Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal design of a non-isolated high power density buck-boost converter

Semiconductor lifetime and power density are considered to be two important development directions of power converter design in the future. Especially in some high dynamic application, the operation condition is changing frequently causing the temperature swing of semiconductor, and it leads to thermal expansion and contraction which affects the lifetime or reliability of semiconductor. A high maximum temperature or a large temperature swing leads to short lifetime. Meanwhile, high power density is a general trend. If the size of a whole system is limited, a high power density converter enables more functionality of the system. Also, high power density also leads to high portability. Works have been done to use interleaved and high switching frequency converter to get a high power density. However, those converters such as interleaved buck converter do not help to improve semiconductor reliability because it can only operate in synchronous conduction mode (SCM) and triangular current mode (TCM). In this work, a $20kW$ DC/DC converter for highly dynamic application is designed. The four-switch buck-boost converter is chosen because of its flexibility and its possibility to get longer semiconductor lifetime and higher power density. It is possible to offer a free-wheeling interval in which no voltage is applied on the inductor, the transferred power can be regulated by adjusting the duty cycle of this interval. In addition, an optimal modulation scheme is proposed for this topology which further helps improve the reliability of semiconductor. An optimal power stage design is given which is a two-stage interleaved structure. Plus, a close-loop control strategy is given according to the design. The proposed modulation scheme is tested with a low voltage level prototype, and the performance prediction is verified.
Development of an immuno-mass spectrometric assay for validation of protein C inhibitor (PCI) as a biomarker for prediction of biochemical recurrence in prostate cancer patients

Supervisory Committee Dr. Terry Pearson, Supervisor (Department of Biochemistry and Microbiology) Dr. Caroline Cameron, Departmental Member (Department of Biochemistry and Microbiology) Dr. Juan Ausio, Departmental Member (Department of Biochemistry and Microbiology) Dr. Francis Choy, Outside Member (Department of Biology) Biomarker validation remains one of the most important constraints to development of new clinical diagnostic assays. To address this challenge, an immunomass spectrometric assay known as SISCAPA has been developed for quantitation of protein biomarkers in human blood. The SISCAPA assay overcomes the sensitivity barrier facing most mass spectrometric approaches by utilizing high affinity antibodies for enrichment of specific surrogate peptide analytes from complex mixtures such as trypsin-digested human plasma. However, several technological barriers remain before the SISCAPA technology gains widespread use for biomarker validation. Improvements are required in areas such as selection of high affinity anti-peptide antibodies, peptide detection sensitivity and increasing sample throughput to allow biomarker validation on large sample sets. The work presented in this dissertation describes the development of new methods for antibody selection and for high-throughput application of SISCAPA technology to biomarker measurement in human plasma. Specifically, two technological developments are described: 1) an assay called MiSCREEN was developed, which iv allows high-throughput screening of anti-peptide antibodies, enabling selection of high affinity reagents for de novo SISCAPA assays and 2) a liquid chromatography (LC)-free SISCAPA assay was developed that enables quantitation of surrogate peptides using both MALDI-TOF and RapidFire/MS platforms. Taken together, these technological advances provide a meaningful solution to the biomarker validation dilemma and allow a unified system for biomarker qualification, verification, validation and development of clinical assays for diagnosis and monitoring of a variety of diseases. To demonstrate the utility of the unified SISCAPA system for biomarker measurement, an assay was developed for protein C inhibitor (PCI) as a marker for prediction of biochemical recurrence in prostate cancer patients. The PCI-specific analyte was shown to predict biochemical recurrence of prostate cancer after radiation/hormone treatment. Early stage detection of recurrence was achieved, when compared to the ‘gold standard’ marker for prostate cancer, prostate specific antigen (PSA). Two-dimensional gel electrophoresis studies on PCI, revealed unique protein spots in a serum sample from a biochemically recurrent patient. Studying such alterations at the protein level may enable understanding of the molecular mechanisms by which PCI is involved in prostate cancer progression.
Parameterizing the seasonal–diurnal wind climate of Rome: Fiumicino and Ciampino

