A Sub-Nanosecond Time Interval Detection System Using FPGA Embedded I/O Resources

2010 ◽  
Vol 57 (2) ◽  
pp. 519-524 ◽  
Author(s):  
Louis Arpin ◽  
Mélanie Bergeron ◽  
Marc-André Tetrault ◽  
Roger Lecomte ◽  
Réjean Fontaine
Keyword(s):  
Detection System ◽  
Time Interval

169 BEHAVIORAL ASPECTS OF ESTRUS IN THE COMMON ELAND ANTELOPE, TAUROTRAGUS ORYX

2009 ◽  
Vol 21 (1) ◽  
pp. 183
Author(s):  
G. Wirtu ◽  
P. M. Pennington ◽  
C. E. Pope ◽  
R. A. MacLean ◽  
J. Mercado ◽  
...  
Keyword(s):  
Estrous Cycle ◽  
Captive Breeding ◽  
Detection System ◽  
Courtship Behavior ◽  
Animal Health ◽  
Reproductive Technologies ◽  
Time Of Day ◽  
Time Interval ◽  
Early Afternoon ◽  
Chi Square Analysis

Knowledge of the estrous cycle and behavior is important in managing captive breeding programs. However, such information is negligible in spiral-horned antelopes, including the eland. In the present study, conducted between September 2007 and March 2008, we sought to characterize mounting activity and courtship behavior in a group of eight adult eland females. Estrus was induced in groups of four females by treatment with 25 mg PGF2 ∝ (IM, Lutalyse®, Pharmacia and Upjohn Company, Kalamazoo, MI, USA) administered after 7 days of daily oral progestin (5 mL of 2.2% altrenogest, DPT Laboratories, San Antonio, TX, USA) or 11 days after an initial treatment of PGF2 ∝ (25 mg). The eight females received each treatment in a crossover design. Females were induced and monitored during the induced and subsequent natural estrus for 34 to 38 days. Observations for estrus behavior were initially assisted by using Estrotect™ heat detector patches (Rockyway, Inc., www.estrotect.com). Since January 2008, the HeatWatch® electronic heat detection system (CowChips, Denver, CO, USA) and an androgenized eland female were used to assist with heat detection. For androgenization, 8 pellets of Synovex-H® (Fort Dodge Animal Health, Fort Dodge, IA) comprising a total dose of 1600 mg testosterone propionate and 160 mg estradiol benzoate were implanted (SC) on the convex side of the ear. To confirm mounting activity detected by Estrotect™ or HeatWatch®, eland behavior was recorded continuously using an 8-channel real time DVR. Chi-square analysis was used to test the association between time of day (day or night) and the frequency of standing to be mounted. The cycle length (n = 6 females) was the time interval (d) between the first mounts at the induced and natural estrus. The time of mounts was determined for a total of 52 mounts. More than half of the mounts (n = 32 or 61.5%) occurred between 6 pm and 6 am (night); however, there was no association between the number of mounts and time of the day (p = 0.886). Analysis of mounts by quarters of the day showed that mounts were evenly distributed between early afternoon (noon to 6 pm: 29%), late afternoon (6 pm to midnight: 33%) and early morning (midnight to 6 am: 29%) but were less frequent during late morning, between 6 am and noon (10%) possibly due to interference by human activities. The number of mounts observed per female ranged from 1 to 20. Mounts lasted for 2 seconds or less. The duration of estrus ranged from 2.1 to 29.0 hours. Typical courtship behaviors included prolonged head butting, flehmen response and following the female in estrus before mounting. The average length of the estrous cycle, based on mounting activities, was 19.3 d (range: 17–24). Although further studies are required in herds with eland males, the present results suggest that estrus detection for captive breeding or application of reproductive technologies in the eland should be spread around different times of the day and night. We have also demonstrated that an electronic mount detection system can be applied to assist with determination of estrus in the eland.

scholarly journals Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 692
Author(s):  
Mohamed Sakkari ◽  
Abeer D. Algarni ◽  
Mourad Zaied
Keyword(s):  
New York ◽  
Social Network ◽  
Reference Model ◽  
Learning Algorithm ◽  
Detection System ◽  
Time Of Day ◽  
Time Interval ◽  
Entropy Model ◽  
The City ◽  
Abnormal Events

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

2022 ◽  
Vol 11 (3) ◽  
pp. 1-11
Author(s):  
Sudhakar Sengan ◽  
Osamah Ibrahim Khalaf ◽  
Vidya Sagar P. ◽  
Dilip Kumar Sharma ◽  
Arokia Jesu Prabhu L. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Attack Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Data Traffic ◽  
Probability Of False Alarm ◽  
Healthcare Data ◽  
Flooding Attacks ◽  
Machine Learning Approach

Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.

