Miracle-dz/UniFAR-FTUs huggingface dataset:
A 12-bit SAR ADC With a DAC-Configurable Window Switching Scheme

This paper presents a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC) that uses a digital-to-analog converter (DAC) configurable window switching technique. By reusing the capacitors in the DAC, the proposed window switching scheme yields window boundaries to determine whether the input is located within the window, and thus avoid unnecessary capacitor switching. The proposed window SAR ADC improves the conversion efficiency and ADC linearity. A qualitative analysis of prior window switching schemes is presented to elaborate for various applications. A low-input capacitance of 1 pF was adopted to relax the input and reference buffers. A prototype ADC was implemented in 180-nm CMOS occupying an active area of 0.1 mm2. At 20 MS/s, it consumes a total power of 1.22 mW from a 1.5-V supply. The measured peak signal-to-noise and distortion ratio and spurious-free dynamic range were 61.7 and 79 dB, respectively. At the Nyquist rate, the measured effective number of bits (ENOB) was 9.53, equivalent to a figure-of-merit (FOM) of 83 fJ/conversion-step. In low-power mode (100 kS/s), it consumed a total power of $1.5~\mu \text{W}$ from a 0.7-V supply. At the Nyquist rate, the measured ENOB was 9.82, equivalent to a FOM of 16.6 fJ/conversion step.
Efficient Music Identification Approach Based on Local Spectrogram Image Descriptors

The diffusion of large music collections has determined the need for algorithms enabling fast song retrieval from query audio excerpts. This is the case of online media sharing platforms that may want to detect copyrighted material. In this paper, we start from a proposed state-of-the-art algorithm for robust music matching based on spectrogram comparison leveraging computer vision concepts. We show that it is possible to further optimize this algorithm exploiting more recent image processing techniques and carrying out the analysis on limited temporal windows, still achieving accurate matching performance. The proposed solution is validated on a dataset of 800 songs, reporting an 80% decrease in computational complexity for an accuracy loss of about only 1%.
Input Disturbance Suppression for Unidirectional Matrix Converter with a Stability-Enhancing Modulation Scheme

This paper proposes an input voltage disturbance suppression control strategy for the unidirectional matrix converter (UMC) with a new modulation scheme enhancing the stability. In the new scheme, the modulation index is directly, rather than reversely, proportional to the instantaneous amplitude of input filter capacitor voltages. Contrary to traditional schemes, the stability of the UMC with this new scheme is even better with the increase of the transferred active power, which is particularly suitable for applications with sinusoidal and balanced input conditions. As to the disturbed input conditions, the new scheme could introduce low-frequency harmonics into output currents. To address this issue, a feedback control strategy of output current amplitude is further proposed to eliminate the additional harmonics. Stability analysis of a UMC with the proposed modulation scheme and feedback control strategy is presented. Experimental results have verified the validity of the proposed control solution.
Modeling and Experimental Assessment of the EMI Characteristics of Switching Converters With Power Semiconductor Filters

An input filtering technology named “power semiconductor filter (PSF)” has been proposed recently. Its operating principle is based on using a series pass device (SPD) to profile the wave shape and magnitude of the input current of converters. The voltage across the SPD is regulated around the “knee point” of the current–voltage characteristic of the SPD to minimize the power dissipation of the SPD. This paper reports the conducted electromagnetic interference (EMI) performance of the converters with the PSF. To suppress differential-mode (DM) EMI, a fast current regulation circuit is proposed to tightly regulate the current through the SPD. To suppress common-mode (CM) EMI, a single CM noise bypass capacitor is proposed. Detailed mathematical models for describing the frequency response of the SPD and main components in the driving network are formulated. A set of selection guidelines for the components will be given. The derived models will be validated by comparing the theoretical prediction with the measurement results of a 100 W, 90–264 Vac LED driver using a buck–boost converter. Results reveal that the PSF reduces the DM noise level by 47.47 dBμV. The CM noise level is reduced by 21.4 dBμV with the bypass capacitor. An integrated circuit for the controller is illustrated to demonstrate the feasibility of reducing the form factor of the filtering section.
Industrial Applications of Data Mining : Engineering Effort Forecasting based on Mining and Analysis of Patterns in Historical Project Execution Data

i ABSTRACT Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand. ii ACKNOWLEDGMENTS I would like to acknowledge and thank my thesis mentor, Karl Kempf and my graduate advisor, Arunabha Sen who were very helpful and offered invaluable guidance, assistance and support. I would also like to thank my thesis committee member, Huan Liu for his advice.
A high‐performance CMOS four‐stage amplifier

