scholarly journals Rainfall eld reconstruction using rain attenuation of microwave mesh networks

1970 ◽  
Vol 7 (2) ◽  
pp. 117-125
Author(s):  
Gemalyn Dacillo Abrajano ◽  
Minoru Okada
Keyword(s):  
Compressed Sensing ◽  
Weather Forecasting ◽  
Mesh Networks ◽  
Detection System ◽  
Rain Attenuation ◽  
Detection Accuracy ◽  
Reconstruction Method ◽  
Weather Patterns ◽  
Communication Links ◽  
Rain Events

Rainfall detection using commercial communication links can complement the existing weather satellite and radar system. Using the rain attenuation data from these networks, the paper proposes a new method of reconstructing the rainfall field through a compressed-sensing based algorithm. Compressed sensing is a new algorithm that can reconstruct a sparse signal from a relatively small number of measurements. System configurations of two links and three links crossing a single area are studied and the detection accuracies for rain location and attenuation level are evaluated. The proposed method is also tested for different climate and weather patterns. The results show that the proposed method has good location detection accuracy, and the rain intensity was reconstructed as well. The proposed detection system and reconstruction method can greatly benefit the tropical countries that experience frequent rains but do not have enough resources for weather forecasting and detection of possible disaster-inducing rain events.

scholarly journals Comparison of Measured Rain Attenuation in the 12.25 GHz Band with Predictions by the ITU-R Model

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Dong You Choi ◽  
Jae Young Pyun ◽  
Sun Kuh Noh ◽  
Sang Woong Lee
Keyword(s):  
Quantitative Analysis ◽  
Prediction Models ◽  
Rain Attenuation ◽  
Efficient Design ◽  
Wet Season ◽  
Communication Links ◽  
Rain Events

Quantitative analysis and prediction of radio attenuation is necessary in order to improve the reliability of satellite-earth communication links and for economically efficient design. For this reason, many countries have made efforts to develop their own rain attenuation prediction models that are suited to their rain environment. In this paper, we present the results of measurements of rain-induced attenuation in vertically polarized signals propagating at 12.25 GHz during certain rain events, which occurred in the rainy wet season of 2001 and 2007 at Yong-in, Korea. The rain attenuation over the link path was measured experimentally and compared with the attenuation obtained using the ITU-R model.

scholarly journals Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

2020 ◽  
Vol 10 (7) ◽  
pp. 2503 ◽  
Author(s):  
Fan Wu ◽  
Pin Cao ◽  
Yubin Du ◽  
Haotian Hu ◽  
Yongying Yang
Keyword(s):  
Image Reconstruction ◽  
Evaluation System ◽  
Surface Defects ◽  
Detection System ◽  
Weighted Average ◽  
Calibration Method ◽  
Detection Accuracy ◽  
Reconstruction Method ◽  
Spot Scanning ◽  
Defect Image

Compared to traditional approaches, the spot scanning surface defect evaluation system (SS-SDES) has better performances on the detection of small defects and defect classification for optical surfaces. However, the existing system deviations will cause distortions and even a missing area in the defect image which is reconstructed from the acquired raw data based on the scanning trace, thus degrading the reliability of detection results. To solve these problems, a system calibration method is proposed with the parameterization of these deviations and the modeling of practical scanning trace. A constraint function, to characterize the straightness and scale errors in the image, is defined. Then an optimization is implemented to minimize it and hence to obtain the optimal estimate of the system deviations, which is subsequently used to adjust the system and reconstruct reliable defect images. Additionally, to further enhance the image quality, an image reconstruction method capable of suppressing signal noise through a weighted average strategy is proposed. Experiments show that with our methods, the system deviations are effectively corrected, and a complete and precise defect image with low distortions that are within 1.8 pixels is reconstructed. Therefore, the detection accuracy and reliability of the system can be improved.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

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