scholarly journals A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Mengwei Liu ◽  
Yujia Zhang ◽  
Jiachuang Wang ◽  
Nan Qin ◽  
Heng Yang ◽  
...  
Keyword(s):  
Object Recognition ◽  
High Performance ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Fire Department ◽  
Test Site ◽  
Neural System ◽  
Visual Input ◽  
Human Beings ◽  
Sensing System

AbstractObject recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.

scholarly journals Research on Sports Training Action Recognition Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Wang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Human Motion ◽  
Medical Monitoring ◽  
Human Beings ◽  
Sports Training ◽  
Motion Recognition ◽  
Human Motion Recognition

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

NEURAL NETWORK FEATURE DETECTOR FOR REAL-TIME VIDEO SIGNAL PROCESSING

1993 ◽  
Vol 04 (04) ◽  
pp. 337-349
Author(s):  
DAVID NAYLOR ◽  
SIMON JONES ◽  
DAVID MYERS ◽  
JOHN VINCENT
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Performance ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Neural System ◽  
Neural Learning ◽  
Network Feature ◽  
On Chip ◽  
Time Image

The application of artificial neural networks to real-time image processing tasks requires the use of dedicated, high performance hardware. A linear array processor called HANNIBAL has been developed which implements the backpropagation neural learning algorithm on-chip. This paper considers the design of a complete neural system which integrates HANNIBAL into an existing image processing environment. The goals for the design of the system have been set partly by the primary application, namely feature recognition, but mainly by the desire for a flexible, high performance hardware tool for the study and evaluation of range of neural image processing applications.

Surface/interface nanoengineering for rechargeable Zn–air batteries

2020 ◽  
Vol 13 (4) ◽  
pp. 1132-1153 ◽  
Author(s):  
Tianpei Zhou ◽  
Nan Zhang ◽  
Changzheng Wu ◽  
Yi Xie
Keyword(s):  
High Performance ◽  
Rapid Development

Surface/interface nanoengineering of electrocatalysts and air electrodes will promote the rapid development of high-performance rechargeable Zn–air batteries.

High Performance Thin Layer Chromatography (HPTLC) for the Investigation of Medicinal Plants

2020 ◽  
Vol 16 ◽  
Author(s):  
Alper Gökbulut
Keyword(s):  
Medicinal Plants ◽  
High Performance ◽  
Separation Efficiency ◽  
Rapid Development ◽  
Herbal Remedies ◽  
Qualitative And Quantitative ◽  
Powerful Analytical Tool ◽  
Quantitative Examination ◽  
Data Acquisition And Processing ◽  
Layer Chromatography

Background: Chromatographic techniques such as TLC basically and, HPLC, GC, HPTLC equipped with various detectors are most frequently used for the qualitative and quantitative examination of herbals. Method: An overview of the recent literature concerning the usage of HPTLC for the analysis of medicinal plants has been reviewed. Results: During the last decade/s, HPTLC, a modern, sophisticated and automatized TLC technique with better and advanced separation efficiency, detection limit, data acquisition and processing, has been used for the analysis of herbal materials and preparations since the rapid development of technology in chromatography world. HPTLC with various detectors is a powerful analytical tool especially for the phytochemical applications such as herbal drug quantification and fingerprint analysis. Conclusion: In this review, a latest perspective has been established and some of the previous studies were summarized for the usage of HPTLC in the analysis of herbal remedies, dietary supplements and nutraceuticals.

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 A RF Redundant TSV Interconnection for High Resistance Si Interposer

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 169
Author(s):  
Mengcheng Wang ◽  
Shenglin Ma ◽  
Yufeng Jin ◽  
Wei Wang ◽  
Jing Chen ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
High Frequency ◽  
High Performance ◽  
Rapid Development ◽  
Coplanar Waveguide ◽  
Equivalent Circuit Model ◽  
High Resistivity ◽  
Performance Requirements ◽  
Wave Radar ◽  
Interconnect Design

Through Silicon Via (TSV) technology is capable meeting effective, compact, high density, high integration, and high-performance requirements. In high-frequency applications, with the rapid development of 5G and millimeter-wave radar, the TSV interposer will become a competitive choice for radio frequency system-in-package (RF SIP) substrates. This paper presents a redundant TSV interconnect design for high resistivity Si interposers for millimeter-wave applications. To verify its feasibility, a set of test structures capable of working at millimeter waves are designed, which are composed of three pieces of CPW (coplanar waveguide) lines connected by single TSV, dual redundant TSV, and quad redundant TSV interconnects. First, HFSS software is used for modeling and simulation, then, a modified equivalent circuit model is established to analysis the effect of the redundant TSVs on the high-frequency transmission performance to solidify the HFSS based simulation. At the same time, a failure simulation was carried out and results prove that redundant TSV can still work normally at 44 GHz frequency when failure occurs. Using the developed TSV process, the sample is then fabricated and tested. Using L-2L de-embedding method to extract S-parameters of the TSV interconnection. The insertion loss of dual and quad redundant TSVs are 0.19 dB and 0.46 dB at 40 GHz, respectively.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Ischemic Stroke Prediction by Exploring Sleep Related Features

2021 ◽  
Vol 11 (5) ◽  
pp. 2083
Author(s):  
Jia Xie ◽  
Zhu Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xingshe Zhou
Keyword(s):  
Ischemic Stroke ◽  
Life Quality ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Neural System ◽  
Human Beings ◽  
Filter Model ◽  
Fine Grained ◽  
Proposed Model ◽  
Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

scholarly journals End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

2021 ◽  
Author(s):  
Baiyu Peng ◽  
Qi Sun ◽  
Shengbo Eben Li ◽  
Dongsuk Kum ◽  
Yuming Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
State Space ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Saliency Map ◽  
Hierarchical Architecture ◽  
Link Type ◽  
The Neural Network ◽  
End To End

AbstractRecent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture. The hierarchical architecture is always complicated and hard to design, while the end-to-end architecture is more promising due to its simple structure. This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving by itself. This paper firstly proposes an architecture for the end-to-end lane-keeping task. Unlike the traditional image-only state space, the presented state space is composed of both camera images and vehicle motion information. Then corresponding dueling neural network structure is introduced, which reduces the variance and improves sampling efficiency. Thirdly, the proposed method is applied to The Open Racing Car Simulator (TORCS) to demonstrate its great performance, where it surpasses human drivers. Finally, the saliency map of the neural network is visualized, which indicates the trained network drives by observing the lane lines. A video for the presented work is available online, https://youtu.be/76ciJmIHMD8 or https://v.youku.com/v_show/id_XNDM4ODc0MTM4NA==.html.

Constructing Three-dimensional Nano-crystalline Diamond Network within Polymer Composites for Enhanced Thermal Conductivity

Nanoscale ◽  
2021 ◽  
Author(s):  
Shaoyang Xiong ◽  
Yue Qin ◽  
Linhong Li ◽  
Guoyong Yang ◽  
Maohua Li ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Polymer Composites ◽  
Thermal Performance ◽  
Thermal Transport ◽  
High Performance ◽  
Rapid Development ◽  
Electronic Devices ◽  
Three Dimensional ◽  
Nano Crystalline ◽  
Enhanced Thermal Conductivity

In order to meet the requirement of thermal performance with the rapid development of high-performance electronic devices, constructing a three-dimensional thermal transport skeleton is an effective method for enhancing thermal...

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