In this paper, we theoretically propose and mathematically describe an effective four‐stage structure in Complementary metal‐oxide‐semiconductor (CMOS) technology based on differential block feedback. The special architecture of frequency compensation network improves frequency response with very small value of compensation capacitor, which results in low die area occupation. The presented novel and simple four‐stage structure is frequency compensated just via single Miller capacitor and a differential block. Symbolical transfer function is calculated, and circuit dynamics are introduced. To well explain theoretical description, the proposed configuration at circuit level is simulated using Taiwan Semiconductor Manufacturing Company Limited (TSMC) 0.18‐μm CMOS technology and HSPICE circuit simulator. The proposed configuration and corresponding circuit benefits from circuit simplicity and low die‐area occupation. The frequency compensation network forms two Miller loops with negative loop gains. Feedback paths are amplified via differential block while feedforward paths are attenuated, leads to improving frequency response compare with conventional structures. Ample simulation results are in good agreement with theoretical description. Leveraging the concept and method proposed four‐stage amplifier exhibits 170 dB, 8.12 MHz, and 90° as direct current (DC) gain, Gain‐BandWidth Product (GBW), and Pase Margin (PM), respectively. The supply voltage is set to 1.8 V, while the simulated circuit consumes 380 μW.
Analytical Modeling of Misalignment in Axial Flux Permanent Magnet Machine

This paper proposes an analytical model for the prediction of air gap magnetic field distribution for axial flux permanent magnet (AFPM) machine with various types of misalignments. The AFPM machine geometry is first transformed to a polar representation. Consequently, the subdomain model based on current sheet technique is developed. Then the stator coordinate system is chosen as reference coordinate to consider both static/dynamic angular and axis misalignments. The back electromagnetic forces and cogging torque are obtained accordingly based on Maxwell's equations. The results show that the proposed approach agrees with the finite-element method. The model is further validated by experiments under healthy, dynamic angular and axis misalignment conditions, which can validate the proposed approach. It turns out that the proposed approach can predict the performance of AFPM machines with types of misalignment quickly and effectively, which is greatly significant for further fault detection.
An Ecological Description of Jazz Improvisation

This paper builds on writings in psychology and philosophy to offer an “ecological” description of jazz improvisation. The description is grounded in the analogy of navigation through a complex environment, an environment that comprises the harmonic and metrical scheme on which the improvisation is based coupled with broader stylistic norms. The improvising soloist perceives this environment in terms of its “affordances,” that is, the possibilities for action that it offers (Gibson, 1979). While navigating the improvisational environment, the soloist also seeks opportunities for artistic display—motivic development, conspicuous risk-taking, and so on. Errors in improvisation reflect the soloist’s misperception of the environment’s affordances. Learning to improvise is a matter of refining perception through repeated experiences of improvisational success and failure. To bring the description to life, I offer evidence from an exploratory study of improvisational errors. The ecological description leads to new interpretations of the referent (the conceptual frame for a solo), improvisational learning and memory, and temporal coordination between soloist and ensemble. It counterbalances the prevailing computational view of improvisation, oriented around input, processing, and output.
Improved dbscan clustering algorithm using SR-tree

The tremendous amount of data produced now a days in various application domains such as molecular biology or geography can only be fully exploited by efficient and effective data mining tools. One of the primary data mining tasks is clustering, which is the task of partitioning points of a data set into distinct groups (clusters) such that two points from one cluster are similar to each other whereas two points from distinct clusters are not. The detection of clusters in a given dataset is important for data analysis. This paper presents a possible DBSCAN clustering algorithm implementation. DBSCAN algorithm is based on density reachable and density connected point. Adding distributed SR-tree technique to density clustering algorithm we can improve clustering results.
Experimental Validation of an Active Wideband SSR Damping Scheme for Series-Compensated Networks