The Research and Development of High Precision Gas Flowmeter Detection System

2014 ◽  
Vol 529 ◽  
pp. 405-409
Author(s):  
Yi Lin Zhao ◽  
Jian Min Zhang ◽  
Ming Jiang
Keyword(s):  
Network Management ◽  
High Precision ◽  
Gas Flow ◽  
Performance Test ◽  
Detection System ◽  
Test System ◽  
Time Interval ◽  
Large Pressure ◽  
Interaction Interface ◽  
Interface Devices

The paper develops a high precision gas flow meter test system based on ARM technology and wireless network management on the basis of the original gas flow measurement equipment,comparise the value of the output fluid that continuously through standard flowmeter and detected flowmeter in the same time interval in order to determine the measured performance test flowmeter. At the same time, The system adopts industrial touch screen to achieve human-computer interaction interface,devices in the workshop share a large pressure device through the wireless transmission control; connect each device and server through the WiFi;structure Ethernet; and realize remote Internet network management.

scholarly journals Are there separate ON and OFF channels in fly motion vision?

1992 ◽  
Vol 8 (2) ◽  
pp. 151-164 ◽  
Author(s):  
Martin Egelhaaf ◽  
Alexander Borst
Keyword(s):  
Visual Field ◽  
Apparent Motion ◽  
Motion Detection ◽  
Visual Information ◽  
Detection System ◽  
Time Interval ◽  
Motion Vision ◽  
Response Component ◽  
Retinal Input ◽  
Motion Detection System

AbstractVisual information is processed in a series of subsequent steps. The performance of each of these steps depends not only on the computations it performs itself but also on the representation of the visual surround on which it operates. Here we investigate the consequences of signal preprocessing for the performance of the motion-detection system of the fly. In particular, we analyze whether the retinal input signals are rectified and segregate into separate ON and OFF channels, which then feed independent parallel motion-detection pathways. We recorded the activity of an identified directionally selective interneuron (HI-cell) in response to apparent motion stimuli, i.e. sequential brightness changes at two neighboring locations in the visual field, as well as to brightness changes at only a single location. For apparent motion stimuli, the motion-dependent response component was determined by subtracting from the overall response the responses to the individual stimulus components when presented alone. The following conclusions could be derived: (1) Apparent motion consisting of a sequence of increased or decreased brightness at two locations in the visual field have the same optimum interstimulus time interval (Fig. 3). (2) Sequences of brightness steps of like polarity (either increments or decrements) elicit positive and negative motion-dependent response components when mimicking motion in the cell's preferred and null direction, respectively. The motion-dependent response components are inverted in sign when the brightness steps of a stimulus sequence have a different polarity (Fig. 7). (3) The responses to the beginning and the end of a brightness pulse depend on the pulse duration. For pulse durations of less than 2 s, both events interact with each other (Fig. 9). All of these results do not provide any indication that the fly processes motion information in independent ON and OFF motion detectors. Brightness changes of both signs are rather represented at the input of the same movement detectors, and interactions between signals resulting from both brightness increments and decrements take their sign into account. This type of preprocessing of the retinal input is argued to render a motion-detection system particularly robust against noise.

scholarly journals EADetection: An efficient and accurate sequential behavior anomaly detection approach over data streams

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880330 ◽  
Author(s):  
Li Cheng ◽  
Yijie Wang ◽  
Yong Zhou ◽  
Xingkong Ma
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Detection Efficiency ◽  
Detection System ◽  
False Positive Rate ◽  
Time Interval ◽  
Sequential Behavior ◽  
Detection Approach ◽  
Anomaly Score

Due to the increasing arriving rate and complex relationship of behavior data streams, how to detect sequential behavior anomaly in an efficient and accurate manner has become an emerging challenge. However, most of the existing literature simply calculates the anomaly score for segmented sequence, and there is limited work going deep to investigate data stream segment and structural relationship. Moreover, existing studies cannot meet efficiency requirements because of large number of projected subsequences. In this article, we propose EADetection, an efficient and accurate sequential behavior anomaly detection approach over data streams. EADetection adopts time interval and fuzzy logic–based correlation to segment event stream adaptively based on rolling window. Through dynamic projection space–based fast pruning, large number of repeated patterns are reduced to improve detection efficiency. Meanwhile, EADetection calculates the anomaly score by top-k pattern–based abnormal scoring based on directed loop graph–based storage strategy, which ensures the accuracy of detection. Specially, we design and implement a streaming anomaly detection system based on EADetection to perform real-time detection. Extensive experiments confirm that EADetection can achieve real time and improve accuracy, significantly reduces latency by 36.8% and reduces false positive rate by 6.4% compared with existing approach.

