scholarly journals Performance Estimation of Sustainable Smart Farming with Blockchain Technology

2021 ◽  
Vol 3 (2) ◽  
pp. 97-106
Author(s):  
Sivaganesan D
Keyword(s):  
Signal To Noise Ratio ◽  
Performance Estimation ◽  
Power Splitting ◽  
Relay Nodes ◽  
Time Switching ◽  
Transmission Success ◽  
Blockchain Technology ◽  
Smart Farming ◽  
Smart Agriculture ◽  
Selection Of

Sustainable smart agriculture with increase in signal to interference or signal to noise ratio (SIR/SNR) for selection of best relay is discussed in a wireless blockchain based network. The overall communication throughput (OCT), power splitting relaying (PSR), time switching relaying (TSR) and transmission success rate (TRS) are also derived during the selection of best relay performance with and without interference. The performance of OCT, PSR, TSR and TRS increases with the increase in the number of potential relay nodes as seen in the results of derivation. The accuracy of the theoretical values are validated by numerical simulations.

scholarly journals Outage probability analysis for hybrid TSR-PSR based SWIPT systems over log-normal fading channels

2021 ◽  
Vol 11 (5) ◽  
pp. 4233
Author(s):  
Hoang Thien Van ◽  
Hoang-Phuong Van ◽  
Danh Hong Le ◽  
Ma Quoc Phu ◽  
Hoang-Sy Nguyen
Keyword(s):  
Outage Probability ◽  
Fading Channels ◽  
System Performance ◽  
Signal To Noise Ratio ◽  
Cooperative Relaying ◽  
Power Splitting ◽  
Amplify And Forward ◽  
Time Switching ◽  
Log Normal ◽  
The Impact

Employing simultaneous information and power transfer (SWIPT) technology in cooperative relaying networks has drawn considerable attention from the research community. We can find several studies that focus on Rayleigh and Nakagami-m fading channels, which are used to model outdoor scenarios. Differing itself from several existing studies, this study is conducted in the context of indoor scenario modelled by log-normal fading channels. Specifically, we investigate a so-called hybrid time switching relaying (TSR)-power splitting relaying (PSR) protocol in an energy-constrained cooperative amplify-and-forward (AF) relaying network. We evaluate the system performance with outage probability (OP) by analytically expressing and simulating it with Monte Carlo method. The impact of power-splitting (PS), time-switching (TS) and signal-to-noise ratio (SNR) on the OP was as well investigated. Subsequently, the system performance of TSR, PSR and hybrid TSR-PSR schemes were compared. The simulation results are relatively accurate because they align well with the theory.

scholarly journals Performance Analysis of Hybrid Protocol Based AF EH Relaying over Asymmetric Fading Channels

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 50 ◽  
Author(s):  
Xutao Sheng ◽  
Guangyue Lu ◽  
Liqin Shi ◽  
Yinghui Ye
Keyword(s):  
Fading Channels ◽  
Rayleigh Fading ◽  
Signal To Noise Ratio ◽  
System Capacity ◽  
Rician Fading ◽  
High Signal ◽  
Amplify And Forward ◽  
Relay Nodes ◽  
Energy Constrained ◽  
Selection Of

Simultaneous wireless information and power transfer is a practicable solution to encourage energy-constrained relay nodes to cooperate with the source to transmit information to the destination. In this paper, we study the outage performance of hybrid protocol based amplify-and-forward (AF) relay networks over asymmetric fading channels, where the source-relay link and the relay-destination link are subjected to Rician fading and Rayleigh fading, respectively. In particular, we derive the lower bound of outage probability and the upper bound of outage capacity based on a high signal-to-noise ratio approximation, respectively. We further investigate the effects of various system parameters, such as the parameters of hybrid protocol, the target rate, and the Rician K-factor, on the investigated network. It is shown that a good selection of parameters of hybrid protocol is of significance to improve system capacity, and that a larger Rician factor is desirable in the investigated network.

scholarly journals An Incentive-based Mechanism for Volunteer Computing Using Blockchain

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Ismaeel Al Ridhawi ◽  
Moayad Aloqaily ◽  
Yaser Jararweh
Keyword(s):  
Resource Sharing ◽  
Secure Communication ◽  
Service Providers ◽  
Volunteer Computing ◽  
Resource Usage ◽  
Content Filtering ◽  
Computing Device ◽  
Blockchain Technology ◽  
Smart Agriculture ◽  
Industrial Solutions

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

scholarly journals Error Performance Estimation of Modulated Retroreflective Transdermal Optical Wireless Links with Diversity under Generalized Pointing Errors

Telecom ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 167-180
Author(s):  
George K. Varotsos ◽  
Hector E. Nistazakis ◽  
Konstantinos Aidinis ◽  
Fadi Jaber ◽  
Mohd Nasor ◽  
...  
Keyword(s):  
High Speed ◽  
Signal To Noise Ratio ◽  
Spatial Diversity ◽  
Performance Estimation ◽  
Optical Wireless ◽  
Wireless Data ◽  
Error Performance ◽  
Pointing Errors ◽  
Implanted Medical Devices ◽  
Recent Developments

Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.