Transmission network reinforcements are being undertaken to meet renewable energy targets toward a low carbon transition. High-voltage direct-current (HVDC) links and series-compensated ac lines are frontrunners in these developments. Although series capacitor installations can lead to subsynchronous resonance (SSR), HVDC links based on voltage source converters (VSCs) can be used to effectively damp SSR upon occurrence. An active damping technique to mitigate torsional interactions (TIs), a form of SSR, is presented. The damping scheme considers an active wideband filter to ensure positive damping in a wide range of subsynchronous frequencies. A state-space representation of the system has been formulated and eigenanalyses have been performed to assess the impact of the HVDC link on the TIs. A damping torque study for SSR screening is carried out, with results complemented with time-domain simulations to assess the accuracy of the small-signal models. The test system is implemented in a real-time digital simulator and connected to a VSC-HVDC scaled-down test rig to validate the damping scheme through hardware-in-the-loop experiments. The presented damping method exhibits a satisfactory performance, with time-domain simulations and laboratory experiments showing a good correlation.
Visual Perception And Action In Sport

The best ebooks about Visual Perception And Action In Sport that you can get for free here by download this Visual Perception And Action In Sport and save to your desktop. This ebooks is under topic such as 'how perception guides action: examples from sport and understanding perception and action in sport: how can studies in perception and action vi v 6 full download free download visual perception and action in sport book visual control of braking in goal-directed action and sport perception and action in sport: half-time comments on the action induction by visual perception of rotational motion performance psychology: perception, action, cognition, and sport neuroscience revisited (? ): a commentary how important is perception-action coupling in the tennis zeitschrift fur sportpsychologie (german journal of sport visual perception and introduction action in golf putting perception for recognition and action (pra) studies in perception and action vi v 6 ebook | slangsurfing non-conscious visual cues related to affect and action interactive video training of perceptual decision-making the role of sound in motor perception and execution high performance circuit e-journal john kolb, phd a machine learning approach to the visual perception of acoustic information during motor control and action do expertise and the degree of perception^action coupling perception and action in golf putting: skill differences free download aviation visual perception book intercepting a 3d versus 2d videoed opponent: visual representative learning design in dynamic interceptive actions demonstration and verbal instructions perception of maximum stepping and leaping distance training perceptual skill by orienting visual attention the influence of video-based perceptual training on physical characteristics of elite female karate athletes vision testing and visual training in sport the use of video game technology for investigating action feedback affects the perception of action-related neurotracker as a supportive indicator for rtp decision making representative learning design and functionality of model-based control of perceptioniaction visual search strategy and interception accuracy ideals multidimensional perceptual-cognitive skills training visual scanning in sports actions: comparison between journal of experimental psychology: human perception and
Facile Synthesis of 2D-HfS2 Flakes for $\mu$ -IDE-Based Methanol Sensor: Fast Detection at Room Temperature

A new and astonishing resistive gas sensor based on Al $_{\mu}-{\text {IDE}}$ /HfS2, synthesized by chemical route, has been developed for precise detection of methanol vapor at room temperature. A high sensitivity of 1.29 has been found at room temperature with the exposure of 500 ppm of methanol vapor at an operating bias of 1 V. Sensitivity has been obtained from transient response analysis. Most importantly, very fast response/recovery characteristics with good baseline restoration have been witnessed. The response time and recovery time have been recorded in the range of ~12.12 s to ~21.14 s and ~23.72 s to ~39 s. Cross-sensitivity with other interfering species has also been studied. A comprehensive justification, including Langmuir adsorption-desorption isotherm for appreciable sensing response, has also been delineated.
A 20‐MHz bandwidth, 75‐dB dynamic range, continuous‐time delta‐sigma modulator with reduced nonidealities

This letter presents a 4‐bit continuous‐time delta‐sigma modulator (CT‐DSM) fabricated using a 65‐nm CMOS process. The circuit is designed for wide‐bandwidth applications, such as those related to wireless communications. This CT‐DSM has an oversampling ratio of 16 with a 640‐MHz sampling frequency. To reduce the clock jitter sensitivity and excess loop delay effect, the first DAC pulse is a nonreturn‐to‐zero (NRZ)–type pulse, whereas the second DAC pulse is a return‐to‐zero (RZ)–type pulse; this is accomplished using a current‐steering DAC. In order to reduce mismatch without using a data‐weighted averaging circuit, the size and layout of the unit current source in the current‐steering DAC are considered carefully. The CT‐DSM achieves a signal‐to‐noise ratio (SNR) of 67.3 dB, a signal‐to‐noise and distortion ratio (SNDR) of 63.4 dB, and a dynamic range of 75 dB for a 20‐MHz signal bandwidth.
Application of Statistical Methods in the Analysis of Sentence Structure