scholarly journals Drowsiness Detection System

2020 ◽  
Vol 9 (4) ◽  
pp. 1341-1343
Keyword(s):  
Detection System ◽  
Current System ◽  
Working Hours ◽  
Time Interval ◽  
Work Time ◽  
Long Working Hours ◽  
Driver Drowsiness ◽  
Long Time ◽  
Eyes Open ◽  
Vast Network

Melbourne is one of the liveliest cities in the world. It has a well efficient transport system, supported by a vast network of trams. Therefore, the mental health and stress level of the tram drivers plays a crucial role in the safety of the passengers. The issue of fatigue and drowsiness in the tram drivers are mostly due to their work-time and the most common thing is that the drowsiness occurs during the work time itself. This drowsiness is a risk for everyone including those who are not travelling in the tram. The current system that is used to prevent the drivers from falling sleeping is called the deadlock system. In this system the driver keeps his foot on a pedal at all times. Whenever the driver lifts his foot from the pedal the tram stops moving. Considering the technologies that are currently implemented in the vehicles seems to be insufficient. More over the driver gets uncomfortable when he keeps his foot onto the lever for a long time during long working hours. We have used OpenCV in python to create a program which monitors the eyes of a person and ensures that they keep the eyes open. The developed algorithm uses python libraries to detect any abnormality in the time interval between blinks and the extent of openness of the driver’s eyes. When an abnormality is detected the driver receives an alarm on his phone indicating driver drowsiness.

scholarly journals First simultaneous detection of electron and positron bunches at the positron capture section of the SuperKEKB factory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tsuyoshi Suwada ◽  
Muhammad Abdul Rehman ◽  
Fusashi Miyahara
Keyword(s):  
Detection System ◽  
Simultaneous Detection ◽  
Axial Direction ◽  
Time Interval ◽  
Capture Process ◽  
Electron Intensity ◽  
Beam Parameters ◽  
Positron Capture ◽  
Positron Bunch ◽  
First Time

AbstractThe direct simultaneous detection of electron and positron bunch signals was successfully performed for the first time with wideband pickups and a detection system at the positron capture section of the SuperKEKB factory. The time interval between the electron and positron bunches, their bunch lengths, and bunch intensities depending on the phase of accelerating structures were measured to investigate their capture process and to maximally optimize the positron intensity. The results show that the time intervals were measured in the range of 135–265 ps, and the line-order switch of the electron and positron bunches in the axial direction was clearly observed as a function of the phase. The positron (electron) intensity was maximized at the optimal phase (180$$^{\circ }$$ ∘ shifted from the optimum). These series of measurements have never been experimentally conducted so far. It is demonstrated that the positron intensity can be systematically optimized with this system as functions of beam parameters in multidimensional spaces for any positron capture section.

scholarly journals First Simultaneous Detection of Electron and Positron Bunches at the Positron Capture Section of the SuperKEKB Factory

2021 ◽  
Author(s):  
Tsuyoshi Suwada ◽  
Muhammad Abdul Rehman ◽  
Fusashi Miyahara
Keyword(s):  
Detection System ◽  
Simultaneous Detection ◽  
Axial Direction ◽  
Time Interval ◽  
Capture Process ◽  
Electron Intensity ◽  
Beam Parameters ◽  
Positron Capture ◽  
Positron Bunch ◽  
First Time

Abstract The direct simultaneous detection of electron and positron bunch signals was successfully performed for the first time with wideband pickups and a detection system at the positron capture section of the SuperKEKB factory. The time interval between the electron and positron bunches, their bunch lengths, and bunch intensities depending on the phase of accelerating structures were measured to investigate their capture process and to maximally optimize the positron intensity. The results show that the time intervals were measured in the range of 135–265 ps, and the line-order switch of the electron and positron bunches in the axial direction was clearly observed as a function of the phase. The positron (electron) intensity was maximized at the optimal phase (180 deg shifted from the optimum). These series of measurements have never been experimentally conducted so far. It is demonstrated that the positron intensity can be systematically optimized with this system as functions of beam parameters in multidimensional spaces for any positron capture section.

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