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A full sharding scheme for Consortium blockchain in a zero-trust, shared resource setting
No full textBlockchain technology has emerged as a response to the growing demand for trust, transparency, and security in the realm of digital financial transactions, in which Bitcoin serves as an initial prime example. It offers an immutable, decentralised ledger system to verify and record transactions without a central authority, indicating a paradigm shift in traditional financial systems. Blockchain is a pioneering topic including a set of technologies that have the potential to revolutionise, improve, or even substitute existing technologies, particularly focusing on its implications and applications in the financial transaction sector. The inherent properties of Blockchain, such as decentralisation, immutability, and transparency, are capable of resolving several prevailing issues in the financial transaction industry. Blockchain can streamline financial processes, increase efficiency, reduce fraud, eliminate intermediaries, and provide unprecedented levels of security and privacy in transactions. Moreover, it fosters the potential to significantly reduce costs and accelerate transaction speed, enhancing the overall efficiency of the financial ecosystem
Preliminary characterisation of an active CMOS pad detector for dosimetry in HDR Brachytherapy
No full textThis thesis aims to characterise the accuracy of a new active CMOS pad detector for dosimetry in HDR Brachytherapy. A 1x1x1 mm3 detector is doped with high resistivity n-type silicon, thickness of 100 μm (Wafer10) and 48 μm (Wafer20) epitaxial substrate and built in standard CMOS along with a transimpedance amplifier. This work investigates and examine this detector’s sensitivity and signal to noise ratio in air for use in High Dose Rate Brachytherapy (HDR BT) dosimetry. To understand operational characteristics of the detector, a current-voltage (I-V) characteristic curve was created, and the depletion voltage of the junction observed by using a Keithley Programmable Voltage Source to sweep the voltage and measure the current collected at the pad. Applying the established depleted voltage, the detector sensitive volume was tested by irradiating 5.5 MeV alpha beam with field size 1 mm x 1 mm at 1000 Hz event rate to observe the impact of radiation on the electronic and collection efficiency of the detectors. Source tracking was performed in the clinic by dwelling HDR Ir-192 Flexisource (Elekta, Veenendaal, the Netherlands) at different depths and using the DDC226 interface to track the charge absorption. The sensitivity and responsiveness were assessed and a conversion factor in nC/Gy was calculated for use in further data analysis. The minimum voltage found to produce full charge collection efficiency is −30 ± 0.1 for both Wafer10 and Wafer20. A HDR brachytherapy Ir-192 Flexisource (Elekta, Veenendaal, the Netherlands) was used for source linearly measurements and a dose comparison were made to AAPM TG-43. A variation of within 5% between measured absorbed dose and TG-43 was found corresponding to the difference of +/-2cGy. Higher dose differences were measured for 2 mm and 7 mm depths due to scattering and radiation field perturbation created by the ceramic/golden package adopted for assembling the sensor prototypes. The accuracy in reproducing dwell positions lies within 1 mm compared to actual distance set by TPS. The sensitivity found for Wafer10 is (5.8 ± 0.021) x 10-8 nC.Gy-1.mCi-1.mm-2 and (3.6 ± 0.14) x 10-8 nC.Gy-1.mCi-1.mm-2 for Wafer20. This supports the correlation between sensitivity and the material’s electronical and architectural design. The response of the detector when irradiated using HDR Ir-192 placed in water equivalent phantom obtained within 0.1% of the time set by TPS. Due to the structure, design, and air gap within the space of the sensor region, scattering and field perturbation prevents the gamma reaching the sensor resulting in high undervaluation of variation in the depth dose response compared to TG-43 for 2mm and 7mm. But for larger depths of 12 to 52mm, the variation is within 5% compared to TPS corresponds to the difference of +/-2 cGy. Further investigation of angular dependence and field perturbation would be beneficial to confirm the proposed detector is suitable for clinical applications such as quality assurance and dosimetry in HDRB
Metamorphic Relations for Testing Automated and Autonomous Driving Systems and Simulation Platforms
