DropMonitor

2021 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Yuancan Lin ◽  
Lei Xie ◽  
Chuyu Wang ◽  
Yanling Bu ◽  
Sanglu Lu
Keyword(s):  
Human Activities ◽  
Indoor Environment ◽  
Liquid Surface ◽  
State Of The Art ◽  
Rfid Tags ◽  
Prototype System ◽  
Hardware Cost ◽  
Important Indicator ◽  
Rf Signals ◽  
The Difference

As an important indicator of the infusion monitoring for clinical treatment, the drip rate is expected to be monitored in an accurate and real-time manner. However, state-of-the-art drip rate monitoring schemes either suffer from high maintenance or incur high hardware cost. In this paper, we propose DropMonitor, an RFID-based approach to perform the mm-level sensing for infusion drip rate monitoring. By attaching a pair of batteryless RFID tags on the drip chamber, we can estimate the drip rate by capturing the RF-signals reflected from the vibrating liquid surface caused by the falling droplets. Particularly, we use the sensing tag to perceive the liquid surface vibration in the drip chamber and further derive the drip rate for infusion monitoring. Moreover, to sufficiently mitigate the multi-path interference from the surrounding human activities, we use the reference tag to perceive the multi-path signals from the indoor environment. By computing the difference of RF-signals from tag pairs, we cancel the multi-path interference and extract the drip-rate-related signals. We have implemented a prototype system and evaluated its performance in real applications. The experiment results show that DropMonitor can accurately estimate the infusion drip rate, and the average relative error of drip rate estimation is below 1% for conventional cases. In this way, considering the essential sampling rates of each tag, DropMonitor is able to monitor the drip rate for over a dozen of infusion bottles/bags in parallel with one COTS RFID system.

scholarly journals Smoke recognition network based on dynamic characteristics

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092566
Author(s):  
Dahan Wang ◽  
Sheng Luo ◽  
Li Zhao ◽  
Xiaoming Pan ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
State Of The Art ◽  
The State ◽  
Detection Accuracy ◽  
Static Characteristics ◽  
Good Tool ◽  
Early Signal ◽  
Fuzzy Objects ◽  
The Difference ◽  
Smoke Recognition

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

Critical Technical Issues in High Voltage SiC Power Devices

2008 ◽  
Vol 600-603 ◽  
pp. 895-900 ◽  
Author(s):  
Anant K. Agarwal ◽  
Albert A. Burk ◽  
Robert Callanan ◽  
Craig Capell ◽  
Mrinal K. Das ◽  
...  
Keyword(s):  
Threshold Voltage ◽  
Carrier Mobility ◽  
Negative Charge ◽  
State Of The Art ◽  
Power Devices ◽  
Power Mosfets ◽  
Large Numbers ◽  
Technical Issues ◽  
The Difference ◽  
Difficult Issue

In this paper, we review the state of the art of SiC switches and the technical issues which remain. Specifically, we will review the progress and remaining challenges associated with SiC power MOSFETs and BJTs. The most difficult issue when fabricating MOSFETs has been an excessive variation in threshold voltage from batch to batch. This difficulty arises due to the fact that the threshold voltage is determined by the difference between two large numbers, namely, a large fixed oxide charge and a large negative charge in the interface traps. There may also be some significant charge captured in the bulk traps in SiC and SiO2. The effect of recombination-induced stacking faults (SFs) on majority carrier mobility has been confirmed with 10 kV Merged PN Schottky (MPS) diodes and MOSFETs. The same SFs have been found to be responsible for degradation of BJTs.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

scholarly journals A social trust and preference segmentation-based matrix factorization recommendation algorithm

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Peng ◽  
Baogui Xin
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
User Preference ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Commercial Activities ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

scholarly journals Comparative analysis of sports consumption habits in Hungary, Poland and Germany

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Dávid Paár ◽  
Antal Kovács ◽  
Miklós Stocker ◽  
Márk Hoffbauer ◽  
Attila Fazekas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Material Wealth ◽  
Important Indicator ◽  
Sports Clubs ◽  
Research Findings ◽  
Study Population ◽  
Sports Consumption ◽  
The Difference ◽  
Sports Venues ◽  
The Relationship

Abstract Background The so-called sports consumption models are looking for the factors that influence the sports spending of households. This paper aims to examine the Hungarian, Polish and German households’ sports expenditures which can be an important indicator of physical activity and sporty lifestyle. Methods Surveying of households in three countries (Hungary, Poland and Germany) has been conducted with a self-designed questionnaire. We have used descriptive and bivariate non-parametric and parametric statistical methods: (1) χ2 test, Mann-Whitney test and Kruskal-Wallis test for checking the relationship between sociodemographic and physical activity variables and (2) independent sample t-test and ANOVA for checking the differences in sports expenditures. Results Our research concluded that men, especially previous athletes, exercise more than women and those who have no history as registered athletes. The choice of sports venues is obviously different between the countries in the sample. Members of the study population spend the most on sports services while they spend the least on sports equipment. German households have the highest spending rates compared to the other two countries. Conclusions Results are in line with our previous research findings and with other literatures. The difference in preferences of sports venues could have the reason of different supply of sports clubs or the different living standards too. It needs further researches to clear it. Material wealth, income level and sport socialisation can be a determining factor regarding the level of sports spending.

