Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Modeling of Variable-Reluctance Tubular Resolver Based on Magnetic Equivalent Circuit and Conformal Mapping

Compared with flat linear structures, tubular linear machines do not face edge effects, making their performance better than their flat counterparts. In this article, a novel linear variable-reluctance resolver with tubular configuration is proposed. To optimize the design of an electromagnetic structure, modeling methods are used. Thus, in this article, an analytical model for the proposed resolver is presented. This model is based on the magnetic equivalent circuit (MEC) method and is considerably less time-consuming than finite element analysis (FEA), yet provides accurate results. A conformal mapping is employed to compute the permeance of the air-gap. The proposed model is then utilized to compensate the resolver’s performance for the longitudinal end-effects. The results of the MEC model are compared with those of FEA, and good agreement is seen. Finally, the compensated sensor is experimentally built and tested, and the simulation results are verified.
Development of Eye Blink Rate Level Classification System Utilizing Sitting Postural Behavior Data

The prevalence of dry eye syndrome (DES) has rapidly increased in recent years, negatively affecting the eye health of many office workers worldwide. Although low eye blink rate (EBR) has been pointed out as one of the main risk factors for DES, it is difficult for office workers to continuously monitor and increase their own involuntary blinking, especially when they are focused on the primary work task. Thus, as an effort to help office workers correct their low EBR, the current study developed a real-time EBR level classification system utilizing sitting postural behavior data. A total of twenty participants performed typical computer tasks on a sensor-embedded chair. The participants’ eye blinking and postural behavior data were collected to develop the EBR level classification system with a random forest algorithm. After evaluating the system performance, the relationships between EBR and postural behaviors were empirically examined to help understand how the system worked for EBR level classification. As a result, the developed system showed high classification performance overall; and compared with high EBR condition, low EBR condition was related to less overall postural variability and greater extent of forward bending posture. The real-time EBR level classification system is expected to contribute to preventing/relieving DES and thereby enhancing the eye health of office workers.
Research on the board level reliability of CQFJ ceramic package

In this paper, the finite element method is used to simulate the board level temperature cycling, mechanical impact and random vibration tests of CQFJ ceramic package. The stress and strain distribution of solder joints under different test conditions are analyzed. At the same time, we make special test boards and board level installation. The simulation results of relevant mechanical and temperature tests are verified. After the board level installation, the product meets the requirements of reliability test, forming a guide for the subsequent design of new products. The results show that under the condition of the same device size and total height, the stress relief ability of CQ F J lead out packaging structure is better than CLCC packaging structure. The J-type lead is more conducive to absorb the stress caused by the mismatch of thermal expansion coefficient between the package and PCB. Therefore, the use of ceramic four side J-type lead flat package can improve the reliability at the of premise of compatible pad size, and realize the compatible replacement of the large size CLCC package. Through this research, we have mastered the CQ F J ceramic package board level reliability simulation analysis and verification technology, which can quickly design and manufacture similar ceramic package to meet the needs of different users.
A Fuzzy-based Hybrid Approach for Estimating Interdisciplinary Learning Efficiency

Students, industries, and departments focused on civil engineering are engaging in inter-disciplinary learning techniques to promote sustainability and meet the urgent requirements of human development. However, only a few studies have discussed the learning efficiency and educational feedback of this trend. This lack of studies may result from the difficulty in estimating subjective engagement. Hence, this study surveyed 173 data points from 6 departments that provided interdisciplinary courses based on three-dimensional trapezoidal fuzzy numbers and a Likert scale. The questionnaire factors were modified from the useful and common scales in science, technology, engineering, and mathematics (STEM) education. The existing studies tend to estimate students learning efficiency based on the hybrid approach of data envelopment analysis (DEA) and principal component analysis (PCA). However, the meaning of variables may be changed in the PCA procedures of axis transformation. Hence, a new fuzzy DEA-Mahalanobis distance approach was proposed to solve this problem. Based on the student gains in the sustainability courses, three groups were clustered and compared. Feedback for students was suggested to improve learning strategies. The sensitivity analysis showed that effective teaching practice and learning from other open courses or YouTube are key factors in increasing learning efficiency in sustainability courses for civil engineering students. Therefore, this paper provides a useful and easy approach to improve the learning strategies for civil engineering students in regard to sustainability education.
An 802pW 93% Peak Efficiency Buck Converter with 5.5×106 Dynamic Range Featuring Fast DVFS and Asynchronous Load-Transient Control

