Data Extraction Rate Research Articles

A screen-shooting resilient data-hiding algorithm based on two-level singular value decomposition

This paper presents a screen-shooting resilient data-hiding scheme for JPEG images based on a two-level Singular Value Decomposition (SVD). The mid-to-high frequency coefficients are chosen to balance the data-embedding robustness and the image's visual quality. At the same time, the two-level singular value decomposition (SVD) is additionally utilized to construct the feature matrices, thereby enhancing the robustness of the embedded message. The combination of DCT and two-level SVD enables achieving a high-performance screen-shooting resilient data-hiding algorithm. Moreover, the affine transformation is employed to rectify the distortion that caused by screen-shooting, thus further improving the data extraction accuracy. Experimental results demonstrate a minimum 3 % improvement in confidential information extraction accuracy, with the data extraction rate exceeding 92 % across screen-shooting angles ranging from −40 to 40°. The performance of the proposed algorithm outperforms other state-of-the-art schemes apparently.

Protocol of a scoping review of systematic reviews and meta-analyses about COVID-19 vaccines and associated adverse events from vaccination.

The continuous dissemination of coronavirus disease of 2019 (COVID-19) literature can inform decision-makers and the public. Since the widespread use of COVID-19 vaccines, more systematic reviews have summarized the effectiveness and reported adverse events associated with vaccination. Previous systematic and scoping reviews on COVID-19 summarized various aspects surrounding COVID-19, however, a scoping review is needed to summarize the characteristics of COVID-19 vaccines and associated adverse events reported in systematic reviews and meta-analyses to provide comprehensive evidence for informed medical decision-making. We will conduct a scoping review concerning COVID-19 vaccines and adverse events from vaccines. We will search from December 2019 to present in Epistemonikos, Campbell Library, CINAHL (Ovid), MEDLINE (Ovid), Scopus, CENTRAL (Ovid), Web of Science, WHO COVID-19 database, Joanna Briggs Institute of Excellence, and COVID-19 Evidence Reviews resource. We will include systematic reviews, meta-analyses, or both of randomized controlled trials and observational studies and exclude individual randomized controlled trials and observational studies. Abstracts and full-texts will be screened prior to selection. Investigators will independently use a calibrated quantitative and qualitative data extraction sheet and rate the quality of articles with AMSTAR, resolving disagreements to aim for good agreement (≥80%). An updated scoping review of the characteristics and safety of COVID-19 vaccines would highlight the accuracy of the evidence to inform decision-making concerning COVID-19 vaccination.

Joint load-balancing and power control strategy to maximize the data extraction rate of LoRaWAN networks

LPWAN enabled networks have a dizzying growth and continue to meet an essential need in the Internet of Things market due to their ability to provide low-cost wireless access to geographically spread-out devices. Consequently, an efficient allocation of wireless resources in order to support numerous devices becomes a major concern. In this paper, we propose an SF assignment approach in LoRa networks, paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the end-devices on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations, taking into account the capture effect and the non-orthogonality of SFs. Furthermore, we extended the solution by a transmission power control strategy for overall system energy-efficiency. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model, which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of Date Extraction Rate (DER) while guaranteeing good scalability on the network size and density.

LoRa-Based IoT Network Assessment in Rural and Urban Scenarios.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

Diagnostic performance of angiography-derived index of microvascular resistance: a systematic review and pooled meta-analysis

Abstract Background The index of microvascular resistance (IMR) is an established measurement of coronary microcirculation status. However, it has not been widely incorporated into routine practice due to need for intracoronary instrumentation (pressure wire) and hyperaemic agents. Several angiography-derived quantitative flow ratio-based indexes of microvascular resistance (angio-IMR) have been proposed rekindling the interest for the assessment and management of microvascular disease. Purpose To review the overall diagnostic accuracy of angio-IMR against wire based IMR. Methods A systematic review of the literature was performed and studies comparing angio-IMR with wire based IMR were included. Individual data was extracted using semi-automatic digitalization. Correlation of angio-IMR with IMR and its diagnostic performance against IMR were analysed. Results Six studies directly comparing angio-IMR with IMR were included. Data extraction rate was 85.1% (582/684 vessels). There was a linear correlation between angio-IMR and IMR (β=0.483, R square=0.298) (Figure 1A). Pooled sensitivity was 77%, specificity was 66%, positive predictive value was 65%, negative predictive value was 78%, and accuracy was 71.0%. Pooled area under receiver operator curve of angio-IMR for predicting IMR diagnosed coronary microvascular disease was 0.754 (95% confidential interval 0.715 to 0.793) (Figure 1B). Similar diagnostic performance was observed in subgroups of patients with or without ST-segment elevation myocardial infarction. Conclusions Currently available angio-IMR showed a clearly useful discrimination and diagnostic performance against the standard of wire based IMR. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): China Scholarship Council