No full textAdvanced Driver Assistance Systems (ADASs) and Autonomous Vehicles (AVs) are becoming increasingly popular. These technologies have the potential to revolutionise transportation, but they also face a number of challenges in terms of safety. Simulation testing —– the use of simulations for testing –— is essential when developing ADAS and autonomous driving (AD) systems. It plays a vital role in augmenting and speeding up real-world testing. It is reproducible, scalable and reduces development costs. However, a challenge for simulation testing can occur when determining whether or not the SUT or the simulator correctly produces the intended behaviors. The effectiveness of simulation testing depends on the simulation platform’s quality: if the simulation platform is unreliable, the simulation findings could be inaccurate and, therefore, useless. The oracle problem is a circumstance where it can be too difficult (or expensive) to verify or validate the simulation platforms due to a lack of appropriate test oracles or ways to establish the validity of the program output or behavior. The use of testing standards and protocols is essential for designing and developing autonomous systems. However, a lack of common testing mechanisms and strategies makes it challenging for testers and industries to achieve their targets. One of the popular methods to alleviate the oracle problem is Metamorphic Testing. When applying MT, the identification of Metamorphic Relations is critical. This thesis aims to provide a simulation-based-MT approach by identifying a series of MRs to implement testing standards and protocols. The empirical studies were conducted on the Verification and Validation (V&V) of three popular driving simulators for ADAS development (MATLAB & Simulink, 51Sim-one Cloud, and esmini) and examining their embedded ADAS systems. This research conducts case studies using existing ADAS testing standards, such as the European New Car Assessment Programme (Euro NCAP), and major simulation testing platforms including MATLAB Simulink, 51Sim-One, and Esmini. These case studies find that combining existing testing standards with MRs can not only alleviate the oracle problem of the former but also help to detect previously unknown software issues in both the simulation platforms and the ADAS software. The software vendors have confirmed our reported issues. This thesis also relates the concept of MT to ISO standards including ISO 34502 (Road vehicles—Test scenarios for automated driving systems—Scenario based safety evaluation framework) and ISO 21448 (Road vehicles—Safety of the intended functionality). In the software engineering literature, although MT has already been applied to the testing of simulation software and AD systems, to the best of our knowledge, this is the first research that investigates specifically AD simulation software as well as the AD systems
Mode Selection Methods for Wireless Powered IoT Networks
No full textInternet of Things (IoT) networks have gained significant attention in recent years as it has the potential to transform various industries. A key concern, however, is that sensing devices have limited operational lifetime. Specifically, they have finite energy, which affects the amount of data they are able to collect and upload. One solution is to power these devices wirelessly, where devices harvest energy from Radio Frequency (RF) signals from transmitters such as a Hybrid Access Point (HAP). A key issue, however, is that energy delivery and data transmissions may be conducted on the same frequency band. This means a HAP has to determine a transmission schedule for energy or/and data transmissions. Another issue is that the channel gain of devices varies over time, which affects the amount of harvested energy and transmitted data. In this respect, a challenging issue is that an HAP has causal channel state information only, meaning it is not aware of energy arrivals or channel gains nor the data rate of devices in future time slots. Given the above issues, this thesis first proposes a novel mode-based structure for an RF-powered IoT network. Specifically, an HAP operates either in downlink mode to charge devices, or in uplink mode to collect data from them. Unlike previous works which only consider one time slot, this thesis aims to maximize the amount of collected data over multiple time slots. The problem at hand is to determine the mode of each time slot. To do this, this thesis presents a novel rolling horizon algorithm. In particular, it uses channel estimates and an Integer Linear Program (ILP) to determine the operating mode of a HAP in each time slot. This thesis then studies a multi-antenna SIC-enabled HAP that uses the said mode structure. Specifically, the HAP is able to receive multiple transmissions simultaneously and charge devices via beamforming. In this respect, the problem is to schedule a set of devices to transmit in uplink slots, and determine antenna weights in downlink slots. To do this, this thesis employs a rolling horizon