11,055 research outputs found
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data
As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,
cyberattack prevention and data transmission integrity become essential
problems. This study provides a complete framework to handle these difficulties
by integrating a cognitive intelligence (CI) framework, an information
processing protocol, and sophisticated artificial intelligence (AI) and big
data analytics approaches. The CI architecture is intended to improve WSN
security by dynamically reacting to an evolving threat scenario. It employs
artificial intelligence algorithms to continuously monitor and analyze network
behavior, identifying and mitigating any intrusions in real time. Anomaly
detection algorithms are also included in the framework to identify packet drop
instances caused by attacks or network congestion. To support the CI
architecture, an information processing protocol focusing on efficient and
secure data transfer within the WSN is introduced. To protect data integrity
and prevent unwanted access, this protocol includes encryption and
authentication techniques. Furthermore, it enhances the routing process with
the use of AI and big data approaches, providing reliable and timely packet
delivery. Extensive simulations and tests are carried out to assess the
efficiency of the suggested framework. The findings show that it is capable of
detecting and preventing several forms of assaults, including as
denial-of-service (DoS) attacks, node compromise, and data tampering.
Furthermore, the framework is highly resilient to packet drop occurrences,
which improves the WSN's overall reliability and performanc
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
Integrated Optical Fiber Sensor for Simultaneous Monitoring of Temperature, Vibration, and Strain in High Temperature Environment
Important high-temperature parts of an aero-engine, especially the power-related fuel system and rotor system, are directly related to the reliability and service life of the engine. The working environment of these parts is extremely harsh, usually overloaded with high temperature, vibration and strain which are the main factors leading to their failure. Therefore, the simultaneous measurement of high temperature, vibration, and strain is essential to monitor and ensure the safe operation of an aero-engine.
In my thesis work, I have focused on the research and development of two new sensors for fuel and rotor systems of an aero-engine that need to withstand the same high temperature condition, typically at 900 °C or above, but with different requirements for vibration and strain measurement.
Firstly, to meet the demand for high temperature operation, high vibration sensitivity, and high strain resolution in fuel systems, an integrated sensor based on two fiber Bragg gratings in series (Bi-FBG sensor) to simultaneously measure temperature, strain, and vibration is proposed and demonstrated. In this sensor, an L-shaped cantilever is introduced to improve the vibration sensitivity. By converting its free end displacement into a stress effect on the FBG, the sensitivity of the L-shaped cantilever is improved by about 400% compared with that of straight cantilevers. To compensate for the strain sensitivity of FBGs, a spring-beam strain sensitization structure is designed and the sensitivity is increased to 5.44 pm/με by concentrating strain deformation. A novel decoupling method ‘Steps Decoupling and Temperature Compensation (SDTC)’ is proposed to address the interference between temperature, vibration, and strain. A model of sensing characteristics and interference of different parameters is established to achieve accurate signal decoupling. Experimental tests have been performed and demonstrated the good performance of the sensor.
Secondly, a sensor based on cascaded three fiber Fabry-Pérot interferometers in series (Tri-FFPI sensor) for multiparameter measurement is designed and demonstrated for engine rotor systems that require higher vibration frequencies and greater strain measurement requirements. In this sensor, the cascaded-FFPI structure is introduced to ensure high temperature and large strain simultaneous measurement. An FFPI with a cantilever for high vibration frequency measurement is designed with a miniaturized size and its geometric parameters optimization model is established to investigate the influencing factors of sensing characteristics. A cascaded-FFPI preparation method with chemical etching and offset fusion is proposed to maintain the flatness and high reflectivity of FFPIs’ surface, which contributes to the improvement of measurement accuracy. A new high-precision cavity length demodulation method is developed based on vector matching and clustering-competition particle swarm optimization (CCPSO) to improve the demodulation accuracy of cascaded-FFPI cavity lengths. By investigating the correlation relationship between the cascaded-FFPI spectral and multidimensional space, the cavity length demodulation is transformed into a search for the highest correlation value in space, solving the problem that the cavity length demodulation accuracy is limited by the resolution of spectral wavelengths. Different clustering and competition characteristics are designed in CCPSO to reduce the demodulation error by 87.2% compared with the commonly used particle swarm optimization method. Good performance and multiparameter decoupling have been successfully demonstrated in experimental tests
