Miracle-dz/UniFAR-FTUs huggingface dataset:
Queries, Data, and Statistics: Pick Two

The query processor of a relational database system executes declarative queries on relational data using query evaluation plans. The cost of the query evaluation plan depends on various statistics defined by the query and data. These statistics include intermediate and base table sizes, and data distributions on columns. In addition to being an important factor in query optimization, such statistics also influence various runtime properties of the query evaluation plan. This thesis explores the interactions between queries, data, and statistics in the query processor of a relational database system. Specifically, we consider problems where any two of the three—queries, data, and statistics—are provided, with the objective of instantiating the missing element in the triple such that the query, when executed on the data, satisfies the statistics on the associated subexpressions. We present multiple query processing problems that can be abstractly formulated in this manner. The first contribution of this thesis is a monitoring framework for collecting and estimating statistics during query execution. We apply this framework to the problems of monitoring the progress of query execution, and adaptively reoptimizing query execution plans. Our monitoring and adaptivity framework has a low overhead, while significantly reducing query execution times. This work demonstrates the feasibility and utility of overlaying statistics estimators on query evaluation plans. Our next contribution is a framework for testing the performance of a query processor by generating targeted test queries and databases. We present techniques for data-aware query generation, and query-aware data generation that satisfy test cases specifying statistical constraints. We formally analyze the hardness of the problems considered, and present systems that support best-effort semantics for targeted query and data generation. The final contribution of this thesis is a set of techniques for designing queries for business intelligence applications that specify cardinality constraints on the result. We present an interactive query refinement framework that explicitly incorporates user feedback into query design, refining queries returning too many or few answers. Each of these contributions is accompanied by a formal analysis of the problem, and a detailed experimental evaluation of an associated system.
Boosting Ranging Performance of LiDAR Using Multi-Pulse Coherent Average

To boost the ranging performance (precision, detection range, and accuracy) of analog-to-digital converter-based (ADC) LiDAR, a multi-pulse coherent average (MCA) method was utilized to improve signal-to-noise ratio (SNR) and consequently enhance the ranging precision. Based on a self-developed LiDAR system, extensive simulations and experiments were performed to verify the theory analysis and demonstrate the following points. First, the MCA method can greatly enhance the root mean square (rms) ranging precision from 11.7 to 1.3 cm. Yet, it cannot improve ranging accuracy. Second, improved ranging precision depends mainly on the selection of the accumulative pulse number (APN) included in the MCA. Given the original pulse signal SNR of 39 dB, the ranging precision can be improved from 3.9 cm to less than 0.5 cm, when APN is set to 85. Third, increased APN will generally enhance ranging precision, within limits.
Analyses of Classifier’s Performance Measures Used in Software Fault Prediction Studies

Assessing the quality of the software is both important and difficult. For this purpose, software fault prediction (SFP) models have been extensively used. However, selecting the right model and declaring the best out of multiple models are dependent on the performance measures. We analyze 14 frequently used, non-graphic classifier’s performance measures used in SFP studies. These analyses would help machine learning practitioners and researchers in SFP to select the most appropriate performance measure for the models’ evaluation. We analyze the performance measures for resilience against producing invalid values through our proposed plausibility criterion. After that, consistency and discriminancy analyses are performed to find the best out of the 14 performance measures. Finally, we draw the order of the selected performance measures from better to worse in both balance and imbalance datasets. Our analyses conclude that the F-measure and the G-mean1 are equally the best candidates to evaluate the SFP models with careful analysis of the result, as there is a risk of invalid values in certain scenarios.
Fast Improved Thin-Film Transistors with Atomic-Layer-Deposited In2O3 Channels via O2 Plasma Treatment

High performance thin-film transistors (TFTs) with atomic-layer-deposited In<inf>2</inf>O<inf>3</inf> channel and Al<inf>2</inf>O<inf>3</inf> gate dielectric were fabricated under a maximum process temperature of 200°. As-fabricated and O<inf>2</inf> annealed devices show a conductor-like behavior because of very high carrier concentration in the channels. Compared to the O<inf>2</inf> annealing, the O<inf>2</inf> plasma treatment can be a much more efficient approach for fast improvement of TFTs by substantially reducing the amount of oxygen vacancies. The In<inf>2</inf>O<inf>3</inf> TFT treated by O<inf>2</inf> plasma demonstrates a field-effect mobility of 11 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup>, a threshold voltage of 0.9 V, a subthreshold swing of 0.38 V/dec, and an on/off current ratio of 10<sup>7</sup>. Moreover, the device stability is enhanced markedly if the treatment temperature increases. This could be mainly due to the decrease of -OH/CO groups in the In<inf>2</inf>O<inf>3</inf> film, which is evidenced by X-ray photoelectron spectroscopy.
A High Accuracy and Wide Input Voltage Range Current Sensor with Adaptive Amplification and Temperature Compensation

