5,755 research outputs found
Investigation of Radar Signal Interaction with Crossflow Turbine for Aviation Application
The increased adoption of wind energy is an important part of the push towards a net zero-emission economy. One obstacle that stands in the way of a higher rate of wind energy adoption is the interference that wind turbines cause to nearby radar installations. Wind turbines negatively affect the performance of nearby radar sites in a variety of different ways. Almost all types of radar are affected in at least one of these ways.In order to understand the degree to which an object such as a wind turbine interacts with radar, it is important to have detailed radar cross section (RCS) data for the object. In this work, a novel, low-cost, scale model radar cross section characterization system is presented with various advantages over traditional designs. This system was used to characterize the RCS of the novel Crossflow wind turbine. Additionally, work has been carried out on the characterization of metamaterial absorber coatings that can be applied to new and existing turbines for the purposes of reducing their radar cross section and the degree to which they cause radar inter-ference. The works presented can be leveraged to reduce concerns around radar interference from wind turbines, as well as to iteratively generate ge-ometries with lower radar cross sections for the aviation and infrastructure sectors, ultimately accelerating the pace of wind energy adoption and the move towards a net zero-emission economy
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Processing genome-wide association studies within a repository of heterogeneous genomic datasets
Background
Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.
Results
To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multisample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.
Conclusions
As a result of our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows
Linearized Data Center Workload and Cooling Management
With the current high levels of energy consumption of data centers, reducing
power consumption by even a small percentage is beneficial. We propose a
framework for thermal-aware workload distribution in a data center to reduce
cooling power consumption. The framework includes linearization of the general
optimization problem and proposing a heuristic to approximate the solution for
the resulting Integer Linear Programming (ILP) problems. We first define a
general nonlinear power optimization problem including several cooling
parameters, heat recirculation effects, and constraints on server temperatures.
We propose to study a linearized version of the problem, which is easier to
analyze. As an energy saving scenario and as a proof of concept for our
approach, we also consider the possibility that the red-line temperature for
idle servers is higher than that for busy servers. For the resulting ILP
problem, we propose a heuristic for intelligent rounding of the fractional
solution. Through numerical simulations, we compare our heuristics with two
baseline algorithms. We also evaluate the performance of the solution of the
linearized system on the original system. The results show that the proposed
approach can reduce the cooling power consumption by more than 30 percent
compared to the case of continuous utilizations and a single red-line
temperature
Emerging Power Electronics Technologies for Sustainable Energy Conversion
This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches
Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures
The integration of blockchain into IoT can provide reliable control of the IoT network's
ability to distribute computation over a large number of devices. It also allows the AI
system to use trusted data for analysis and forecasts while utilising the available IoT
hardware to coordinate the execution of tasks in parallel, using a fully distributed
approach.
This thesis's  rst contribution is a practical implementation of a real world IoT-
blockchain application,
ood detection use case, is demonstrated using Ethereum proof
of authority (PoA). This includes performance measurements of the transaction con-
 rmation time, the system end-to-end latency, and the average power consumption.
The study showed that blockchain can be integrated into IoT applications, and that
Ethereum PoA can be used within IoT for permissioned implementation. This can be
achieved while the average energy consumption of running the
ood detection system
including the Ethereum Geth client is small (around 0.3J).
The second contribution is a novel IoT-centric consensus protocol called honesty-
based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed
and then deployed and tested. Performance measurements and evaluation along with
the security analyses of HDPoA were conducted using a total of 30 di erent IoT de-
vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements
included energy consumption, the devices' hash power, and the transaction con rma-
tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J,
54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the
ESP8266 devices, respectively, this achieved while there is limited impact on each de-
vice's power. In HDPoA the transaction con rmation time was reduced to only one
block compared to up to six blocks in bitcoin.