Automated phase attribute-based picking applied to reflection seismics

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V141-V150 ◽  
Author(s):  
Emanuele Forte ◽  
Matteo Dossi ◽  
Michele Pipan ◽  
Anna Del Ben
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Method ◽  
Data Sets ◽  
Reflection Seismic ◽  
Instantaneous Phase ◽  
Whole Process ◽  
Specific Analysis ◽  
Reflection Seismics ◽  
Selection Of ◽  
Energy Package

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.

scholarly journals Design and Optimization for UAV-Enabled Two-Way Relaying System with SWIPT

2020 ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two userequipment exchange information with the help of UAV relay and harvest energythrough power splitting (PS) scheme. Under the transmission power constraintsat UEs and UAV relay, a non-convex intractable optimization problem isformulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factors optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of two proposed schemes.

scholarly journals DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

2019 ◽  
Author(s):  
Nguyen P. Nguyen ◽  
Jacob Gotberg ◽  
Ilker Ersoy ◽  
Filiz Bunyak ◽  
Tommi White
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Micrographs ◽  
Signal To Noise Ratio ◽  
Particle Analysis ◽  
User Interactions ◽  
Distance Map ◽  
Protein Particles ◽  
Selection Of ◽  
Specific Particle

AbstractSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnet’s first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated “deep regression” system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on β-galactosidase (EMPIAR-10017) [2] and β-galactosidase (EMPIAR-10061) [3], was then compared to RELION’s interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

scholarly journals Collecting Microplastics in Gardens: Case Study (i) of Soil

2021 ◽  
Vol 9 ◽  
Author(s):  
Zahra Sobhani ◽  
Yunlong Luo ◽  
Christopher T. Gibson ◽  
Youhong Tang ◽  
Ravi Naidu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Statistical Point ◽  
Image Mapping ◽  
Noise Ratio ◽  
In The Beginning ◽  
Raman Image ◽  
Selection Of

As an emerging contaminant, microplastic is receiving increasing attention. However, the contamination source is not fully known, and new sources are still being identified. Herewith, we report that microplastics can be found in our gardens, either due to the wrongdoing of leaving plastic bubble wraps to be mixed with mulches or due to the use of plastic landscape fabrics in the mulch bed. In the beginning, they were of large sizes, such as > 5 mm. However, after 7 years in the garden, owing to natural degradation, weathering, or abrasion, microplastics are released. We categorize the plastic fragments into different groups, 5 mm–0.75 mm, 0.75 mm–100 μm, and 100–0.8 μm, using filters such as kitchenware, meaning we can collect microplastics in our gardens by ourselves. We then characterized the plastics using Raman image mapping and a logic-based algorithm to increase the signal-to-noise ratio and the image certainty. This is because the signal-to-noise ratio from a single Raman spectrum, or even from an individual peak, is significantly less than that from a spectrum matrix of Raman mapping (such as 1 vs. 50 × 50) that contains 2,500 spectra, from the statistical point of view. From the 10 g soil we sampled, we could detect the microplastics, including large (5 mm–100 μm) fragments and small (<100 μm) ones, suggesting the degradation fate of plastics in the gardens. Overall, these results warn us that we must be careful when we do gardening, including selection of plastic items for gardens.

scholarly journals Smart Agriculture and Smart Farming using IoT Technology

2021 ◽  
Vol 2089 (1) ◽  
pp. 012038
Author(s):  
V Dankan Gowda ◽  
M Sandeep Prabhu ◽  
M Ramesha ◽  
Jayashree M Kudari ◽  
Ansuman Samal
Keyword(s):  
Internet Of Things ◽  
Treatment Optimization ◽  
Agricultural Producers ◽  
Support Tools ◽  
Smart Farming ◽  
Protection Systems ◽  
Smart Agriculture ◽  
Remote Monitoring Systems ◽  
Future Technologies ◽  
The Internet Of Things

Abstract It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture.

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