Adding Redundancy to LoRaWAN for Emergency Communications at the Factory Floor

Wireless communications may be the key solution to satisfy mobility, flexibility, and scalability requirements of the Internet of Things. Particularly, the low power wide area networks, operating in unlicensed bands, are promising because they permit both private and public backends. Unfortunately, their target applications are situations where sporadic transmissions are tolerated. They could not fit scenarios in which high reliability and short round trip time are required. In this article, the LoRaWAN solution is considered for managing emergency and alarm communications. Reliability and timeliness limits have been overcome by jointly using a message replication scheme with variable payload length together with low-cost repeaters. Experiments have been carried out in both fixed and low-speed mobility scenarios (with an interfering traffic of 95 message/h, generated by regular LoRaWAN colocated network). Results, in the former case, show that the success probability of confirmed messages is 100%, the 99.6% of the available data are delivered to the backend, and the average delay decreases by 150 ms. When mobility is considered (e.g., for an automatic guided vehicle use case) results confirm significant improvements for both the transaction success ratio and the data extraction rate that significantly increase up to 95% and 87%, respectively. On the contrary, the overall average delay is not always reduced.

A Survey on LoRaWAN Technology: Recent Trends, Opportunities, Simulation Tools and Future Directions

Low-power wide-area network (LPWAN) technologies play a pivotal role in IoT applications, owing to their capability to meet the key IoT requirements (e.g., long range, low cost, small data volumes, massive device number, and low energy consumption). Between all obtainable LPWAN technologies, long-range wide-area network (LoRaWAN) technology has attracted much interest from both industry and academia due to networking autonomous architecture and an open standard specification. This paper presents a comparative review of five selected driving LPWAN technologies, including NB-IoT, SigFox, Telensa, Ingenu (RPMA), and LoRa/LoRaWAN. The comparison shows that LoRa/LoRaWAN and SigFox surpass other technologies in terms of device lifetime, network capacity, adaptive data rate, and cost. In contrast, NB-IoT technology excels in latency and quality of service. Furthermore, we present a technical overview of LoRa/LoRaWAN technology by considering its main features, opportunities, and open issues. We also compare the most important simulation tools for investigating and analyzing LoRa/LoRaWAN network performance that has been developed recently. Then, we introduce a comparative evaluation of LoRa simulators to highlight their features. Furthermore, we classify the recent efforts to improve LoRa/LoRaWAN performance in terms of energy consumption, pure data extraction rate, network scalability, network coverage, quality of service, and security. Finally, although we focus more on LoRa/LoRaWAN issues and solutions, we introduce guidance and directions for future research on LPWAN technologies.

The Downside of Software-Defined Networking in Wireless Network

Mobile traffic volumes have grown exponentially because of the increase in services and applications. Traditional networks are complex to manage because the forwarding, control, and management planes are all bundled together and, thus, administrators are supposed to deploy high-level policies, as each vendor has its own configuration methods. Software-Defined Networking (SDN) is considered the future paradigm of communication networks. It decouples control logic from its underlying hardware, thereby promoting logically centralized network control and making the network more programmable and easy to configure. Low-power wireless technologies are moving toward a multitenant and multiapplication Internet of Things (IoT), which requires an architecture with scalable, reliable, and configured solutions. However, employing an SDN-based centralized architecture in the environment of a low-power wireless IoT network introduces significant challenges, such as difficult-to-control traffic, unreliable links, network contention, and high associated overheads that can significantly affect the performance of the network. This paper is a contribution toward a performance evaluation for the use of SDN in wireless networking by evaluating the latency, packet drop ratio (PDR), data extraction rate (DER), and overheads. The results show that SDN adds a high percentage of overheads to the network, which is about 43% of the 57% user packets, and the DER drops when the number of mesh nodes are increased, in addition to the high loss that was observed for packets that traveled over more hops.

A field-measurements-based LoRa network planning tool

Long range (LoRa) transmission technology enables energy-constrained devices such as the tiny sensor systems used in internet-of-things applications that are distributed over wide areas while still being able to establish appropriate connectivity. This has resulted in the development of an exponentially increasing number of different solutions and services based on LoRa, be they dedicated to the long-term monitoring of distributed plants and infrastructures or to human-centred applications such as safety-oriented sensor systems for use in the workplace. In dense LoRa networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential for ensuring reliable and stable communication and minimising costs. In this paper, after comparing different path loss models based on a field measurement campaign for LoRa received signal strength indicator values within a university campus, two main modifications of the LoRa simulator tool were implemented. These were aimed at improving the accuracy of the prediction of the number of sustainable nodes in relation to the target data extraction rate set. The simulations based on field measurements demonstrated that through an improved path loss evaluation and the use of three gateways, the number of nodes could be increased theoretically from around 100 to around 6,000.