This study uses the Offset Elliptical Normal (OEN) mixture model to parameterize the seasonal–diurnal wind climate of Rome, Italy, for engineering design purposes, and also to reveal the physical meteorology of this “mixed” wind climate. The joint probability density function (PDF) of the wind vectors in the zonal–meridional plane are represented by a mixture of disjoint bivariate‐Normal ellipses. Each OEN ellipse is tentatively attributed to a physical wind mechanism: sea/land breezes; transient coastal jets; katabatic flows; the migrating atmospheric tide; and (non‐diurnal) geostrophic winds. The model enables a fuzzy probability of each wind mechanism to be assigned to each observation, allowing the progression of the various diurnal cycles to be tracked through time. This seasonal–diurnal parametric model is directly applicable to wind energy resource estimation, pollution studies, pedestrian comfort, and heating and ventilation design. The model is also an essential starting point in methods to simulate realistic time series of wind speed and direction.
Pybotics: Python Toolbox for Robotics

Modern robotic programming relies on offline simulation to reduce process downtime. In a virtual environment, application specialists can program, visualize, and test their robotic application before deploying it to the real production environment. This offline process saves both time and costs while increasing the safety and efficacy of the robotic application. However, to achieve a high level of fidelity between virtual and production environments, the robot system must be accurate. According to ISO 5725-1, accuracy refers to closeness or trueness of measurements to a specific value, while precision refers to the closeness of the measurements to each other. Most industrial robots are inherently precise (i.e., repeatable), but they are not necessarily accurate. One cost-effective approach to obtaining a more accurate robot is through calibration, where the actual kinematic and non-kinematic parameters of the robot model are identified and improved upon when compared to the nominal model.
Faster privacy-preserving location proximity schemes for circles and polygons

In the last decade, location information became easily obtainable using off-the-shelf mobile devices. This gave a momentum to developing Location Based Services (LBSs) such as location proximity detection, which can be used to find friends or taxis nearby. LBSs can, however, be easily misused to track users, which draws attention to the need of protecting privacy of these users. In this work, we address this issue by designing, implementing, and evaluating multiple algorithms for Privacy-Preserving Location Proximity (PPLP) that are based on different secure computation protocols. Our PPLP protocols support both circle and polygon range queries and have runtimes from a few to some hundreds of milliseconds and bandwidth requirements from a few hundreds of bytes to one megabyte. Consequently, they are well-suited for different scenarios and offer faster runtimes and savings in bandwidth and computational power as well as security improvements compared to previous PPLP schemes. In addition, the computationally most expensive parts of the PPLP computation can be precomputed in our protocols, such that the input-dependent online phase runs in just a few milliseconds.
CountMarks: Multi-finger Marking Menus for Mobile Interaction with Head-Mounted Displays

In this thesis, we present and evaluate CountMarks, a multi-touch marking menu technique designed for use with mobile head-mounted displays. CountMarks extends conventional marking menus (gesture-based radial menus) by using multi-finger input on a mobile phone screen. We discuss the design of two variations of CountMarks, exploring selection efficiency, public acceptability, and ergonomic comfort. We conduct two studies: the first compares CountMarks to a traditional marking menu. Our second study evaluates CountMarks while standing and walking and with interaction occurring on hand-held and leg-mounted devices. Our results show that CountMarks makes faster selections and allows for better search accuracy than marking menus, and can be used in the hand while standing or walking. Finally, we present an application demonstrating the implementation of CountMarks in an existing user interface and we suggest direction for future work.
Performance Analysis of Indoor Smart Environmental Control Factors: Using Temperature to Control the Rate of Formaldehyde Emission

