Computation of Traffic Time Series for Large Populations of IoT Devices

The Internet of Things (IoT) contains sets of hundreds of thousands of network-enabled devices communicating with central controlling nodes or information collectors. The correct behaviour of these devices can be monitored by inspecting the traffic that they create. This passive monitoring methodology allows the detection of device failures or security breaches. However, the creation of hundreds of thousands of traffic time series in real time is not achievable without highly optimised algorithms. We herein compare three algorithms for time-series extraction from traffic captured in real time. We demonstrate how a single-core central processing unit (CPU) can extract more than three bidirectional traffic time series for each one of more than 20,000 IoT devices in real time using the algorithm DStries with recursive search. This proposal also enables the fast reconfiguration of the analysis computer when new IoT devices are added to the network.

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An efficient solution for fast generation of multi-GNSS real-time products

10.5194/egusphere-egu21-8306 ◽

2021 ◽

Author(s):

Hongjie Zheng ◽

Hanyu Chang ◽

Yongqiang Yuan ◽

Qingyun Wang ◽

Yuhao Li ◽

...

Keyword(s):

Data Processing ◽

Real Time ◽

Processing Time ◽

Efficient Solution ◽

Gpu Computing ◽

Sampling Rate ◽

Precise Orbit Determination ◽

Processing Unit ◽

Processing Efficiency ◽

Central Processing

Global navigation satellite systems (GNSS) have been playing an indispensable role in providing positioning, navigation and timing (PNT) services to global users. Over the past few years, GNSS have been rapidly developed with abundant networks, modern constellations, and multi-frequency observations. To take full advantages of multi-constellation and multi-frequency GNSS, several new mathematic models have been developed such as multi-frequency ambiguity resolution (AR) and the uncombined data processing with raw observations. In addition, new GNSS products including the uncalibrated phase delay (UPD), the observable signal bias (OSB), and the integer recovery clock (IRC) have been generated and provided by analysis centers to support advanced GNSS applications.&#160;&#160;&#160;&#160;&#160;&#160; However, the increasing number of GNSS observations raises a great challenge to the fast generation of multi-constellation and multi-frequency products. In this study, we proposed an efficient solution to realize the fast updating of multi-GNSS real-time products by making full use of the advanced computing techniques. Firstly, instead of the traditional vector operations, the &#8220;level-3 operations&#8221; (matrix by matrix) of Basic Liner Algebra Subprograms (BLAS) is used as much as possible in the Least Square (LSQ) processing, which can improve the efficiency due to the central processing unit (CPU) optimization and faster memory data transmission. Furthermore, most steps of multi-GNSS data processing are transformed from serial mode to parallel mode to take advantage of the multi-core CPU architecture and graphics processing unit (GPU) computing resources. Moreover, we choose the OpenBLAS library for matrix computation as it has good performances in parallel environment.&#160;&#160;&#160;&#160;&#160;&#160; The proposed method is then validated on a 3.30 GHz AMD CPU with 6 cores. The result demonstrates that the proposed method can substantially improve the processing efficiency for multi-GNSS product generation. For the precise orbit determination (POD) solution with 150 ground stations and 128 satellites (GPS/BDS/Galileo/GLONASS/QZSS) in ionosphere-free (IF) mode, the processing time can be shortened from 50 to 10 minutes, which can guarantee the hourly updating of multi-GNSS ultra-rapid orbit products. The processing time of uncombined POD can also be reduced by about 80%. Meanwhile, the multi-GNSS real-time clock products can be easily generated in 5 seconds or even higher sampling rate. In addition, the processing efficiency of UPD and OSB products can also be increased by 4-6 times.

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New Optimal Solutions for Real-Time Reconfigurable Periodic Asynchronous Operating System Tasks with Minimizations of Response Time

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2012100105 ◽

2012 ◽

Vol 1 (4) ◽

pp. 88-131 ◽

Cited By ~ 2

Author(s):

Hamza Gharsellaoui ◽

Mohamed Khalgui ◽

Samir Ben Ahmed

Keyword(s):