Improving Quality-Of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard—LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova’s (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova’s, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

An Efficient Equal Air-Time Transmission Strategy for Wireless Seismometer Array Based on LoRaWAN With CuckooHash

Recently, large-scale wireless seismometer array (WSA) have played an increasingly important role in oil/gas exploration industry and seismology research. However, due to limited wireless channel bandwidth, transmission latency and high power consumption caused by transmission conflicts in battery-powered seismometers, real-time large-scale WSA system remains unsolved. In this article, we introduce a LoRaWAN-based WSA system with high precise time slot deriving from a synchronization platform composed of GPS timing module and temperature compensated crystal oscillator (TCXO) clock counter. Using the global sharing time slot, we propose an Equal Air Time and CuckooHash (EAT&CH) algorithm for the WSA to resolve channel congestion and delays when multiple seismometers upload data simultaneously. In EAT&CH, an equal air-time spreading factor (SF) allocation method based on the coarse/fine-grained theory is employed to reduce the collision probability of data transmission. To improve the scalability of the WSA in terms of reducing both collision probability and data delay, a different carrier frequency (CF) channel with the same SF allocation plan depending on CuckooHash is designed to reduce the network latency. Simulation results show that the proposed algorithm achieves nearly 100% data extraction rate of the WSA with less than two times the delay, compared with the existing Adaptive Data Rate (ADR) mechanism.

A Rate Adaptation Scheme with Enhanced Loss Differentiation for LoRaWAN Communications

As an emerging technology for long-range communication, LoRaWAN provides several data rates for transmission and the choice of data rates will affect the communication distance and the link quality. So how to allocate this communication resource in a network with a large amount of devices is a challenge. In this paper, we classify the reasons of packets loss as channel attenuation and data collision and then propose a rate adaptation scheme with enhanced loss differentiation. The proposed scheme can estimate two packet loss rates according to the channel attenuation probability model and data collision probability model respectively. And the selection of communication data rate is given by the balance of probabilities. Simulation results indicate that the proposed scheme is better than ADR and EXPLoRa on Data Extraction Rate and Goodput.

Augmenting LoRaWAN Performance With Listen Before Talk

Standard LoRaWANs leverage pure ALOHA at the medium access control layer, which is proved to be a performance bottleneck as the network size scales up. Stimulated by this fact, this paper studies the applicability and the performance of listen before talk (LBT) medium access schemes in the context of LoRaWANs. We consider two different implementations of LBT: physical layer LBT based on energy detection only and MAC layer LBT based on layer 2 frame decoding, and we propose a Markovian framework to evaluate the performance of LoRaWANs under such setting in terms of data extraction rate and average delay experienced by transmitted uplink messages. The proposed framework is also leveraged to assess the performance of “mixed” LoRaWAN scenarios, where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to LBT.

Gabor wavelet multi-linear discriminant analysis for data extraction in ECG signals

Electrocardiogram (ECG) analysis is a common clinical cardiac examination for detecting the cardiac abnormalities. ECG signal has many components and features like P, QRS and T. The waveform with P, QRS and T components are used to identify the cardiac disease. But, the ECG signals are contaminated by the presence of many noise or artifacts. In addition, the data extraction and classification remained challenging issue in ECG signal analysis. In order to improve the data extraction rate and classification accuracy, Gabor Wavelet Multi-linear Discriminant based Data Extraction (GWMD-DE) technique is introduced. Initially in this technique, the preprocessing of ECG signal is carried out using median filter for removing the noise or artifacts. After performing the preprocessing tasks, Gabor Wavelet Transformation is used in GWMD-DE technique for extracting the P, T waves and QRS complex without any component loss from ECG signals resulting in higher data extraction rate. Finally, multi-linear discriminant analysis is performed in GWMD-DE technique for classifying the extracted data as P, T waves and QRS complex with higher classification accuracy. The performance of GWMD-DE technique is measured in terms of data extraction rate, classification accuracy, and execution time. The simulation results show that GWMD-DE technique is able to improve the performance of data extraction rate and also reduces the execution time of data extraction when compared to state-of-the-art works. Moreover, proposed GWMD-DE technique improves the classification accuracy and minimizes the signal-to-mean square error, computational complexity and space complexity when compared to existing methods, Symlets sym5 wavelet function and Hilbert transform based adaptive threshold technique (Lin et al., IRBM 35(6):351–361, 2014; Rodriguez et al., in IJART 13:261–269, 2015).