC) algorithm has been analysed. To overcome the shortcomings such as high calculation cost for the MUSIC algorithm, the DOA-matrix algorithm has been presented. DOA-matrix algorithm can effectively distinguish the radar signals with different polarisation states in the same direction by use of eigen-decomposition of the DOA-matrix. Through matrix correlation calculation, spectral peak search has been avoided, and the computational cost and algorithm complexity are greatly reduced. Finally, the feasibility of the DOA-matrix algorithm has been simulated and analysed. The simulation results show that the DOA-matrix algorithm can effectively complete the joint estimation of DOA and polarisation parameters for radar signal. The running time reaches millisecond level, which has a strong application value in engineering.

A convolutional neural network approach for visual recognition in wheel production lines

China has been the world’s largest automotive manufacturing country since 2008. The automotive wheel industry in China has been growing steadily in pace with the automobile industry. Visual recognition system that automatically classifies wheel types is a key component in the wheel production line. Traditional recognition methods are mainly based on extracted feature matching. Their accuracy, robustness, and processing speed are often compromised considerably in actual production. To overcome this problem, we proposed a convolutional neural network approach to adaptively classify wheel types in actual production lines with a complex visual background. The essential steps to achieve wheel identification include image acquisition, image preprocessing, and classification. The image differencing algorithm and histogram technique are developed on acquired wheel images to remove track disturbances. The wheel images after image processing were organized into training and test sets. This approach improved the residual network model ResNet-18 and then evaluated this model based on the wheel test data. Experiments showed that this method can obtain an accuracy over 98% on nearly 70,000 wheel images and its single image processing time can reach millisecond level.

Helical Magnetic Field-Induced Real-Time Plasmonic Chirality Modulation.

The astrophysical phenomenon of mimetic helical magnetic field (hB)-assisted self-assembly is herein introduced to build helical superstructures that display chiroptical properties. As a building block, magnetoplasmonic (MagPlas) Ag@Fe3O4 core-shell nanoparticles are used to guide plasmonic Ag nanoparticles onto a helical magnetic flux. The chirality of the assembled helical structures and tailored circular dichroism are successfully tuned in real time, and the handedness of the assembled structures is dynamically switched by the hB at the millisecond level, which is at least 6000-fold faster than other template-assisted methods. The peak position of circular dichroism can be reconfigured by altering the plasmonic resonance or coupling by controlling the size of the Ag core and magnetic flux density. The hB-induced chirality modulation represents a method to control the polarization state of light at the nexus of plasmonics, magnetic self-assembly, colloidal science, liquid crystals, and chirality. It presents active and dynamic chiral assemblies of magnetoplasmonic nanomaterials, enabling further practical applications in optical devices.

Cache-Enabled Coordinated Mobile Edge Network: Opportunities and Challenges

Cache-enabled coordinated mobile edge network is an emerging network architecture, wherein serving nodes located at the network edge have the capabilities of baseband signal processing and caching files at their local cache. The main goals of such an emerging network architecture are to alleviate the burden on fronthaul links, while achieving low-latency high rate content delivery, say on the order of the millisecond level end-to-end content delivery latency. Consequently, the application of delay-sensitive and content-aware has been executed in close proximity to end users. In this article, an outlook of research directions, challenges, and opportunities is provided and discussed in depth. We first introduce the cache-enabled coordinated mobile edge network architecture, and then discuss the key technical challenges and opening research issues that need to be addressed for this architecture. Then, to reduce the delivery latency and the burden on fronthaul links, new cache-enabled physical layer transmission schemes are discussed. Finally, artificial intelligence based cache-enabled communications are discussed as future research directions. Numerical studies show that several gains are achieved by caching popular content at the network edge with proper coordinated transmission schemes.

Neural oscillations in the fronto-striatal network predict vocal output in bats

The ability to vocalize is ubiquitous in vertebrates, but neural networks underlying vocal control remain poorly understood. Here, we performed simultaneous neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus, CN) during the production of echolocation pulses and communication calls in bats. This approach allowed us to assess the general aspects underlying vocal production in mammals and the unique evolutionary adaptations of bat echolocation. Our data indicate that before vocalization, a distinctive change in high-gamma and beta oscillations (50-80 Hz and 12-30 Hz, respectively) takes place in the bat frontal cortex and dorsal striatum. Such precise fine-tuning of neural oscillations could allow animals to selectively activate motor programs required for the production of either echolocation or communication vocalizations. Moreover, the functional coupling between frontal and striatal areas, occurring in the theta oscillatory band (4-8 Hz), differs markedly at the millisecond level, depending on whether the animals are in a navigational mode (that is, emitting echolocation pulses) or in a social communication mode (emitting communication calls). Overall, this study indicates that fronto-striatal oscillations could provide a neural correlate for vocal control in bats.

