Network Monitoring Traffic Compression Using Singular Value Decomposition

With increasing magnitude of computer network activity, the ability to monitor all network traffic is becoming strained. The need to represent large amounts of data in smaller forms is essential to continued growth of network monitoring tools and network administrators\u27 capabilities. Network monitoring captures many different measurements of the data flowing through the network. This thesis introduces a new method of sending network traffic monitoring data that reduces the overall volume of data from the traditional method of packet capture. By populating a matrix with specific data values in a sparse format, this experiment reduces the data using singular value decomposition (SVD) compression. Matrices were populated using network monitoring datasets from 1996 Information Exploration Shootout (IES). The data populated into the matrices was varied along time frame and data field to determine if the SVD compression algorithm reduced the quantity of original data values. Results indicated that the quantity of data varies dependent on the volume of the data field chosen. The matrix population method was based on port values to allow combining values within the matrix cells. The results trended to a successful reduction of data if the time frame is increased significantly

A Fuzzy Logic Based Algorithm for Finding Astronomical Objects in Wide-Angle Frames

Accurate automatic identification of astronomical objects in an imperfect world of non-linear wide-angle optics, imperfect optics, inaccurately pointed telescopes, and defect-ridden cameras is not always a trivial first step. In the past few years, this problem has been exacerbated by the rise of digital imaging, providing vast digital streams of astronomical images and data. In the modern age of increasing bandwidth, human identifications are many times impracticably slow. In order to perform an automatic computer-based analysis of astronomical frames, a quick and accurate identification of astronomical objects is required. Such identification must follow a rigorous transformation from topocentric celestial coordinates into image coordinates on a CCD frame. This paper presents a fuzzy logic based algorithm that estimates needed coordinate transformations in a practical setting. Using a training set of reference stars, the algorithm statically builds a fuzzy logic model. At runtime, the algorithm uses this model to associate stellar objects visible in the frames to known-catalogued objects, and generates files that contain photometry information of objects visible in the frame. Use of this algorithm facilitates real-time monitoring of stars and bright transients, allowing identifications and alerts to be issued more reliably. The algorithm is being implemented by the Night Sky Live all-sky monitoring global network and has shown itself significantly more reliable than the previously used non-fuzzy logic algorithm.Comment: Accepted for publication in PAS

Deep neural network-based physical distancing monitoring system with tensorRT optimization

During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies

A Study of Basic 3D Visualization Architecture for Network Operation and Management Tools

Recently, network operation tools using 3D visualization technologies have become more and more important. Generally, 3D visualized network operation tools are useful for computer network management or operation. However, a development of 3D visualized network operation tools requires advanced technical skills and highly cost. On the other hand, 3D computer graphics technologies become more familiar in recent years because of that computer hardwares and softwares are rapidly growing and obtain high performance. In this research, we have developed basic architecture of 3D visualization system for network operation and management tools, by using an open source 3DCG software ``Blender'' and a programming language ``Python``. In this paper, we explain details, results of evaluation and efficiency of the proposed architecture

An adaptive envelope analysis in a wireless sensor network for bearing fault diagnosis using fast kurtogram algorithm

This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter

An overview of link-level measurement techniques for wide-area wireless networks

By building wireless link-level measurement tools we hope to improvement the design, deployment and management of wide-area wireless community networks. This paper identifies existing link-level measurement techniques and discusses the advantages and disadvantages of each in the context of measuring and monitoring such networks. Finally, we make a case for the need for more sophisticated techniques and tools which will assist both day-to-day network operations as well as wireless network research

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance