Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum)(Elsevier, 2014-03) Torres-Aviles F; Romeo JS; Lopez-Kleine LBackground Molecular mechanisms of plant–pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.Item Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired from Thermopiles and Machine Learning(IEEE, 4/06/2021) Faulkner N; Alam F; Legg M; Demidenko SThe development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.Item A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks(IEEE, 20/01/2021) Aslam S; Alam F; Hasan SF; Rashid MAClustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.Item SpringLoc: A device-free localization technique for indoor positioning and tracking using adaptive RSSI spring relaxation(Institute of Electrical and Electronics Engineers (IEEE), 5/05/2019) Konings D; Alam F; Noble F; Lai EDevice-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.Item Let the powers combine(Elsevier, 5/01/2016) Allison JRLipoproteins play a variety of roles in bacterial physiology and virulence. Correct localization is essential for lipoprotein function, yet the mechanisms by which this occurs are not yet fully understood. In this issue of Structure, East et al. (2016) describe the factors that govern secretion of the PulA lipoprotein.Item CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection(IEEE Xplore, 12/10/2020) Faulkner N; Parr B; Alam F; Legg M; Demidenko SPassive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.Item Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record(Oxford University Press (OUP), 30/08/2017) Walonoski J; Kramer M; Nichols J; Quina A; Moesel; Hall D; Duffett C; Dube K; Gallagher T; McLachlan SObjective: Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. Materials and Methods: We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. Results: Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. Discussion: Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. Conclusion: Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.Item IoT Big Data provenance scheme using blockchain on Hadoop ecosystem(BioMed Central Ltd, 2021-12) Honar Pajooh H; Rashid MA; Alam F; Demidenko SThe diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).Item Dual-band waveform generator with ultra-wide low-frequency tuning-range(Institute of Electrical and Electronics Engineers (IEEE), 22/04/2016) AL-DARKAZLY IAA; Hasan SMRThis paper presents a novel mixed-signal low-power dual-band square/triangular waveform generator (WFG) chip with a wide low-frequency tuning range for medical bio-electric stimulation therapy. It consists of a relaxation oscillator comprising a hysteresis Schmitt trigger and a timing integrator, along with frequency divider (FD) stages and path selector output for driving an electrode from 16 selectable channels. It was fabricated using Global Foundries 8RF-DM 130-nm CMOS process with a supply voltage of ±1 V for the oscillator and +1 V for logic circuits. The WFG provides an output of around 1.5 Vp-p at a nominal low oscillation frequency of 17 kHz using small-size on-chip passive components of values 10 kΩ and 10 pF. The WFG core (band I) can be tuned in the range 6.44-1003 kHz through bias current adjustment, while a lower frequency (band II) in the range 0.1 Hz-502 kHz can be provided digitally through a 2 stage. The power consumption was only 0.457 mW for the WFG and 2.1 mW for the FD circuit while occupying a total silicon area of only 18 426 μm2.Item A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand(Institute of Electrical and Electronics Engineers, 23/08/2022) Saleem MH; Potgieter J; Arif KDeep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.