This paper presents a high accuracy current sensor based on CMOS $0.18\mu \mathrm{m}$ technology. A specially designed current sensing amplifier (CSA) is used to achieve high accuracy (0.10% gain error at room temperature) and wide input voltage range (maximum input common-mode voltage is 46V). In addition, with the temperature compensation, gain error can be controlled under 0.77%. A second-order delta-sigma ADC is used to convert analog signals to digital signals. Moreover, in order to reduces the effective number of bits (ENOB) and increase the ADC minimum resolution voltage, this paper proposes an adaptive amplification subsystem. At last, the power consumption of the whole circuit is controlled in $8.97\mu \mathrm{W}. $
Optimization method based on simplex algorithm for current control of modular multilevel converters

The paper proposes an optimization formulation of the control problem for modular multilevel converter (MMC). The main control stage computes arm voltages on average over a fixed switching period by minimizing control errors in order to satisfy as best as possible the desired references of input, circulating and output currents, while taking into account arm voltage limits. Then, mean-values of required arm voltages are achieved by phase shift pulse-width modulation (PSPWM) by computing duty cycles for each submodule while taking into account the issue of active balancing of the capacitor voltages in a secondary control stage. The proposed optimization problems are solved by using a numerical method based on the simplex algorithm and simulation results are shown in order to support the validity of the approach.
Speed Sensorless Model Predictive Control Based on Disturbance Observer for Induction Motor Drives

Model predictive control (MPC) is emerging as a powerful control scheme for high performance control of induction motor (IM) drives due to its fast torque response. In this paper, a full-order observer-based speed sensorless MPC method is studied. However, in conventional MPC design, the torque reference in the cost function is generated by the proportional-integral (PI) speed controller, thus the torque reference’s generation rate is not fast and its accuracy is low especially when the load torque is given suddenly and the inertia value is varying. This paper investigates a speed sensorless model predictive control based on disturbance observer (DOB) for induction motor drives. The disturbance observer is used as the feed-forward compensation part for the PI controller, and it not only improves the torque reference’s rate, but also enhances the robustness against load disturbance and inertia variations. The simulation results verified the effectiveness of the proposed method.
Developing MEMS Electric Current Sensors for End-use Monitoring of Power Supply: Part IX – Threshold-Triggered Current Sensing

A passive type MEMS DC current sensor for monitoring the electricity consumption by either one-wire or two-wire appliance cord was proposed, micro-fabricated and tested in our past work (DTIP 2011, 2012). A novel oscillating type MEMS DC current sensor, comprised of both actuating and sensing elements, was further proposed for constant DC current measurement (DTIP2013). The relationship between magnetic induction and sensitivity of the DC current sensor was discussed both analytically and experimentally (DTIP2017). In this work, exploratory study on threshold-triggered AC current sensing mechanism by making use of bistability applicable to two-wire AC appliances is proposed in this work. It can monitor the AC current whether reaches a critical level when it is used for AC current. Only when the monitored amplitude of AC current reaches a preset threshold, the bistable piezoelectric cantilever sensing system will transform from intrawell oscillations to interwell oscillations, realize snap-through and give out a high output.
SiC Power Device Evolution Opening a New Era in Power Electronics

A new era in power electronics has been just opened by commercial introduction of silicon carbide (SiC) MOSFETs in a variety of power electronic systems such as power supplies, renewable energy, transportation, heating, robotics, and electric utility transmission/distribution. The utilization of SiC power devices in these systems can enable significant energy saving because of much lower power-loss devices compared to conventional silicon (Si) power devices. In this paper the progress of SiC power transistors is presented in comparison with Si MOSFETs and IGBTs.
Die Attachment Material based on Micro AG Paste for the Application of High Powder Devices

Sintered Ag paste having the high temperature stability and high thermal/electrical conductivity are considered to be the most promising candidate as die-attaching materials for high power devices. In this work, a new micro Ag paste including three kinds of Ag particles with different diameter and morphology was developed. It was easily sintered at low temperature of 200 °C to achieve a low resistivity of 4.1 μΩ·cm. High mechanical strength over 20 MPa was realized at only 180 °C for 30 minutes without any pressure, the strength was improved to over 40 MPa at 250 °C. The high performance corresponds to the dense microstructure due to the size-matching of Ag particles.
Virtual Flux Oriented Sensorless Direct Power Control of QZS Inverter Connected to Grid for Solar PV Applications

