scholarly journals For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

2021 ◽  
Author(s):  
Changhao Ge ◽  
Shubo Yang ◽  
Wenjian Sun ◽  
Yang Luo ◽  
Chunbo Luo
Keyword(s):  
Performance Management ◽  
High Performance ◽  
Control Strategies ◽  
Classification Model ◽  
Emergency Control ◽  
Average Accuracy ◽  
Rf Signals ◽  
State Detection ◽  
Significant Research ◽  
Rf Signal

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

scholarly journals For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

2021 ◽  
Author(s):  
Changhao Ge ◽  
Shubo Yang ◽  
Wenjian Sun ◽  
Yang Luo ◽  
Chunbo Luo
Keyword(s):  
Performance Management ◽  
High Performance ◽  
Control Strategies ◽  
Classification Model ◽  
Emergency Control ◽  
Average Accuracy ◽  
Rf Signals ◽  
State Detection ◽  
Significant Research ◽  
Rf Signal

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

scholarly journals A photonic microwave and RF integrator based on a transversal filter

2020 ◽  
Author(s):  
David Moss ◽  
xingyuan xu ◽  
mengxi tan ◽  
Jiayang Wu ◽  
Roberto Morandotti ◽  
...  
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

<p><b>We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.</b></p>

A photonic based microwave and RF integrator with a transversal filter

2021 ◽  
Author(s):  
david moss
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

Abstract We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

scholarly journals A photonic microwave and RF integrator based on a transversal filter

2020 ◽  
Author(s):  
David Moss ◽  
xingyuan xu ◽  
mengxi tan ◽  
Jiayang Wu ◽  
Roberto Morandotti ◽  
...  
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

<p><b>We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.</b></p>

scholarly journals IEEE Trans on Circuits and Systems Photonic Integrator with Microcombs

2020 ◽  
Author(s):  
David Moss
Keyword(s):  
High Performance ◽  
Time Window ◽  
Single Mode ◽  
Integration Time ◽  
Signal Integration ◽  
Diverse Range ◽  
Time Bandwidth Product ◽  
Rf Signals ◽  
High Degree ◽  
Rf Signal

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large time-bandwidth product of 81. Our approach also features a high degree of reconfigurability, by simply adjusting the value of dispersion (i.e., the length of dispersive fibre), the integration time window and resolution can be reconfigured to accommodate a diverse range of applications. We employed 13 km of standard single-mode fibre to achieve a large integration time window of ~6.8 ns, a time resolution as fast as ~84 ps, with a broad bandwidth of 11.9 GHz. In addition, we perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

Performance Management Transformation

2020 ◽  
Keyword(s):  
Organizational Performance ◽  
Performance Management ◽  
High Performance ◽  
Best Practice ◽  
Call To Action ◽  
Daily Work ◽  
The Past ◽  
Great Debate ◽  
The Subject ◽  
New Research

No other talent process has been the subject of such great debate and emotion as performance management (PM). For decades, different strategies have been tried to improve PM processes, yielding an endless cycle of reform to capture the next “flavor-of-the-day” PM trend. The past 5 years, however, have brought novel thinking that is different from past trends. Companies are reducing their formal processes, driving performance-based cultures, and embedding effective PM behavior into daily work rather than relying on annual reviews to drive these. Through case studies provided from leading organizations, this book illustrates the range of PM processes that companies are using today. These show a shift away from adopting someone else’s best practice; instead, companies are designing bespoke PM processes that fit their specific strategy, climate, and needs. Leading PM thought leaders offer their views about the state of PM today, what we have learned and where we need to focus future efforts, including provocative new research that shows what matters most in driving high performance. This book is a call to action for talent management professionals to go beyond traditional best practice and provide thought leadership in designing PM processes and systems that will enhance both individual and organizational performance.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Investigating Lignin-Derived Monomers and Oligomers in Low-Molecular-Weight Fractions Separated from Depolymerized Black Liquor Retentate by Membrane Filtration

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2887
Author(s):  
Kena Li ◽  
Jens Prothmann ◽  
Margareta Sandahl ◽  
Sara Blomberg ◽  
Charlotta Turner ◽  
...  
Keyword(s):  
Molecular Weight ◽  
High Performance ◽  
Membrane Filtration ◽  
Black Liquor ◽  
Complex Mixture ◽  
Value Added ◽  
Kraft Pulping ◽  
Classification Model ◽  
Low Molecular Weight ◽  
Chemical Complexity

Base-catalyzed depolymerization of black liquor retentate (BLR) from the kraft pulping process, followed by ultrafiltration, has been suggested as a means of obtaining low-molecular-weight (LMW) compounds. The chemical complexity of BLR, which consists of a mixture of softwood and hardwood lignin that has undergone several kinds of treatment, leads to a complex mixture of LMW compounds, making the separation of components for the formation of value-added chemicals more difficult. Identifying the phenolic compounds in the LMW fractions obtained under different depolymerization conditions is essential for the upgrading process. In this study, a state-of-the-art nontargeted analysis method using ultra-high-performance supercritical fluid chromatography coupled to high-resolution multiple-stage tandem mass spectrometry (UHPSFC/HRMSn) combined with a Kendrick mass defect-based classification model was applied to analyze the monomers and oligomers in the LMW fractions separated from BLR samples depolymerized at 170–210 °C. The most common phenolic compound types were dimers, followed by monomers. A second round of depolymerization yielded low amounts of monomers and dimers, while a high number of trimers were formed, thought to be the result of repolymerization.

scholarly journals Enhanced Convolutional-Neural-Network Architecture for Crop Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4292
Author(s):  
Mónica Y. Moreno-Revelo ◽  
Lorena Guachi-Guachi ◽  
Juan Bernardo Gómez-Mendoza ◽  
Javier Revelo-Fuelagán ◽  
Diego H. Peluffo-Ordóñez
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Classification Model ◽  
Classification Error ◽  
Small Scale ◽  
Post Processing ◽  
Average Accuracy ◽  
Processing Step ◽  
Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

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