Tag anti-collision algorithms in RFID systems - a new trend

RFID is a wireless communication technology that provides automatic identification or tracking and data collection from any tagged object. Due to the shared communication channel between the reader and the tags during the identification process in RFID systems, many tags may communicate with the reader at the same time, which causes collisions. The problem of tag collision has to be addressed to have fast multiple tag identification process. There are two main approaches to the tag collision problem: ALOHA based algorithms and tree based algorithms. Although these methods reduce the collision and solve the problem to some extent, they are not fast and efficient enough in real applications. A new trend emerged recently which takes the advantages of both ALOHA and tree based approaches. This paper describes the process and performance of the tag anti-collision algorithms of the tree-ALOHA trend

The effect of COVID-19 on the characteristics of adult emergency department visits:A retrospective cohort tertiary hospital experience in Riyadh

BACKGROUND: On March 2, 2020, Saudi Arabia identified the first positive COVID-19 case. Since then, several aspects of the COVID-19 impact on Emergency Departments (EDs) use have been reported. The objective of this study is to describe the pattern and characteristics of Emergency Department visits during the COVID-19 pandemic period, compared with the same period in the previous year, including the patients’ demographic information, acuity level, length of stay, and admission rate. METHODS: Data were collected from King Abdulaziz Medical City in Riyadh, Saudi Arabia. The health records of all the patients who presented at the Emergency Department from January 2019 to September 2020 were retrospectively reviewed. The variations in the patient and the visit characteristics were described for the periods before and during COVID-19. RESULTS: The records of 209,954 patients who presented at the Emergency Department were retrieved. In contrast to 2019, the number of visits during the pandemic period reduced by 23%. A dramatic decrease was observed after the announcement of the first COVID-19 diagnosed case in Saudi Arabia, and subsequently the numbers gradually increased. The patients who presented at the Emergency Department during the pandemic period were slightly older (mean age, 43.1 versus 44.0 years), more likely to be older, more urgent and had a higher admission rate compared to the pre-pandemic period. There was a slight increase in visits during the daytime curfew hours and a decrease during the nighttime. CONCLUSION: We report a considerable decrease in the number of Emergency Department visits. The reduction was higher in non-urgent and less urgent cases. Patients presenting at the Emergency Department during the curfew times were more likely to stay longer in the Emergency Department and more likely to be admitted, compared with the pre-pandemic period

Employee voice: An employee satisfaction level by selected healthcare service providers in the Czech Republic

The employee satisfaction level is closely related to the performance and quality of work performed by an employee and, consequently, it translates into the competitiveness and success of a corporation, because a motivated and satisfied employee builds and participates in the success of any corporation (organization, firm, hospital, etc.). The aim of the article was to discover more about the current situation employee satisfaction level by selected healthcare service providers in the context of the gender of employees and the length of current employment of the employees by selected healthcare service providers in the Czech Republic. The overall employee satisfaction level was monitored through seven selected research areas. The research was carried out in 2017 from the sample of 608 respondents. Two research hypotheses and one research question have been formulated. The verification or rejection of null research hypotheses was done through the statistical method of the Pearson's Chi-square test. The results came along with the discovery that there is a statistically significant relation between the overall employee satisfaction level by selected healthcare service providers and the gender of employees and there is no statistically significant relation between the overall employee satisfaction level by selected healthcare service providers and the length of current employment of the employees by selected healthcare service providers. © Academy of Management. All rights reserved

What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations.

Testing strategies is crucial for genetics clinics and testing laboratories. In this study, we tried to compare the hit rate between solo and trio and trio plus testing and between trio and sibship testing. Finally, we studied the impact of extended family analysis, mainly in complex and unsolved cases. Three cohorts were used for this analysis: one cohort to assess the hit rate between solo, trio and trio plus testing, another cohort to examine the impact of the testing strategy of sibship genome vs trio-based analysis, and a third cohort to test the impact of an extended family analysis of up to eight family members to lower the number of candidate variants. The hit rates in solo, trio and trio plus testing were 39, 40, and 41%, respectively. The total number of candidate variants in the sibship testing strategy was 117 variants compared to 59 variants in the trio-based analysis. We noticed that the average number of coding candidate variants in trio-based analysis was 1192 variants and 26,454 noncoding variants, and this number was lowered by 50-75% after adding additional family members, with up to two coding and 66 noncoding homozygous variants only, in families with eight family members. There was no difference in the hit rate between solo and extended family members. Trio-based analysis was a better approach than sibship testing, even in a consanguineous population. Finally, each additional family member helped to narrow down the number of variants by 50-75%. Our findings could help clinicians, researchers and testing laboratories select the most cost-effective and appropriate sequencing approach for their patients. Furthermore, using extended family analysis is a very useful tool for complex cases with novel genes

Multi-Criteria Decision Making to Detect Multiple Moving Targets in Radar Using Digital Codes