scholarly journals RBP: a website fingerprinting obfuscation method against intelligent fingerprinting attacks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tao Luo ◽  
LiangMin Wang ◽  
ShangNan Yin ◽  
Hao Shentu ◽  
Hui Zhao
Keyword(s):  
Data Security ◽  
Arrival Time ◽  
State Of The Art ◽  
Web Security ◽  
Security And Privacy ◽  
Low Delay ◽  
Defense Technology ◽  
The Difference ◽  
Bandwidth Overhead ◽  
Traffic Analysis Attack

AbstractEdge computing has developed rapidly in recent years due to its advantages of low bandwidth overhead and low delay, but it also brings challenges in data security and privacy. Website fingerprinting (WF) is a passive traffic analysis attack that threatens website privacy which poses a great threat to user’s privacy and web security. It collects network packets generated while a user accesses website, and then uses a series of techniques to discover patterns of network packets to infer the type of website user accesses. Many anonymous networks such as Tor can meet the need of hide identity from users in network activities, but they are also threatened by WF attacks. In this paper, we propose a website fingerprinting obfuscation method against intelligent fingerprinting attacks, called Random Bidirectional Padding (RBP). It is a novel website fingerprinting defense technology based on time sampling and random bidirectional packets padding, which can covert the real packets distribution to destroy the Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between the datasets with random bidirectional virtual packets padding. We evaluate the defense against state-of-the-art website fingerprinting attacks in real scenarios, and show its effectiveness.

Recovery of forensic traces with use of state-of-the-art. imaging techniques – system for marking, tracing and maintaining chain of custody

2018 ◽  
Vol 299 ◽  
pp. 52-56
Author(s):  
Adam Frankowski ◽  
◽  
Artur Dębski ◽  
Keyword(s):  
State Of The Art ◽  
Imaging Techniques ◽  
Crime Scene ◽  
Rfid Tags ◽  
Chain Of Custody

The article describes the possibility of using modern techniques for crime scene imaging and a perspective of creating a system for denominating, tracing and maintaining the chain of custody of evidence. In particular, it presents the possibility of reproducing a crime scene based on recorded images and measurements performed with use of special markings. The Authors describe the chain of custody over the evidence and automation of procedures thanks to use of RFiD tags.

A Reduced Fuel Cell Stack Model for Control and Fault Diagnosis

2006 ◽  
Vol 3 (4) ◽  
pp. 384-388 ◽  
Author(s):  
Damiano Di Penta ◽  
Karim Bencherif ◽  
Michel Sorine ◽  
Qinghua Zhang
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Gas Adsorption ◽  
Carbon Monoxide Poisoning ◽  
Important Indicator ◽  
Fuel Cell Stack ◽  
Electrode Potentials ◽  
The Difference ◽  
Adsorption Desorption ◽  
Stack Model

This paper proposes a reduced fuel cell stack model for control and fault diagnosis which was validated with experimental data. Firstly, the electro-chemical phenomena are modeled based on a mechanism of gas adsorption/desorption on catalysts at the anode and at the cathode of the stack, including activation, diffusion, and carbon monoxide poisoning. The electrical voltage of a stack cell is then modeled by the difference between the two electrode potentials. A simplified thermal model of the fuel cell stack is also developed in order to take into account heat generation from reactions, heat transfers, and evaporation/condensation of water. Finally, the efficiency ratio is computed as a model output. It is used to evaluate the efficiency changes of the entire system, providing an important indicator for fault detection.

scholarly journals Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1539
Author(s):  
Kai Kwong Hon ◽  
Pak Wai Chan
Keyword(s):  
Detection Efficiency ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Doppler Lidar ◽  
Hit Rate ◽  
Glide Path ◽  
The World ◽  
Alerting System ◽  
The Difference ◽  
Hong Kong International Airport

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

scholarly journals An Experimental Analysis of Model Compression Techniques for Object Detection

2020 ◽  
Author(s):  
Andrey De Aguiar Salvi ◽  
Rodrigo Coelho Barros
Keyword(s):  
Object Detection ◽  
Experimental Analysis ◽  
State Of The Art ◽  
Neural Architecture ◽  
Model Compression ◽  
Processing Power ◽  
Benchmark Datasets ◽  
The Difference ◽  
And Performance ◽  
Consumption Constraints

Recent research on Convolutional Neural Networks focuses on how to create models with a reduced number of parameters and a smaller storage size while keeping the model’s ability to perform its task, allowing the use of the best CNN for automating tasks in limited devices, with reduced processing power, memory, or energy consumption constraints. There are many different approaches in the literature: removing parameters, reduction of the floating-point precision, creating smaller models that mimic larger models, neural architecture search (NAS), etc. With all those possibilities, it is challenging to say which approach provides a better trade-off between model reduction and performance, due to the difference between the approaches, their respective models, the benchmark datasets, or variations in training details. Therefore, this article contributes to the literature by comparing three state-of-the-art model compression approaches to reduce a well-known convolutional approach for object detection, namely YOLOv3. Our experimental analysis shows that it is possible to create a reduced version of YOLOv3 with 90% fewer parameters and still outperform the original model by pruning parameters. We also create models that require only 0.43% of the original model’s inference effort.

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