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
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Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things
This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of ThingsCopyright © 2023 by the authors. The internet of things, a collection of diversified distributed nodes, implies a varying choice of activities ranging from sleep monitoring and tracking of activities, to more complex activities such as data analytics and management. With an increase in scale comes even greater complexities, leading to significant challenges such as excess energy dissipation, which can lead to a decrease in IoT devices’ lifespan. Internet of things’ (IoT) multiple variable activities and ample data management greatly influence devices’ lifespan, making resource optimisation a necessity. Existing methods with respect to aspects of resource management and optimisation are limited in their concern of devices energy dissipation. This paper therefore proposes a decentralised approach, which contains an amalgamation of efficient clustering techniques, edge computing paradigms, and a hybrid algorithm, targeted at curbing resource optimisation problems and life span issues associated with IoT devices. The decentralised topology aimed at the resource optimisation of IoT places equal importance on resource allocation and resource scheduling, as opposed to existing methods, by incorporating aspects of the static (round robin), dynamic (resource-based), and clustering (particle swarm optimisation) algorithms, to provide a solid foundation for an optimised and secure IoT. The simulation constructs five test-case scenarios and uses performance indicators to evaluate the effects the proposed model has on resource optimisation in IoT. The simulation results indicate the superiority of the PSOR2B to the ant colony, the current centralised optimisation approach, LEACH, and C-LBCA.This research received no external funding
BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI
In recent years, indoor human presence detection based on supervised learning
(SL) and channel state information (CSI) has attracted much attention. However,
the existing studies that rely on spatial information of CSI are susceptible to
environmental changes, such as object movement, atmospheric factors, and
machine rebooting, which degrade prediction accuracy. Moreover, SL-based
methods require time-consuming labeling for retraining models. Therefore, it is
imperative to design a continuously monitored model life-cycle using a
semi-supervised learning (SSL) based scheme. In this paper, we conceive a
bifold teacher-student (BTS) learning approach for presence detection systems
that combines SSL by utilizing partially labeled and unlabeled datasets. The
proposed primal-dual teacher-student network intelligently learns spatial and
temporal features from labeled and unlabeled CSI. Additionally, the enhanced
penalized loss function leverages entropy and distance measures to distinguish
drifted data, i.e., features of new datasets affected by time-varying effects
and altered from the original distribution. The experimental results
demonstrate that the proposed BTS system sustains asymptotic accuracy after
retraining the model with unlabeled data. Furthermore, the label-free BTS
outperforms existing SSL-based models in terms of the highest detection
accuracy while achieving the asymptotic performance of SL-based methods
Instance-based Learning with Prototype Reduction for Real-Time Proportional Myocontrol: A Randomized User Study Demonstrating Accuracy-preserving Data Reduction for Prosthetic Embedded Systems
This work presents the design, implementation and validation of learning
techniques based on the kNN scheme for gesture detection in prosthetic control.
To cope with high computational demands in instance-based prediction, methods
of dataset reduction are evaluated considering real-time determinism to allow
for the reliable integration into battery-powered portable devices. The
influence of parameterization and varying proportionality schemes is analyzed,
utilizing an eight-channel-sEMG armband. Besides offline cross-validation
accuracy, success rates in real-time pilot experiments (online target
achievement tests) are determined. Based on the assessment of specific dataset
reduction techniques' adequacy for embedded control applications regarding
accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself
promising when applying kNN on the reduced set. A randomized, double-blind user
study was conducted to evaluate the respective methods (kNN and kNN with
DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier
Features (RR-RFF). The kNN-based methods performed significantly better
(p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was
no statistically significant difference (significance level 0.05). This is
remarkable in consideration of only one sample per class in the reduced set,
thus yielding a reduction rate of over 99% while preserving success rate. The
same behaviour could be confirmed in an extended user study. With k=1, which
turned out to be an excellent choice, the runtime complexity of both kNN (in
every prediction step) as well as DSM-kNN (in the training phase) becomes
linear concerning the number of original samples, favouring dependable wearable
prosthesis applications
Polymer-Based Micromachining for Scalable and Cost-Effective Fabrication of Gap Waveguide Devices Beyond 100 GHz