algorithm to solve a Mixed Integer Linear Program (MILP) to determine its operating mode in each time slot. In addition, this thesis also proposes a data-driven approach to take advantage of the massive computational power at data centers to construct neural networks. Specifically, in the offline stage, the approach generates exhaustive collection of channel gain scenarios and stores the optimal mode of each time slot for each scenario in neural networks. In the online stage, for a given channel gain realization, the HAP then uses these neural networks to retrieve the operating mode of a slot. After that, it solves an LP to determine the optimal beam weight of each antenna in downlink slots, and uses a greedy strategy to determine transmitting devices in uplink slots. Lastly, this thesis studies downlink and uplink transmissions, where devices receive both energy and data from a HAP via power splitting. Further, Rate Splitting Multiple Access (RSMA) is used for data transmissions. The problem at hand is to determine (i) the mode of each slot over a planning horizon, (ii) the transmit power allocated for each packet, (iii) the power splitting ratio of each device in downlink slots, and (iv) the decoding order used by the HAP in uplink slots. This thesis first uses a Markov Decision Process (MDP) to model the said problems. Then it proposes a Q-learning based approach to determine the mode of each time slot in order to maximize the number of data transmissions over multiple time slots. Then in each downlink and uplink slot, the HAP uses linear programs to determine the transmitted power of each packet, the power splitting ratio of each device and the decoding order of uplink packets
Cuntz-Krieger algebras associated to self-similar groupoids
No full textIn this thesis, we study self-similar actions of groupoids on row-finite directed graphs and their associated Cuntz-Krieger algebras. Roughly speaking, if for all scales of parts of the object reiterate the whole, then the object is self-similar. For algebraic objects such as groups or groupoids that have this self-similarity property, we simply call them selfsimilar groups or self-similar groupoids. As an illustration of this self-similarity property, we recall the addition algorithm of integers that we learned from primary school, there is a so-called carrying operation that takes place to deal with addition of larger integers. Analogous to this carrying operation there is so-called a restriction map that encodes self-similarity property. In the 1980s, Grigorchuk, Gupta, and Sidki were among the pioneers who introduced the concept of self-similar groups to address the question of whether there exist groups with intermediate growth. These groups exhibit self-similarity in their actions on the path-spaces of graphs with a single vertex. In contrast, Laca, Raeburn, Rammage, and Whittaker (2018) extended this notion to encompass self-similar actions on more general directed graphs by introducing the concept of a self-similar groupoid. A self-similar groupoid is defined as a system of partial isomorphisms of the path-spaces of a finite directed graph. Laca et al. (2018) explored self-similar groupoids and their associated C∗-algebras to investigate the KMS states on their corresponding dynamical systems. In contrast to their approach, which employed Hilbert modules and Cuntz-Pimsner algebras, we focus solely on generators and relations, as well as the associated Cuntz-Krieger algebras. Moreover, we extend our analysis to a broad class of self-similar groupoids, with particular emphasis on the ideal structure of their associated C∗-algebras
The mechanism of neuroprotective and anti-aging effects of cannabidiol
No full textCannabidiol (CBD) is the primary non-intoxicating compound from the Cannabis sativa plant, which shows a wide range of benefits in aging and age-related disorder like Alzheimer’s disease (AD). However, the mechanisms according to these responsibilities remain unclear. This thesis aims to investigate the mechanisms of CBD underlying neuroprotective and anti-aging effects
Targets Coverage and Data Computation in Energy Harvesting IoT Networks