Technological advancement in battlefield and surveillance applications switch the radar investigators to put more effort into it, numerous theories and models have been proposed to improve the process of target detection in Doppler tolerant radar. However, still, more effort is needed towards the minimization of the noise below the radar threshold limit to accurately detect the target. In this paper, a digital coding technique is being discussed to mitigate the noise and to create clear windows for desired target detection. Moreover, multi-criteria of digital code combinations are developed using discrete mathematics and all designed codes have been tested to investigate various target detection properties such as the auto-correlation, cross-correlation properties, and ambiguity function using mat-lab to optimize and enhance the static and moving target in presence of the Doppler in a multi-target environment

Optimal Disease Diagnosis in Internet of Things (IoT) Based Healthcare System Using Energy Efficient Clustering

This paper aims to introduce a novel approach that includes three steps, namely Energy efficient clustering, Disease diagnosis, and an Alert system. Initially, energy-efficient clustering of nodes was conducted, and to render the clustering more optimal, its centroid was optimally selected by a new hybrid algorithm. In addition, this cluster formation was conducted based on constraints such as distance and energy. Further, the disease diagnosis in IoT was performed under two phases namely, “feature extraction and classification”. During feature extraction, the statistical and higher-order features were extracted. These extracted features were then classified via Optimized Deep Convolutional Neural Network (DCNN). To make the classification more precise, the weights of the DCNN were optimally tuned by a new hybrid algorithm referred to as Hybrid Elephant and Moth Flame with Adaptive Learning (HEM-AL). Finally, an alert system was enabled via proposed severity level estimation, which determined the severity of the disease, suggesting patients to visit the hospital. Lastly, the supremacy of the developed approach was examined via evaluation over the other extant techniques. Accordingly, the proposed model attained an accuracy of 0.99 for test case 1, and was 7.41%, 17.34%, and 13.41% better than traditional NN, CNN, and DCNN models

Security to wireless sensor networks against malicious attacks using Hamming residue method

Abstract Wireless sensor networks (WSNs) consist of small sensor nodes with limited energy. Such nodes have the ability to monitor the physical conditions and communicate information among the nodes without the requirement of the physical medium. WSNs are autonomous and are distributed in space. Due to the absence of central authority and random deployment of nodes in the network, WSN is prone to security threats. Well-known attacks in WSN are a malicious attack (such as compromised node imitating as one of the network nodes, misleading other nodes). In the art of work, various methods are developed to overcome these attacks either by cryptographic approaches or by time synchronization. But these methods may fail because of WSN autonomous structure. In this paper, an efficient approach called Hamming residue method (HRM) is presented to mitigate the malicious attacks. The experimental results validate the presented approach

Optimal Disease Diagnosis in Internet of Things (IoT) Based Healthcare System Using Energy Efficient Clustering

This paper aims to introduce a novel approach that includes three steps, namely Energy efficient clustering, Disease diagnosis, and an Alert system. Initially, energy-efficient clustering of nodes was conducted, and to render the clustering more optimal, its centroid was optimally selected by a new hybrid algorithm. In addition, this cluster formation was conducted based on constraints such as distance and energy. Further, the disease diagnosis in IoT was performed under two phases namely, “feature extraction and classification”. During feature extraction, the statistical and higher-order features were extracted. These extracted features were then classified via Optimized Deep Convolutional Neural Network (DCNN). To make the classification more precise, the weights of the DCNN were optimally tuned by a new hybrid algorithm referred to as Hybrid Elephant and Moth Flame with Adaptive Learning (HEM-AL). Finally, an alert system was enabled via proposed severity level estimation, which determined the severity of the disease, suggesting patients to visit the hospital. Lastly, the supremacy of the developed approach was examined via evaluation over the other extant techniques. Accordingly, the proposed model attained an accuracy of 0.99 for test case 1, and was 7.41%, 17.34%, and 13.41% better than traditional NN, CNN, and DCNN models

An enhanced method of estimating number of tags using signal strength properties in RFID systems

Radio frequency identification (RFID) is a wireless communication technology that provides automatic identification and data collection. In the application of RFID systems with large number of tags, many tags may respond to the reader in the same time. Thus, enhanced anti-collision algorithms are required to have fast multiple tag identification process. Thus, several anti-collision algorithms are developed mainly in two different areas: ALOHA based algorithm and tree based algorithm. These algorithms help to achieve better identification performance, by adjusting the frame size closer to the optimum size. There are many methods to select the appropriate frame size based on the number of collided slots, the collision ratio or the maximum throughput. In this paper a new method is introduced by using the characteristics of the received signal by the reader

A signal strength based tag estimation technique for RFID systems

Radio frequency identification (RFID) is a wireless communication technology that provides automatic identification and data collection. In RFID systems with a large number of tags, many tags respond to the reader at the same time. Thus, enhanced anti-collision algorithms are required to have a fast multiple tag identification process. Anti-collision algorithms can be classified as probabilistic (eg. ALOHA based) lgorithms and deterministic (tree based) algorithms. Better performance is achieved in all algorithms when accurate estimation of the number of tags is achieved. This paper presents a new method of estimating number of tags using signal strength properties. The simulated results show that our algorithm (efficiency 35%) significantly improves on existing estimation algorithms. That is 1% away from the perfect estimator and the theoretical optimum efficiency of slotted Aloha (36%).© 2010 IEEE