Livestock industry is daily producing large amounts of multi-scale data (pathogen-, animal-, site-, system-, regional- level) from different
sources such as diagnostic laboratories, trade and production records, management and environmental monitoring systems; however, all these
data are still presented and used separately and are largely infra-utilized to timely (i.e., near real-time) inform livestock health decisions.
Recent advances in the automation of data capture, standardization, multi-scale integration and sharing/communication (i.e. The Internet Of
Things) as well as in the development of novel data mining analytical and visualization capabilities specifically adapted to the livestock industry
are dramatically changing this paradigm. As a result, we expect vertical advances in the way we prevent and manage livestock diseases both
locally and globally. Our team at the Center for Animal Disease Modeling and Surveillance (CADMS), in collaboration with researchers at Iowa
State University and industry leaders at Boehringer Ingelheim and GlobalVetLINK have been working in an exceptional research-industry
partnership to develop key data connections and novel Big Data capabilities within the Disease BioPortal (http://bioportal.ucdavis.edu/). This
web-based platform includes automation of diagnostic interpretations and facilitates the combined analysis of health, production and trade data
using novel space-time-genomic visualization and data mining tools. Access to confidential databases is individually granted with different
levels of secure access, visualization and editing capabilities for participating producers, labs, veterinarians and other stakeholders. Each user
can create and share customized dashboards and reports to inform risk-based, more cost-effective, decisions at site, system or regional level.
Here we will provide practical examples of applications in the swine, poultry and aquaculture industries. We hope to contribute to the more
coordinated and effective prevention and control of infectious diseases locally and globally.
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Situational Strategic Awareness Monitoring Surveillance System—Microcomputer and Microcomputer Clustering used for Intelligent, Economical, Scalable, and Deployable Approach for Safeguarding Materials
The principal objective of this research is to create a system that is quickly deployable, scalable, adaptable, and intelligent and provides cost-effective surveillance, both locally and globally. The intelligent surveillance system should be capable of rapid implementation to track (monitor) sensitive materials, i.e., radioactive or weapons stockpiles and person(s) within rooms, buildings, and/or areas in order to predict potential incidents proactively (versus reactively) through intelligence, locally and globally. The system will incorporate a combination of electronic systems that include commercial and modifiable off-the-shelf microcomputers to create a microcomputer cluster which acts as a mini supercomputer which leverages real-time data feed if a potential threat is present. Through programming, software, and intelligence (artificial intelligence, machine learning, and neural networks), the system should be capable of monitoring, tracking, and warning (communicating) the system observer operations (command and control) within a few minutes when sensitive materials are at potential risk for loss. The potential customer is government agencies looking to control sensitive materials and/or items in developing world markets intelligently, economically, and quickly.
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- Award ID(s):
- 1842577
- NSF-PAR ID:
- 10185299
- Date Published:
- Journal Name:
- The Journal of imaging science and technology
- Volume:
- 63
- Issue:
- 6
- ISSN:
- 1062-3701
- Page Range / eLocation ID:
- 060408-1–060408-10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.
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Abstract The insect circulatory system contains an open hemocoel, in which the mechanism of hemolymph flow control is ambiguous. As a continuous fluidic structure, this cavity should exhibit pressure changes that propagate quickly. Narrow-waisted insects create sustained pressure differences across segments, but their constricted waist provides an evident mechanism for compartmentalization. Insects with no obvious constrictions between segments may be capable of functionally compartmentalizing the body, which could explain complex hemolymph flows. Here, we test the hypothesis of functional compartmentalization by measuring pressures in a beetle and recording abdominal movements. We found that the pressure is indeed uniform within the abdomen and thorax, congruent with the predicted behavior of an open system. However, during some abdominal movements, pressures were on average 62% higher in the abdomen than in the thorax, suggesting that functional compartmentalization creates a gradient within the hemocoel. Synchrotron tomography and dissection show that the arthrodial membrane and thoracic muscles may contribute to this dynamic pressurization. Analysis of volume change suggests that the gut may play an important role in regulating pressure by translating between body segments. Overall, this study suggests that functional compartmentalization may provide an explanation for how fluid flows are managed in an open circulatory system.
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Social Distancing has proved a necessary measure in con- trolling the spread of Coronavirus. The CDC (Center for dis- ease control and prevention) in the United States recommends 6 feet as a safe distance between individuals. Therefore, a surveillance system capable of measuring distances between individuals can prove beneficial in limiting the spread. Video surveillance systems have already been introduced in various fields of our daily life to enhance security and protect individuals and sensitive infrastructure. In this work we present a way to use the existing video surveillance infrastructure to measure and monitor social distancing. We present a practical approach to monitor this distance using artificial intelligence and projective geometry techniques. Our approach, after initial setup, works in real-time requiring only a monocular surveillance camera feed. The proposed approach utilizes YOLO v4 neural network object detector for detecting pedestrians in the camera’s view. Projective transformation is used to localize the pedestrians on the ground. Finally, the real world distances between pedestrians is calculated and visualized with the right perspective and occlusion relations as if the distance marks are actually on the ground. All the implementation is in real-time, and is performed on python using the OPENCV libraries and the YOLO v4 neural net with pre- trained weights. Experimental results are provided to validate our approach. The code of this work will be made publicly available at GitHub upon acceptance.more » « less
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Parkinson’s disease (PD) is a neurological progressive movement disorder, affecting more than 10 million people globally. PD demands a longitudinal assessment of symptoms to monitor the disease progression and manage the treatments. Existing assessment methods require patients with PD (PwPD) to visit a clinic every 3–6 months to perform movement assessments conducted by trained clinicians. However, periodic visits pose barriers as PwPDs have limited mobility, and healthcare cost increases. Hence, there is a strong demand for using telemedicine technologies for assessing PwPDs in remote settings. In this work, we present an in-home telemedicine kit, named iTex (intelligent Textile), which is a patient-centered design to carry out accessible tele-assessments of movement symptoms in people with PD. iTex is composed of a pair of smart textile gloves connected to a customized embedded tablet. iTex gloves are integrated with flex sensors on the fingers and inertial measurement unit (IMU) and have an onboard microcontroller unit with IoT (Internet of Things) capabilities including data storage and wireless communication. The gloves acquire the sensor data wirelessly to monitor various hand movements such as finger tapping, hand opening and closing, and other movement tasks. The gloves are connected to a customized tablet computer acting as an IoT device, configured to host a wireless access point, and host an MQTT broker and a time-series database server. The tablet also employs a patient-centered interface to guide PwPDs through the movement exam protocol. The system was deployed in four PwPDs who used iTex at home independently for a week. They performed the test independently before and after medication intake. Later, we performed data analysis of the in-home study and created a feature set. The study findings reported that the iTex gloves were capable to collect movement-related data and distinguish between pre-medication and post-medication cases in a majority of the participants. The IoT infrastructure demonstrated robust performance in home settings and offered minimum barriers for the assessment exams and the data communication with a remote server. In the post-study survey, all four participants expressed that the system was easy to use and poses a minimum barrier to performing the test independently. The present findings indicate that the iTex glove system has the potential for periodic and objective assessment of PD motor symptoms in remote settings.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2352/J.ImagingSci.Technol.2019.63.6.060408