Abstract The goal of this research is to explore sentence structures expressed by parts of speech. Due to a small amount of data, a problem of sparse data has arisen, which was solved by recording the annotated sentences and considering a “framework” of a sentence made up from a verb and a noun, which was conditionally called a code. The code of a sentence is created by changing each word of a sentence by a symbol (letter or number) that encodes one or other property of that word as a constituent of the sentence. Zipf’s law describes sentences, encoded like that, rather well. If we ‘learn’ well to identify and analyze (annotate, translate, etc.) sentences of the simplest structure, we can automatically process quite a large part of text sentences. It is possible to identify at least 17% of sentences consisting of the simplest structure.
A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city. In BSS, stations becoming empty or full is the main cause of customers failing to rent or return bikes. Some truck-based rebalancing strategies are proposed to solve this problem. However, there are still challenges around the relocation of bikes. The truck operating costs also need to be considered. In this paper, we propose a customer-oriented rebalancing strategy to solve this problem. In our strategy, two algorithms are proposed to ensure the whole system is balanced for as long as possible. The first algorithm calculates the optimal state of each station through the one-dimensional Random Walk Process with two absorption walls. Based on the derived optimal state of each station, the second algorithm recommends the station that has the largest difference between its current state and its optimal state to the customer. In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy. The simulation results indicate that the proposed strategy is able to effectively decrease the imbalance in the system and increase the system’s performance compared with the truck-based methods.
Application of Data Mining Technology in Network Marketing

Nowadays, science and technology in the unceasing progress, enterprises in the process of the development, and various technology applied to marketing and to the development of enterprises have brought some benefits. Although information technology brings some benefits to enterprise management, it will also bring some problems to the development of enterprise. The accumulation of a large amount of data, is the main problem for the enterprises to face, coupled with the more expensive money and effort on data processing from enterprises every year, which has a negative impact on the development of enterprises. The application of data mining technology in network marketing can help enterprises solve these problems effectively, which can not only integrate the data more comprehensively, but also extract more effective information for enterprises. This paper is a detailed description of the specific application of data mining technology in network marketing.
Hidden Markov Model Based Intrusion Alert Prediction

Intrusion detection is only a starting step in securing IT infrastructure. Prediction of intrusions is the next step to provide an active defense against incoming attacks. Most of the existing intrusion prediction methods mainly focus on prediction of either intrusion type or intrusion category. Also, most of them are built based on domain knowledge and specific scenario knowledge. This thesis proposes an alert prediction framework which provides more detailed information than just the intrusion type or category to initiate possible defensive measures. The proposed algorithm is based on hidden Markov model and it does not depend on specific domain knowledge. Instead, it depends on a training process. Hence the proposed algorithm is adaptable to different conditions. Also, it is based on prediction of the next alert cluster, which contains source IP address, destination IP range, alert type and alert category. Hence, prediction of next alert cluster provides more information about future strategies of the attacker. Experiments were conducted using a public data set generated over 2500 alert predictions. Proposed alert prediction framework achieved accuracy of 81% and 77% for single step and five step predictions respectively for prediction of the next alert cluster. It also achieved an accuracy of prediction of 95% and 92% for single step and five step predictions respectively for prediction of the next alert category. The proposed methods achieved 5% prediction accuracy improvement for alert category over variable length Markov based alert prediction method, while providing more information for a possible defense.
Engaging Young Learners in Integration through Mathematical Modeling: Asking Big Questions, Finding Answers, and Doing Big Thinking

Abstract The conceptual framework of mathematical modeling (e.g., Lesh & Doerr, 2003) is a vital area in mathematics education research, and its implementation has potential for deeply involving children in integrated and meaningful learning. In mathematical modeling learners are active agents in content-integrated, real-world problem solving. This emphasis on integrating multiple content areas to answer big questions, the pursuit of mathematical modeling, descends from Dewey’s work. We present the definition, principles, and design of modeling practices for readers who may be familiar with early childhood curriculum but less so with using modeling for learning. We explore the application of mathematical modeling to early childhood classrooms and its compatibility with early childhood pedagogies and philosophies. Young children may often be underestimated, assumed to be unable to pose big questions that can be answered through activity, experience, and data; but we discuss how young children can be engaged in problems through mathematical modeling. Finally, as preservice teacher educators, we discuss preparing preservice and in-service teachers for modeling in their classrooms. We offer examples and guidance for early childhood teachers to engage in authentic practice – meeting children where their interests are and creating integrated problem-solving experiences.