Formaldehyde is one of the indoor air quality pollutants produced by indoor decoration. The World Health Organization has listed Formaldehyde as a primary carcinogen. Wood planks for house interior decoration are common sources of formaldehyde. Based on the technology research and development of monitoring and control of the smart home, the air quality should be effectively improved through emission and suppression. This study uses extremely unventilated student renters in Taiwan as simulation spaces. Two classes of wood plank construction materials, F1 and F3, which are classified according to as different concentrations of formaldehyde standard board grades by Taiwanese. Through three different temperature control experiments, each group of experiments use electrochemical formaldehyde detectors to measure at least two consecutive days of data to analyze the influence to which temperature, the control factor, affected formaldehyde emission rate. The results revealed that the F3 class wood planks can increase the rate of formaldehyde emission through heating, which has a significant effect on home environment control, but they need a temperature below 10 °C to barely maintain within the regulations in Taiwan. The F1 class wood planks have an emission rate of nearly zero in a low-temperature environment due to its low formaldehyde emission. The emission rate of the F1 in a high-temperature environment is not as effective as the F3. The addition of experimental data can be used as a reference basis for AI decision making, which is conducive to the establishment of indoor intelligent control systems.
Enhancement Belief Function with Ontology to Solve the Conflict for the System of Systems

: System of Systems (SoS) is a huge system consisting of small stand-alone systems to offer unique abilities. In such complex construction, managing achievement of SoS objectives along with those of its Component Systems (CS) is a challenge. Whereas the integration of CS may cause an extended impact issue called conflict. In this research, the principles of basic belief function were used to measure conflict in the SoS. This is intended to detect conflicts between CS in an attempt to resolve it. Moreover, we have based on the ontology in the SoS integration. In addition to the identification of relationships between the CS semantically. We have proposed a method to resolve conflicts called Ontology Belief Function System of Systems (OBFSoS). As a homogenous SoS, we apply the OBFSoS on Real-lime Strategy (RTS) game. We depend on the use of a learning agent. It reduces conflicts and saves time due to attempts. For execution, we got the result of 10 attempts with about 30 systems (players) in the SoS. It giving a good decision in a short time with an accuracy of approximately 87%.
More health care utilization with more insurance coverage? Evidence from a latent class model with German data

We analyse the impact of optional deductibles, private supplementary health insurance and income on the demand for health care utilization, measured as the number of physician visits with data from the German Socio-Economic Panel (SOEP). With a set of newly available variables for the years 2002, 2004 and 2006 that measure individual health more accurately and including risk-attitudes towards health we find that possible endogeneity of the insurance choice is not a problem. A latent class approach that takes into account the panel structure of the data reveals that especially individuals who have few doctor visits, the low users, respond strongest to insurance status and income. In this group we find that more insurance increases the demand for physician visits and there is a pro-rich inequity in health care utilization. No such effects are found for the high users.
Detecting Financial Statement Fraud Using Random Forest with SMOTE

The study explores the comparison of various classification models in detecting fraudulent financial statements (FFS). Due to the high-class imbalance in this unique domain, the samples chosen in existing researches tend to be processed not so realistically. Therefore Random Forest is adopted to learn imbalanced data, in addition, sampling with SMOTE. Some more effective measure metrics of performance are also added. The experimental dataset includes 11726 publicly available Chinese financial disclosures from 2007 to 2017, of which 1314 financial statements were accused of fraud by CSRC. The result shows that the Random Forest outperforms other algorithms: Artificial Neural Network (ANN), Logistics Regression (LR), Support Vector Machines (SVM), CART, Decision Trees, Bayesian Networks, Bagging, Stacking and Adaboost.
Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems

Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.
The Impact of Text Preprocessing and Term Weighting on Arabic Text Classification

This research presents and compares the impact of text preprocessing, which has not been addressed before, on Arabic text classification using popular text classification algorithms; Decision Tree, K Nearest Neighbors, Support Vector Machines, Naïve Bayes and its variations. Text preprocessing includes applying different term weighting schemes, and Arabic morphological analysis (stemming and light stemming). We implemented and integrated Arabic morphological analysis tools within the leading open source machine learning tools: Weka, and RapidMiner. Text Classification algorithms are applied on seven Arabic corpora (3 in-house collected and 4 existing corpora). Experimental results show: (1) Light stemming with term pruning is best feature reduction technique. (2) Support Vector Machines and Naïve Bayes variations outperform other algorithms. (3) Weighting schemes impact the performance of distance based classifier.
Combinatorial-Oriented Feedback for Sensor Data Search in Internet of Things