Response Time ◽

Real Time ◽

Scheduling Algorithm ◽

Processing Unit ◽

Software Faults ◽

Worst Case ◽

Agent Based ◽

Central Processing ◽

Technical Solutions ◽

Task Systems

Scheduling tasks is an essential requirement in most real-time and embedded systems, but leads to unwanted central processing unit (CPU) overheads. The authors present a real-time schedulability algorithm for preemptable, asynchronous and periodic reconfigurable task systems with arbitrary relative deadlines, scheduled on a uniprocessor by an optimal scheduling algorithm based on the earliest deadline first (EDF) principles and on the dynamic reconfiguration. A reconfiguration scenario is assumed to be a dynamic automatic operation allowing addition, removal or update of operating system’s (OS) functional asynchronous tasks. When such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated. The authors propose an intelligent agent-based architecture where a software agent is used to satisfy the user requirements and to respect time constraints. The agent dynamically provides precious technical solutions for users when these constraints are not verified, by removing tasks according to predefined heuristic, or by modifying the worst case execution times (WCETs), periods, and deadlines of tasks in order to meet deadlines and to minimize their response time. They implement the agent to support these services which are applied to a Blackberry Bold 9700 and to a Volvo system and present and discuss the results of experiments.

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Real-Time 3D Reconstruction of Thin Surface Based on Laser Line Scanner

Sensors ◽

10.3390/s20020534 ◽

2020 ◽

Vol 20 (2) ◽

pp. 534 ◽

Cited By ~ 1

Author(s):

Yuan He ◽

Shunyi Zheng ◽

Fengbo Zhu ◽

Xia Huang

Keyword(s):

3D Reconstruction ◽

Real Time ◽

Laser Line ◽

Frame Rate ◽

Processing Unit ◽

Memory Usage ◽

Central Processing ◽

Time Performance ◽

Topological Errors ◽

Line Scanner

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.

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Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision

Symmetry ◽

10.3390/sym11040585 ◽

2019 ◽

Vol 11 (4) ◽

pp. 585

Author(s):

Yufei Wu ◽

Xiaofei Ruan ◽

Yu Zhang ◽

Huang Zhou ◽

Shengyu Du ◽

...

Keyword(s):

Real Time ◽

Pose Estimation ◽

Graphics Processing Unit ◽

Parallel Execution ◽

Processing Unit ◽

Network Efficiency ◽

Hand Pose Estimation ◽

Central Processing ◽

Deployment Optimization ◽

Hand Pose

The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal.

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Real-Time Particle Swarm Optimization on FPGA for the Optimal Message-Chain Structure

Electronics ◽

10.3390/electronics7110274 ◽

2018 ◽

Vol 7 (11) ◽

pp. 274 ◽

Cited By ~ 4

Author(s):

Heoncheol Lee ◽

Kipyo Kim ◽

Yongsung Kwon ◽

Eonpyo Hong

Keyword(s):

Particle Swarm Optimization ◽

Real Time ◽

Particle Swarm ◽

Chain Structure ◽

Processing Unit ◽

Swarm Optimization ◽

Central Processing ◽

Time Optimization ◽

New Variant ◽

Real Time Optimization

This paper addresses the real-time optimization problem of the message-chain structure to maximize the throughput in data communications based on half-duplex command-response protocols. This paper proposes a new variant of the particle swarm optimization (PSO) algorithm to resolve real-time optimization, which is implemented on field programmable gate arrays (FPGA) to be performed faster in parallel and to avoid the delays caused by other tasks on a central processing unit. The proposed method was verified by finding the optimal message-chain structure much faster than the original PSO, as well as reliably with different system and algorithm parameters.

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Web Traffic Time Series Forecasting using ARIMA and LSTM RNN

ITM Web of Conferences ◽

10.1051/itmconf/20203203017 ◽

2020 ◽

Vol 32 ◽

pp. 03017

Author(s):

Tejas Shelatkar ◽

Stephen Tondale ◽

Swaraj Yadav ◽

Sheetal Ahir

Keyword(s):

Time Series ◽

Real Time ◽

Time Series Forecasting ◽

Web Traffic ◽

Time Data ◽

Traffic Forecasting ◽

Traffic Time Series ◽

Breakdown Time ◽

Server Breakdown ◽

Multiple Domains

Nowadays, web traffic forecasting is a major problem as this can cause setbacks to the workings of major websites. Time-series forecasting has been a hot topic for research. Predicting future time series values is one of the most difficult problems in the industry. The time series field encompasses many different issues, ranging from inference and analysis to forecasting and classification. Forecasting the network traffic and displaying it in a dashboard that updates in real-time would be the most efficient way to convey the information. Creating a Dashboard would help in monitoring and analyzing real-time data. In this day and age, we are too dependent on Google server but if we want to host a server for large users we could have predicted the number of users from previous years to avoid server breakdown. Time Series forecasting is crucial to multiple domains. ARIMA; LSTM RNN; web traffic; prediction;time series;