The third contribution is a novel, secure, distributed and decentralised architecture
for supporting the implementation of distributed arti cial intelligence (DAI) using
hardware platforms provided by IoT. A trained DAI system was implemented over the
IoT, where each IoT device hosts one or more neurons within the DAI layers. This
is accomplished through the utilisation of blockchain technology that allows trusted
interaction and information exchange between distributed neurons. Three di erent
datasets were tested and the system achieved a similar accuracy as when testing on a
standalone system; both achieved accuracies of 92%-98%. The system accomplished
that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT
while ensuring the accuracy of the system is preserved.
The fourth contribution is a novel and secure architecture that integrates the ad-
vantages o ered by edge computing, arti cial intelligence (AI), IoT end-devices, and
blockchain. This new architecture has the ability to monitor the environment, collect
data, analyse it, process it using an AI-expert engine, provide predictions and action-
able outcomes, and  nally share it on a public blockchain platform. The pandemic
caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as
a use-case implementation to test and evaluate the proposed system. While providing
the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved
while the AI-engine only requires a 7% increase in power consumption. This demon-
strate the system's ability to protect the data and support the AI system, and improves
the IoT overall security with limited impact on the IoT devices.
The  fth and  nal contribution is enhancing the security of the HDPoA through
the integration of a hardware secure module (HSM) and a hardware wallet (HW). A
performance evaluation regarding the energy consumption of nodes that are equipped
with HSM and HW and a security analysis were conducted. In addition to enhancing
the nodes' security, the HSM can be used to sign more than 120 bytes/joule and
encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule
and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM
and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain
applications while providing much needed security in terms of con dentiality, trust in
devices, and attack deterrence.
The above contributions showed that blockchain can be integrated into IoT systems.
It showed that blockchain can successfully support the integration of other technolo-
gies such as AI, IoT end devices, and edge computing into one system thus allowing
organisations and users to bene t greatly from a resilient, distributed, decentralised,
self-managed, robust, and secure systems
Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET
Wireless networks that are decentralized and communicate without using
existing infrastructure are known as mobile ad-hoc networks. The most common
sorts of threats and attacks can affect MANETs. Therefore, it is advised to
utilize intrusion detection, which controls the system to detect additional
security issues. Monitoring is essential to avoid attacks and provide extra
protection against unauthorized access. Although the current solutions have
been designed to defeat the attack nodes, they still require additional
hardware, have considerable delivery delays, do not offer high throughput or
packet delivery ratios, or do not do so without using more energy. The
capability of a mobile node to forward packets, which is dependent on the
platform's life quality, may be impacted by the absence of the network node
power source. We developed the Safe Routing Approach (SRA), which uses
behaviour analysis to track and monitor attackers who discard packets during
the route discovery process. The attacking node recognition system is made for
irregular routing node detection to protect the controller network's usual
properties from becoming recognized as an attack node. The suggested method
examines the nearby attack nodes and conceals the trusted node in the routing
pathway. The path is instantly assigned after the initial discovery of trust
nodes based on each node's strength value. It extends the network's life span
and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy
consumption, network performance, and detection of attack nodes, the suggested
approach is contrasted with AIS, ZIDS, and Improved AODV. The findings
demonstrate that the recommended strategy performs superior in terms of PDR,
residual energy, and network throughput
Performance comparison of CPU and GPGPU calculations using three simple case studies
In this work, we have prepared and analyzed three case studies comparing CPU and GPGPU calculations.
After briefly introducing the topic of parallel programming by means of contemporary CPU and GPGPU technologies, we provide an overview of selected existing works closely related to the topic of the paper.
For each of the case studies, a set of programs has been implemented using the following technologies: pure CPU, CPU SIMD, CPU multi-threaded, CPU multi-threaded with SIMD instructions, and GPU - Cuda.
We also illustrate the basic idea of the operation of selected algorithms using code snippets.
Subsequently, the particular implementations are compared, and obtained results are evaluated and discussed
Recommended from our members
Multivolume devices, kits and related methods for quantification and detection of nucleic acids and other analytes
Provided are devices comprising multivolume analysis regions, the devices being capable of supporting amplification, detection, and other processes. Also provided are related methods of detecting or estimating the presence nucleic acids, viral levels, and other biological markers of interest
- …