Sensor data search is an imminent component for the booming Internet of Things (IoT). Current proposals would refer to either keyword or spatial location, then the matched sensor data should be manually selected by requesters. However, the selection is arduous as there are massive connected devices. To this end, we propose a combinatorial-oriented feedback (COF) mechanism to guarantee the reliability and accessibility of feedback results via enabling an intuitive exhibition of aggregated sensor data. Two critical issues are addressed in this article: 1) sensor data combination and 2) aggregated results ranking. To facilitate the combination process, we first propose two decisive factors for aggregated result evaluation. The multiple sensor-data combinatorial (MSC) problem is converted into a multiobjective optimization model. To balance the tradeoff between competing quality metrics, we introduce Pareto sensor set (PSS) as an optimal solution for MSC problem, and devise the elitist directive breeding (EDB) method to get PSS solutions. In order to speed up the search efficiency and improve the feedback recall, we then develop the fast EDB (FEDB) algorithm, which is able to return top- ${k}$ ranked results according to different searching requirements. To demonstrate the effectiveness of COF mechanism, we conduct extensive simulations based on a virtual mobile sensing scenario. The results show that our proposed COF mechanism outperforms the state-of-the-art solutions significantly in terms of search efficiency and feedback quality, and specially, FEDB responds quite faster than EDB under the command of top- ${k}$ ranking.
Towards an interactive drone : a Bayesian optimization approach

For a drone, equipped with an impedance controller, to correctly deal with physical contact and interaction with the environment, the impedance parameters should be adjusted correctly depending on the interaction task. Concentrating on interaction tasks requiring a constant set of impedance parameters values throughout operation. A model-free learning framework is proposed to automatically find the suitable parameters values. The framework relies on Bayesian optimization and episodic reward calculation requiring the drone to repeatedly perform a predetermined task in the environment actively searching in the impedance parameters space. The sample-efficiency and safety of learning were improved by adding two novel modifications to Bayesian optimization. The first one is local optimization of acquisition functions allowing learning to get early warnings during exploration. The second one is conditioning the reward signal with a logistic function exploiting the initial knowledge and enabling generalizing learning settings to different situations. The proposed technique was validated by applying it to learn the suitable impedance parameters values for a simulated drone performing sliding tasks. The results show that the proposed framework is able to automatically find suitable impedance parameters values in different situations given the same initial knowledge and that the learned parameters values can be generalized to similar interaction tasks.
Multi-Label-Based Similarity Learning for Vehicle Re-Identification

The massive attention to the surveillance video-based analysis makes the vehicle re-identification one of the current hot areas of interest to study. Extracting discriminative visual representations for vehicle re-identification is a challenging task due to the low-variance among the vehicles that share same model, brand, type, and color. Recently, several methods have been proposed for vehicle re-identification, that either use feature learning or metric learning approach. However, designing an efficient and cost-effective model is significantly demanded. In this paper, we propose multi-label-based similarity learning (MLSL) for vehicle re-identification obtaining an efficient deep-learning-based model that derives robust vehicle representations. Overall, our model features two main parts. First, a multi-label-based similarity learner that employs Siamese network on three different attributes of the vehicles: vehicle ID, color, and type. The second part is a regular CNN-based feature learner that employed to learn feature representations with vehicle ID attribute. The model is trained jointly with both parts. In order to validate the effectiveness of our model, a set of extensive experiments has been conducted on three of the largest well-known datasets VeRi-776, VehicleID, and VERI-Wild datasets. Furthermore, the parts of the proposed model are validated by exploring the influence of each part on the entire model performance. The results prove the superiority of our model over multiple state-of-the-art methods on the three mentioned datasets.
Online Spectrum Partitioning for LTE-U and WLAN Coexistence in Unlicensed Spectrum

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.