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Real-Time Monte Carlo Optimization on FPGA for the Efficient and Reliable Message Chain Structure

Electronics ◽

10.3390/electronics8080866 ◽

2019 ◽

Vol 8 (8) ◽

pp. 866 ◽

Cited By ~ 1

Author(s):

Heoncheol Lee ◽

Kipyo Kim

Keyword(s):

Monte Carlo ◽

Real Time ◽

Communication Systems ◽

Optimization Method ◽

Chain Structure ◽

Processing Unit ◽

Monte Carlo Optimization ◽

Central Processing ◽

Field Programmable ◽

Programmable Gate Arrays

This paper addresses the real-time optimization problem to find the most efficient and reliable message chain structure in data communications based on half-duplex command–response protocols such as MIL-STD-1553B communication systems. This paper proposes a real-time Monte Carlo optimization method implemented on field programmable gate arrays (FPGA) which can not only be conducted very quickly but also avoid the conflicts with other tasks on a central processing unit (CPU). Evaluation results showed that the proposed method can consistently find the optimal message chain structure within a quite small and deterministic time, which was much faster than the conventional Monte Carlo optimization method on a CPU.

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GPU Accelerated real-time Melanoma Detection

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.13169 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1208

Author(s):

Ajai Sunny Joseph ◽

Elizabeth Isaac

Keyword(s):

Real Time ◽

Video Processing ◽

Processing Technique ◽

Video Data ◽

Image Processing Technique ◽

Processing Unit ◽

Central Processing ◽

Melanoma Detection ◽

Real Time Analysis ◽

Graphical Processing

Melanoma is recognized as one of the most dangerous type of skin cancer. A novel method to detect melanoma in real time with the help of Graphical Processing Unit (GPU) is proposed. Existing systems can process medical images and perform a diagnosis based on Image Processing technique and Artiﬁcial Intelligence. They are also able to perform video processing with the help of large hardware resources at the backend. This incurs signiﬁcantly higher costs and space and are complex by both software and hardware. Graphical Processing Units have high processing capabilities compared to a Central Processing Unit of a system. Various approaches were used for implementing real time detection of Melanoma. The results and analysis based on various approaches and the best approach based on our study is discussed in this work. A performance analysis for the approaches on the basis of CPU and GPU environment is also discussed. The proposed system will perform real-time analysis of live medical video data and performs diagnosis. The system when implemented yielded an accuracy of 90.133% which is comparable to existing systems.

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Near real-time digital holographic imaging on conventional central processing unit

Optical Measurement Systems for Industrial Inspection XI ◽

10.1117/12.2526112 ◽

2019 ◽

Author(s):

Vira R. Besaga ◽

Anton V. Saetchnikov ◽

Nils C. Gerhardt ◽

Andreas Ostendorf ◽

Martin R. Hofmann

Keyword(s):

Real Time ◽

Central Processing Unit ◽

Processing Unit ◽

Central Processing ◽

Holographic Imaging

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Generating Technology Evolution Prediction Intervals With Bootstrap Method

Volume 7: 30th International Conference on Design Theory and Methodology ◽

10.1115/detc2018-85259 ◽

2018 ◽

Author(s):

Guanglu Zhang ◽

Douglas Allaire ◽

Daniel A. McAdams ◽

Venkatesh Shankar

Keyword(s):

Time Series ◽

Bootstrap Method ◽

Development Planning ◽

Prediction Intervals ◽

Data Uncertainty ◽

Processing Unit ◽

Holdout Sample ◽

Technology Evolution ◽

Central Processing

Technology evolution prediction, or technological forecasting, is critical for designers to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecast by prediction intervals to assess future uncertainty and make contingency plans. Available technology evolution data is a time series but is generally with non-uniform spacing. Existing methods associated with typical time series models assume uniformly spaced data, so these methods cannot be used to construct prediction intervals for technology evolution prediction. In this paper, we develop a generic method that use bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any technology evolution prediction model. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level α to generate prediction intervals through a holdout sample analysis rather than set α = 0.05 as is typically done in the literature. We validate our method to generate the prediction intervals through a case study of central processing unit transistor count evolution. The case study shows that the prediction intervals generated by our method cover every actual data point in a holdout sample test. To apply our method in practice, we outline four steps for designers to generate prediction intervals for technology evolution prediction.

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