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"Identification systems"
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Capturing Daily Disease Experiences of Adolescents With Chronic Pain: mHealth-Mediated Symptom Tracking
Chronic pain is a common problem in adolescents that can negatively impact all aspects of their health-related quality of life. The developmental period of adolescence represents a critical window of opportunity to optimize and solidify positive health behaviors and minimize future pain-related disability and impaired work productivity. This research focuses on the development and evaluation of a smartphone-based pain self-management app for adolescents with chronic pain.
The objectives of this study were to characterize (1) the feasibility of deploying a mobile health (mHealth) app (iCanCope) to the personal smartphones of adolescent research participants; (2) adherence to daily symptom tracking over 55 consecutive days; (3) participant interaction with their symptom history; and (4) daily pain-related experiences of adolescents with chronic pain.
We recruited adolescents aged 15-18 years from 3 Canadian pediatric tertiary care chronic pain clinics. Participants received standardized instructions to download the iCanCope app and use it once a day for 55 days. Detailed app analytics were captured at the user level. Adherence was operationally defined as per the relative proportion of completed symptom reports. Linear mixed models were used to examine the trajectories of daily symptom reporting.
We recruited 60 participants between March 2017 and April 2018. The mean age of the participants was 16.4 (SD 0.9) years, and 88% (53/60) of them were female. The app was deployed to 98% (59/60) devices. Among the 59 participants, adherence was as follows: low (4, 7%), low-moderate (14, 24%), high-moderate (16, 27%), and high (25, 42%). Most (49/59, 83%) participants chose to view their historical symptom trends. Participants reported pain intensity and pain-related symptoms of moderate severity, and these ratings tended to be stable over time.
This study indicates that (1) the iCanCope app can be deployed to adolescents' personal smartphones with high feasibility; (2) adolescents demonstrated moderate-to-high adherence over 55 days; (3) most participants chose to view their symptom history; and (4) adolescents with chronic pain experience stable symptomology of moderate severity.
ClinicalTrials.gov NCT02601755; https://clinicaltrials.gov/ct2/show/NCT02601755 (Archived by WebCite at http://www.webcitation.org/74F4SLnmc).
Journal Article
RF in RFID - Passive UHF RFID in Practice
This book includes a survey of all RFID fundamentals and practices in the first part of the book while the second part focuses on UHF passive technology. This coverage of UHF technology and its components including tags, readers, and antennas is essential to commercial implementation in supply chain logistics and security. Readers of this book should have an electrical engineering background, but have not yet dealt with RFID. To this end, the author is very careful to illustrate all concepts and detail his explanations meticulously. In this way, he will bring the reader along organically showing him/her what to expect, develop, and use while implementing an RFID system.
eBook
AI-enhanced real-time cattle identification system through tracking across various environments
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them
in real
time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system
Journal Article
Longer is Not Always Better
New techniques for the species-level sorting of millions of specimens are needed in order to accelerate species discovery,determine howmany species live on earth, and develop efficient biomonitoring techniques. These sorting methods should be reliable, scalable, and cost-effective, as well as being largely insensitive to low-quality genomic DNA, given that this is usually all that can be obtained from museum specimens. Mini-barcodes seem to satisfy these criteria, but it is unclear how well they perform for species-level sorting when compared with full-length barcodes. This is here tested based on 20 empirical data sets covering ca. 30,000 specimens (5500 species) and six clade-specific data sets from GenBank covering ca. 98,000 specimens (20,000 species). All specimens in these data sets had full-length barcodes and had been sorted to species-level based on morphology. Mini-barcodes of different lengths and positions were obtained in silico from full-length barcodes using a sliding window approach (three windows: 100 bp, 200 bp, and 300 bp) and by excising nine mini-barcodes with established primers (length: 94–407 bp).We then tested whether barcode length and/or position reduces species-level congruence between morphospecies and molecular operational taxonomic units (mOTUs) that were obtained using three different species delimitation techniques (Poisson Tree Process,Automatic Barcode Gap Discovery, and Objective Clustering). Surprisingly,we find no significant differences in performance for both species- or specimen-level identification between full-length and mini-barcodes as long as they are of moderate length (200 bp). Only very short mini-barcodes (200 bp) perform poorly, especially when they are located near the 5 end of the Folmer region. The mean congruence between morphospecies and mOTUs was ca. 75% for barcodes >200 bp and the congruent mOTUs contain ca. 75% of all specimens. Most conflict is caused by ca. 10% of the specimens that can be identified and should be targeted for reexamination in order to efficiently resolve conflict. Our study suggests that large-scale species discovery, identification, and metabarcoding can utilize mini-barcodes without any demonstrable loss of information compared to full-length barcodes.
Journal Article
Based Medical Systems for Patient’s Authentication: Towards a New Verification Secure Framework Using CIA Standard
In medical systems for patient’s authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient’s authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.
Journal Article
Detection of Cattle Using Drones and Convolutional Neural Networks
Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.
Journal Article