Massey Documents by Type

Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294

Browse

Subject: search.filters.subject.0906 Electrical and Electronic Engineering

Search Results

Now showing 1 - 10 of 21
  • Thumbnail Image
    Item
    Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.
    (MDPI (Basel, Switzerland), 2021-12-22) Liu T; Sabrina F; Jang-Jaccard J; Xu W; Wei Y
    A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.
  • Thumbnail Image
    Item
    Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning
    (MDPI AG, 24/03/2023) Noble F; Xu M; Alam F
    Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.
  • Thumbnail Image
    Item
    Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array
    (MDPI (Basel, Switzerland), 20/03/2021) Parr B; Legg M; Bradley S; Alam F
    Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.
  • Thumbnail Image
    Item
    Portable Multi-Inputs Renewable Energy System for Small Scale Remote Application
    (Scientific Research Publishing Inc, 14/02/2018) Al-Bahadly I
    This paper presents a potable renewable energy system. The portable renewable energy power unit is designed from a need. The need is for first response teams in remote natural disaster situations to have a reliable source of energy to power a small vaccine refrigerator or water purification system and a basic satellite communication system. It is important that such a need is explored as a practical solution has the potential to save the lives of people in remote areas, who would otherwise suffer from a lack of humanitarian aid. Currently diesel generators are the primary source of electricity generation for disaster responders and in most situations work very well and provide a sufficient amount of electricity to meet the power needs. However, in remote areas road infrastructure is often damaged. In this type of situation getting a constant supply of diesel to the area is an expensive or impractical operation. This is where the portable renewable energy power unit bridges the gap and allows a more practical solution to be implemented. The specific aim of the work is to design a compact, stand-alone, product that can be easily transported by people across uneven terrain. It can generate power from wind, solar and hydro energy sources. In this work a new non-isolated multiport DC-DC converter topology for a hybrid energy system in low power applications is proposed. The new topology assimilates multiple renewable energy sources and power up multiple loads with different output levels. A complete Solid works model and FEA analysis, on required components, is completed. The scope of the work encompasses both the electrical and mechanical design of the system.
  • Thumbnail Image
    Item
    Hyperledger Fabric Blockchain for Securing the Edge Internet of Things
    (MDPI (Basel, Switzerland), 7/01/2021) Pajooh HH; Rashid M; Alam F; Demidenko S
    Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
  • Thumbnail Image
    Item
    Sensors and Instruments for Brix Measurement: A Review
    (MDPI AG, 16/03/2022) Jaywant SA; Singh H; Arif KM
    Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry.
  • Thumbnail Image
    Item
    Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
    (MDPI AG, 23/09/2022) Konings D; Alam F; Faulkner N; de Jong C
    In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
  • Thumbnail Image
    Item
    Multi-Layer Blockchain-Based Security Architecture for Internet of Things
    (MDPI (Basel, Switzerland), 2021-02) Pajooh HH; Rashid M; Alam F; Demidenko S
    The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.
  • Thumbnail Image
    Item
    Accurate Ultrasound Indoor Localization Using Spring-Relaxation Technique
    (MDPI (Basel, Switzerland), 2021-06) Chew MT; Alam F; Legg M; Sen Gupta G
    This paper reports on the development of an ultrasonic sensing-based active localization system. The system employs an ultrasonic array to transmit chirp signals and time-of-flight measurement for ranging. The position of the receiver is estimated iteratively using the spring-relaxation technique. A median and 90-percentile error of 12.4 and 21.7 mm, respectively, were obtained for measurements at 625 positions within a 1.2 × 1.2 m area testbed. The spring-relaxation technique outperforms the widely adopted linear least square-based lateration technique while using the same ranging data. The performance of the system is benchmarked against that of visible light positioning using the same platform setup. The reported results show the ultrasonic system to be more accurate when compared with the visible light positioning system, which achieved median and 90-percentile errors of 33.7 and 58.6 mm, respectively.
  • Thumbnail Image
    Item
    Novel adaptive transmission protocol for mobile sensors that improves energy efficiency and removes the limitation of state based adaptive power control protocol (SAPC)
    (MDPI AG, 15/03/2017) Basu D; Sen Gupta G; Moretti G; Gui X
    In this paper, we have presented a novel transmission protocol which is suited for battery-powered sensors that are worn by a patient when under medical treatment, and allow constant monitoring of health indices. These body-wearable sensors log data from the patient and transmit the data to a base-station or gateway, via a wireless link at specific intervals. The signal link quality varies because the distance between the patient and the gateway is not fixed. This may lead to packet drops that increase the energy consumption due to repeated retransmission. The proposed novel transmission power control protocol combines a state based adaptive power control (SAPC) algorithm and an intelligent adaptive drop-off algorithm, to track the changes in the link quality, in order to maintain an acceptable Packet success rate (PSR)(~99%). This removes the limitation of the SAPC by making the drop-off rate adaptive. Simulations were conducted to emulate a subject’s movement in different physical scenarios—an indoor office environment and an outdoor running track. The simulation results were validated through experiments in which the transmitter, together with the sensor mounted on the subject, and the subject themselves were made to move freely within the communicable range. Results showed that the proposed protocol performs at par with the best performing SAPC corresponding to a fixed drop-off rate value.