The terahertz (THz) frequency bands have gained attention over the past few years due to the growing number of applications in fields like communication, healthcare, imaging, and spectroscopy. Above 100 GHz transmission line losses become dominating, and waveguides are typically used for transmission. As the operating frequency approaches higher frequencies, the dimensions of the waveguide-based components continue to decrease. This makes the traditional machine-based (computer numerical control, CNC) fabrication method increasingly challenging in terms of time, cost, and volume production. Micromachining has the potential of addressing the manufacturing issues of THz waveguide components. However, the current microfabrication techniques either suffer from technological immaturity, are time-consuming, or lack sufficient cost-efficiency. A straightforward, fast, and low-cost fabrication method that can offer batch fabrication of waveguide components operating at THz frequency range is needed to address the requirements.A gap waveguide is a planar waveguide technology which does not suffer from the dielectric loss of planar waveguides, and which does not require any electrical connections between the metal walls. It therefore offers competitive loss performance together with providing several benefits in terms of assembly and integration of active components. This thesis demonstrates the realization of gap waveguide components operating above 100 GHz, in a low-cost and time-efficient way employing the development of new polymer-based fabrication methods.A template-based injection molding process has been designed to realize a high gain antenna operating at D band (110 - 170 GHz). The injection molding of OSTEMER is an uncomplicated and fast device fabrication method. In the proposed method, the time-consuming and complicated parts need to be fabricated only once and can later be reused.A dry film photoresist-based method is also presented for the fabrication of waveguide components operating above 100 GHz. Dry film photoresist offers rapid fabrication of waveguide components without using complex and advanced machinery. For the integration of active circuits and passive waveguides section a straightforward solution has been demonstrated. By utilizing dry film photoresist, a periodic metal pin array has been fabricated and incorporated in a waveguide to microstrip transition that can be an effective and low-cost way of integrating MMIC of arbitrary size to waveguide blocks
Planar-type silicon thermoelectric generator with phononic nanostructures for 100 {\mu}W energy harvesting
Energy harvesting is essential for the internet-of-things networks where a
tremendous number of sensors require power. Thermoelectric generators (TEGs),
especially those based on silicon (Si), are a promising source of clean and
sustainable energy for these sensors. However, the reported performance of
planar-type Si TEGs never exceeded power factors of 0.1
due to the poor thermoelectric performance of Si and the suboptimal design of
the devices. Here, we report a planar-type Si TEG with a power factor of 1.3
around room temperature. The increase in thermoelectric
performance of Si by nanostructuring based on the phonon-glass electron-crystal
concept and optimized three-dimensional heat-guiding structures resulted in a
significant power factor. In-field testing demonstrated that our Si TEG
functions as a 100--class harvester. This result is an essential step
toward energy harvesting with a low-environmental load and cost-effective
material with high throughput, a necessary condition for energy-autonomous
sensor nodes for the trillion sensors universe
A Conceptual Framework for Designing Interactive Human-Centred Building Spaces to Enhance User Experience in Specific-Purpose Buildings
Human/User interaction with buildings are mostly restricted to interacting
with building automation systems through user-interfaces that mainly aim to
improve energy efficiency of buildings and ensure comfort of occupants. This
research builds on the existing theories of Human-Building Interaction (HBI)
and proposes a novel conceptual framework for HBI that combines the concepts of
Human-Computer Interaction (HCI) and Ambient Intelligence (AmI). The proposed
framework aims to study the needs of occupants in specific-purpose buildings,
which is currently undermined. Specifically, we explore the application of the
proposed HBI framework to improve the learning experience of students in
academic buildings. Focus groups and semi-structured interviews were conducted
among students who are considered primary occupants of Goodwin Hall, a flagship
smart engineering building at Virginia Tech. Qualitative coding and concept
mapping were used to analyze the qualitative data and determine the impact of
occupant-specific needs on the learning experience of students in academic
buildings. The occupant-specific problem that was found to have the highest
direct impact on learning experience was finding study space and highest
indirect impact was Indoor Environment Quality (IEQ). We discuss new ideas for
designing Intelligent User Interfaces (IUI), e.g. Augmented Reality (AR),
increase the perceivable affordances for building occupants and considering a
context-aware ubiquitous analytics-based strategy to provide services that are
tailored to address the identified needs
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