Where and when to look: Sequential effects at the millisecond level

Learning and imitating a complex motor action requires to visually follow complex movements, but conscious perception seems too slow for such tasks. Recent findings suggest that visual perception has a higher temporal resolution at an unconscious than at a conscious level. Here we investigate whether high-temporal resolution in visual perception relies on prediction mechanisms and attention shifts based on recently experienced sequences of visual information. To that aim we explore sequential effects during four different simultaneity/asynchrony discrimination tasks. Two stimuli are displayed on each trial with varying stimulus onset asynchronies (SOA). Subjects decide whether the stimuli are simultaneous or asynchronous and give manual responses. The main finding is an advantage for different-order over same-order trials, when subjects decided that stimuli had been simultaneous on Trial t - 1 , and when Trial t is with an SOA slightly larger than Trial t - 1, or equivalent. The advantage for different-order trials disappears when the stimuli change eccentricity but not direction between trials (Experiment 2), and persists with stimuli displayed in the centre and unlikely to elicit a sense of direction (Experiment 4). It is still observed when asynchronies on Trial t - 1 are small and undetected (Experiment 3). The findings can be explained by an attention shift that is precisely planned in time and space and that incidentally allows subjects to detect an isolated stimulus on the screen, thus helping them to detect an asynchrony.

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Research on Linux Real-time and performance Evaluation for Loongson 3A3000 processor

In order to solve the real-time problem of Linux system for loongson3A, the real-time enhancement design of Linux kernel is carried out by using the idea of PREEMPT_RT patch, the performance analysis support based on ftrace tool is improved, and the real-time optimization method based on reserved core scheduling is proposed. The real-time performance of the system is tested from four aspects: context switching, interrupt response, scheduling latency and network latency. Five context switching methods are proposed, including active switching, priority preemptive switching, semaphores, message queuing and signal distribution latency. A test method based on the internal timer of the PCI board is designed. The test results show that the maximum latency of real-time Linux system based on Loongson3A3000 is less than 30us, the reserved core scheduling latency is the minimum, and the maximum jitter of network communication latency is less than 120us. In conclusion, the system based on loongson3A3000 can meet the demand for hard real-time applications at millisecond level.

Design of a testing system for dynamic arming time of clock mechanism in fuze safety system

In view of the fact that the specific time of arming can not be obtained when testing the arming of fuze safety system at home and abroad at present, and there are some problems such as cost and repeatability, a testing system based on the dynamic arming time of fuze safety system is proposed. In this system, the mechanical signal is transformed into electrical signal by micro switch, laser and photodiode, and the millisecond level accurate timing of dynamic arming time of fuze safety system is realized. Through the design of start, timing, stop modules and display modules, the calculation and display functions of the actual arming time of fuse components after dynamic simulation are completed. The test and data analysis show that the measurement accuracy of the test system is high and the error is small, which meets the accuracy requirements.

Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping

Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular and time-varying traffic requests in future multi-beam satellite systems. In BH optimization, adopting conventional iterative heuristics may have their own limitations in providing timely solutions, and directly using data-driven technique to approximate optimization variables may lead to constraint violation and degraded performance. In this paper, we explore a combined learning-and-optimization (LO 2) Features to be learned in BH design; 3) How to address the feasibility issue incurred by machine learning. We provide numerical results and analysis to show that the learning component in L&O significantly accelerates the procedure of identifying promising BH patterns, resulting in reduced computing time from seconds/minutes to milliseconds level. The identified learning feature enables high accuracy in predictions. In addition, the optimization component in L&O guarantees the solution’s feasibility and improves the overall performance with around 5% gap to the optimum.

Migration strategy of cloud collaborative computing for delay-sensitive industrial IoT applications in the context of intelligent manufacturing

In the context of intelligent manufacturing, machinery and equipment in the industrial manufacturing process form the “industrial Internet of Things.” In this process of interlocking production, the requirements for sensor data delay typically reach the millisecond level. Once the data is delayed, the equipment will be shut down, which will make the production difficult or dangerous. In the context of intelligent manufacturing, local computers have been unable to complete calculations and decisions quickly and on time for the huge computing demands. Therefore, the cloud computing migration mode needs to be introduced, but cloud computing migration will cause additional delays. Based on the above problems, this paper designs a cloud cooperative migration strategy based on the information exchange structure of the industrial Internet of Things and the delay mechanism caused by the migration. The feasibility of selecting the optimal migration strategy based on task partitioning is verified by simulation.