Impedance source inverters (Z-source and Quazi-Z-source (QZS)) are the promising converter topologies in the future generation because of the buck, boost and buck-boost modes of operation. In this paper, three phase voltage sensorless grid connected QZS inverter is proposed to inject Maximum Direct Power. The new system uses the Virtual Flux Oriented Control (VFOC) algorithm for current control and virtual Flux (VF) estimation method for grid voltage. The complete Virtual Flux direct Power control (VFOC-DPC) exemplify the performance and operation of the proposed system compared to the conventional DPC algorithm is presented. Proposed strategy is also investigated under predistorted and unbalance grid conditions. VFOC-DPC provides numerous benefits mainly providing balanced sinusoidal line currents when the supply voltage is unbalanced. Simulation results are presented under unbalanced and pre-existing harmonic content in the grid voltage to verify the VF estimation of grid voltage. Tracking of active- reactive power is accurately done with current control loops providing good steady state and transient response to step change in reference.
V2X based Probabilistic Cooperative Position Estimation Applying GNSS Double Differences

Future applications for connected and automated driving depend on high-precision, lane selective positioning especially in dense urban environments. Estimating a user's position is often based on Global Navigation Satellite Systems (GNSS), but stand-alone GNSS positioning methods do not meet the necessary performance requirements. To achieve higher accuracies, additional sensor information is usually incorporated. Recent trends to enhance GNSS based positioning have focused on Cooperative Positioning (CP)approaches which allow the elimination of correlated GNSS error terms. The work presented in this paper provides a Dedicated Short Range Communication (DSRC)enhanced CP scheme using IEEE 802.11p and low-cost, multi-constellation GNSS receivers. A proposal for integrating GNSS raw data exchange through DSRC is given. An Extended Kalman Filter (EKF)performing GNSS Double Differencing (DD)is used as positioning algorithm and is compared to a conventional Least Squares Estimator (LSE). The proposed method is described in detail and validated in an experimental, dynamic measurement scenario.
Accelerating Vector Quantization Based Speaker Identification

Matching of feature vectors extracted from speech sample of an unknown speaker, with models of registered speakers is the most time consuming component of real0time speaker identification systems. Time controlling parameters are size and count of extracted test feature vectors as well as size, complexity and count of models of registered speakers. We studied vector quantization (VQ) for accelerating the bottlenecking component of speaker identification which is less investigated than Gaussian mixture model (GMM). Already reported acceleration techniques in VQ approach reduce test feature vector count by pre0quantization and reduce candidate registered speakers by pruning unlikely ones, thereby, introducing risk of accuracy degradation. The speedup technique used in this paper partially prunes VQ codebook mean vectors using partial distortion elimination (PDE). Acceleration factor of up to 3.29 on 630 registered speakers of TIMIT 8kHz speech data and 4 on 91 registered speakers of CSLU speech data is achieved respectively.
Application of Ultra-Wideband Double Layer Printed Antenna for Partial Discharge Detection

Partial discharge (PD) is a local electrification phenomenon that partially connects insulation between the conductors and occurs either on the surface of the conductor or inside the insulation (void). During the PD there are several phenomena that accompany the occurrence of PD, such as impulse currents, heat radiation, electromagnetic waves, mechanical waves and chemical processes. This phenomenon is detected and measured to know the existence of PD. One of the PD measurements is ultra high frequency (UHF) method, by measuring the waves generated by PD using antenna. One of antenna having good characteristics is UWB double layer printed antenna. In this paper the application of ultra-wideband double layer printed antenna for partial discharge detection is reported. The application of antenna on PD measurement, shows that the antenna is able to detect PD. The characteristics of PD: PDIV, PDEV, PD waveform are measured using this antenna. Ultra-wideband (UWB) double layer printed antenna is an antenna developed from a square microstrip antenna with symmetrical T-shaped tethering. The proposed antenna is implemented on Epoxy FR-4 substrate with permittivity of 4.3, thickness of 1.6mm, and 72.8mm x 60.0mm in size. The VNA testing of the antenna shows that the antenna bandwidth is from 50MHz to 2.30GHz. The measured results of PD wave are PDIV, PD waveform and PDEV.
Burst Topic Detection in Real Time Spatial–Temporal Data Stream

In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial–temporal data stream and integrates local topic detection with global topic detection to find the spatial–temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness.
Data Aggregation in Software-Defined Wireless Sensor Networks: A Review