No full textTo date, a wide range of industries leverage the Internet of Things (IoT) to improve efficiency and productivity. Briefly, an IoT network consists of wireless sensor devices that are used to monitor an environment, provide coverage, or collect data of targets. The collected data can then be processed before it is uploaded to a sink/gateway or the cloud to be used by various applications. The full potential of IoT, however, is limited by a number of issues. First, most IoT networks are designed for a specific application. This motivates the use of virtualization technologies, where the same network substrate runs multiple applications concurrently. In particular, devices or network operators virtualize resources such as energy, memory, and/or computational time/cycles, which in turn allows these resources to be shared by virtual network functions (VNFs) belonging to different applications. The second issue concerns energy. Specifically, the limited amount of energy on sensor devices governs the amount of data collected or processed by an IoT network. To this end, this thesis considers a number of approaches to overcome the energy limitation of devices. The first approach is to employ imprecise computation to trade-off the quality of computation result with the energy consumed by devices. Another approach is to apply energy harvesting techniques to harness ambient energy, e.g., solar. In this respect, a key consideration is battery property, meaning devices have to control their charging and discharging operations to avoid non-ideal properties, such as memory effects
Single-molecule and single-cell studies of pro- and anti-mutagenic DNA repair in live Escherichia coli cells
No full textAll organisms incur damage to their DNA in the course of their daily lives and have developed sophisticated mechanisms for DNA repair. Many of the enzymes involved in these DNA repair pathways have been identified and their biochemical activities are well understood. However, there is a relatively poor understanding of how these enzymes are put into play in response to DNA damaging agents. As bacterial resistance to antibiotics has increasingly become a major world health problem, it is imperative to understand how DNA damage induced by antibiotics interacts with bacterial DNA repair systems. Detailed understanding of the molecular mechanisms of DNA repair could allow for the development of new antibiotics and, just as importantly, allow us to identify the sources of antibiotic-resistance mutations. The goal of this thesis is to better understand the anti- and pro-mutagenic DNA repair pathways in live Escherichia coli cells. The primary focus is on the interplay between the error-prone DNA polymerases and their binding partners. There is also significant focus on the resolution of toxic DNA repair intermediates. Utilising single-molecule fluorescence live-cell imaging, it has been possible to monitor the activities of proteins involved in these pathways in real time to gain new insights into their behaviour
Regulation of Food Safety by the National Agency for Drug and Food Control (NADFC) in Indonesia: A Quest for Effectiveness
No full textFood safety is an alarming issue in Indonesia which has enacted numerous laws and regulations and established several regulatory bodies to regulate unsafe food. Nonetheless, the regulation remains largely ineffective. This study investigates the effectiveness of food safety regulation with a special reference to the performance of the National Agency for Drug and Food Control (NADFC) as the main body entrusted with the responsibility to conduct this regulation. This thesis presents qualitative research carried out by employing a desktop study method that involves collating and evaluating available information from various sources. The rules and regulations governing food safety in Indonesia are examined viewing through their equivalents mainly in New South Wales, Australia (NSW). NSW has been chosen because of its wider recognition as a successful regime in regulating food safety
Few-Shot Audio Classification Model for Detecting Classroom Interactions Using LaSO Features in Prototypical Networks
No full textThis research introduces an innovative approach to few-shot sound classification applied to classroom sound recordings that integrates Label Set Operation (LaSO) features with Prototypical Networks. Traditional audio classification methods often require extensive labeled datasets, which can be impractical in real-world scenarios where obtaining large amounts of labeled audio data is challenging. This is particularly the case for the target application of automatically annotating long recordings of classroom audio to understand student learning in classrooms. This paper proposes an enhanced few-shot learning approach based on Prototypical Networks by incorporating LaSO features, to augment the feature space for the Prototypical Network. This methodology focuses on detecting and classifying teacher and student voices for future understanding and analysis of classroom interactions. Experimental results indicate the proposed approach incorporating LaSO features significantly improves classification accuracy of a prototypical network used for few-shot learning. This work paves the way for more advanced and automated solutions in educational environments, facilitating better monitoring and understanding of classroom dynamics