Wireless Sensor Networks (WSN) have made a substantial impact in our lives by allowing us to communicate, monitor certain environments thus enabling us to build systems such as software-defined wireless sensor networks (SDWSN) which are expected to contribute a lot on the design of many applications in future. Software-defined wireless sensor networks decouple the control plane and data plane. The control plane serves as the brain of the system while the data plane performs functions such as data aggregation, packet generation and transmission in the programmable nodes. Flow aggregation allows a limited number of flow entries to be reduced while maximizing utilities. It also improves the energy consumptions, security, overall throughput performance and network lifetime. Therefore, this paper investigates data aggregation in network technologies such as WSN and SDN and proposes flow aggregation for control packets in software-defined wireless sensor networks.
Path tracking control for a robot-trailer system in the presence of measurement bias

In this paper, the authors design a controller for a robot-trailer system to solve the path following problem. A kinematic state-space model is derived using tractor steering angle rate as system input so that undesirable effects of input disturbances can be reduced. A tracking error of the trailer position may exist when there are biases on the measurements of heading angles. An improved integral separation combined with linear quadratic regulator (LQR) is designed for the system to remove the trailer position error. The tractor relative location is used to estimate whether the system is under steady state. The controller with integral action can be difficult to tune, so a genetic algorithm (GA) is then used to find optimal LQR controller parameters. Simulation results verify the approaches above.
A High Boost Bi-directional Series Z-source Inverter With Novel Switched Inductor Cells

The conventional Z-source inverter (ZSI) has the following disadvantages: unidirectional power flow, high start-up inrush current, presence of abnormal working state, and low boost factor. To solve the aforementioned problems, a high boost bi-directional series ZSI with novel switched inductor cells is proposed. The proposed novel switched inductor (SL) cell replaces three diodes in conventional switched inductor (SL) cell of the series ZSI with two capacitors and one active switch, thus allowing bi-directional power flow. Besides, the Z-source diode is also replaced by a full controlled switch. The simulation and experimental results indicate that the proposed boost ZSI has the following merits: high boost factor, soft-start capability, bidirectional power flow capability, and elimination of abnormal working mode under low power factor condition.
A Ka-Band Low Noise Amplifier for Phased Array Radar System in 65nm CMOS

An optimized Ka-band low noise amplifier (LNA) for phased array radar system is presented in this paper. Two gain-boosting techniques are quantitatively analyzed and employed on the proposed LNA to achieve high gain and low noise performances. Meanwhile, three switched-capacitor arrays are inserted to prevent the center frequency (35GHz) from shifting with the PVT variations. Implemented in 65nm CMOS, the presented LNA features 26.2dB gain over 7GHz-3dB bandwidth (BW-3dB) while drawing 24.5mA current from one 1V power supply. The noise figure (NF) is lower than 3.4dB at 35GHz. S11 and S22 are below -10dB within the entire operating frequency band (33GHz-37GHz). The input third-order intercept point (IIP3) is higher than -22dBm.
Reducing the wcet of applications on low end embedded systems

Applications in embedded systems often need to meet specified timing constraints. It is advantageous to not only calculate the Worst-Case Execution Time (WCET) of an application, but to also perform transformations that attempt to reduce the WCET, since an application with a lower WCET will be less likely to violate its timing constraints. A compiler has been integrated with a timing analyzer to obtain the WCET of a program on demand during compilation. This environment is used to investigate three different types of compiler optimization techniques to reduce WCET. First, an interactive compilation system has been developed that allows a user to interact with a compiler and get feedback regarding the WCET. In addition, a genetic algorithm is used to automatically search for an effective optimization phase sequence to reduce the WCET. Second, a WCET code positioning optimization has been investigated that uses worst-case path information to reorder basic blocks so that the branch penalties can be reduced in the worst-case path. Third, WCET path optimizations, similar to frequent path optimizations, are used to reduce the WCET. There are several contributions to this work. To the best of our knowledge, this is the first compiler that interacts with a timing analyzer to use WCET predictions during the compilation of applications. The dissertation demonstrates that a genetic algorithm search can find an optimization sequence that simultaneously improves both WCET and code size. New compiler optimizations have been developed that use WC path information from a timing analyzer. The results show that the WCET code positioning algorithm typically finds the optimal layout of the basic blocks with the minimal WCET. It is also shown that frequent path optimizations can be applied on WC paths using worst-case path information from a timing analyzer to reduce WCET. These new compiler optimizations described in this dissertation not only significantly reduce WCET, but also are completely automatic.
Sentiment Analysis Based on Appraisal Theory for Assessing Incumbent Electability

Sentiment analysis is a useful study for determining opinions by classifying text. The document used in the research comes from Twitter about public opinion about community satisfaction related to performance of incumbent. The method used is Appraisal Theory. The data used are 1587 for Jokowi related data, 1774 for Ministry related data, and 1337 government related data. The result of data analysis from this research is that people have positive sentiments for incumbent. Innovation is a term that has the highest positive sentiment, whereas imaging is the term that has the lowest negative sentiment.