This paper presents a buck converter with sub-nW quiescent power, high efficiency, and a wide dynamic range for ultra-low-power (ULP) IoT SoCs. To optimize the SoC power consumption, the buck converter supports fast dynamic voltage and frequency scaling (DVFS) and enables fast load-transient response (FLTR) through asynchronous control. In addition, the buck converter is fully self-contained with all features integrated on chip including a proposed adaptive deadtime controller. Fabricated in 65nm CMOS, measurement results show the buck converter has an 802pW quiescent power at 1.5V input voltage and a 93% peak efficiency. The measured dynamic range is from 0.5nW to 2.75mW, which is over 6 orders of magnitude. The measured voltage droop is 54mV for a 45nA-to-1mA load current step thanks to the asynchronous load-transient detector. The buck converter achieves the highest efficiency and widest dynamic range among all the state-of-the-art sub-nW switching voltage regulators, which makes it well suited for power management in ULP SoCs.
An Analytical Switching Loss Model for a SiC MOSFET and Schottky Diode Half-Bridge Based on Nonlinear Differential Equations

An accurate analytical switching loss model for a SiC MOSFET and Schottky diode half-bridge for a wide operating range is proposed in this paper, which is based on nonlinear differential circuit equations including parasitics. In the model, nonlinear device characteristics are used, including the dynamic gate-drain capacitance and the transfer characteristics measured under real switching conditions. With the proposed model, the accuracy improvement by using measured characteristics instead of device data sheet information is analyzed. In addition, the impact of making different common assumptions/simplifications on the accuracy of switching loss models is evaluated.
GPAE-LSTMnet: A Novel Learning Structure for Mobile MIMO Channel Prediction

Mobile channel estimation is very challenging as usually it requires more pilots and channel observations to obtain the channel state information (CSI) and the resultant estimation accuracy may decrease with the number of antennas and sub-carriers. Through exploring the long-and-short-term intrinsic spatial and temporal correlation among a set of historic channel instances randomly obtained within a certain communication environment, channel prediction can help increase the CSI accuracy w.r. t. to that obtained from only the pilots, and thus save signaling overhead and computational cost. In this paper, we propose a novel generative Periodic-Activator-enabled Auto Encoder-LSTM network (GPAE-LSTMnet) for accurate channel prediction of mobile MIMO channels, which first compresses the high dimensional channel matrix with high-frequency features to a low dimensional space with relatively low-frequency feature space that has high data smoothness and is suitable for time-series sequence prediction. After that, a LSTM network is used to predict the channel in the low dimensional space, which ensures high accuracy and low computational cost. Experimental results show that our proposed learning structure outperforms existing methods especially when the dimension of CSI to be predicted is relatively high, the time interval of the CSI sequence is relatively long and the number of network parameters is highly limited.
Investigation of 3D printed polymer-based heat dissipator for GaN transistors

GaN transistors are limited in their operational capabilities due to some limitations, of which the thermal management aspects. Until now, most of the existing heat-dissipator systems using additive manufacturing (AM) are based on a metallic finned heat sink, which is heavy and has a relatively high thermal resistance. Heat dissipation based on a phase change as operated in heat pipes is more efficient. Such heat sinks have been experimented with metals or ceramics and not by now with polymers. However, this may be of great interest. The use of polymer may enable reducing weight and cost of the thermal device. It may allow also improving the chemical compatibility of the heat pipe material and fluid, as this is often a severe issue. This work presents a characterization of a 3D-printed polymer-based heat pipe evaporator intended for GaN transistors. The electronic copper circuit on the polymer surface is created using plastronics technology. The metallized circuit presents an adequate electrical conductivity. The thermal characterization performed with the HFE 7000 fluid, shows that it is actually possible to cover the entire heated polymer wall with active nucleation sites once the full developed nucleate boiling regime is reached. The heat conduction through the insulating polymer wall appears as the limiting phenomenon for heat transfer.
Reinforcement Learning Algorithms: An Overview and Classification

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms' limitations plays a vital role in selecting the appropriate reinforcement learning algorithm that successfully solves the problem on hand in an efficient manner. Consequently, in this study, we identify three main environment types and classify reinforcement learning algorithms according to those environment types. Moreover, within each category, we identify relationships between algorithms. The overview of each algorithm provides insight into the algorithms' foundations and reviews similarities and differences among algorithms. This study provides a perspective on the field and helps practitioners and researchers to select the appropriate algorithm for their use case.
Random Interleaving Multiplexing Based IRSA Random Access System for Satellite-Enabled Internet-of-Things

A random access framework based on random interleaving multiplexing (RIM) is proposed to further improve throughput for internet of things in satellite networks, which resort to the state-of-art physical layer techniques that can resolve multiple packet collisions issue. The interleavers are randomly selected by active devices from a set of available interleavers, which are used to differentiate signals of different devices sharing a subframe that consists of multiple time slots. The message passing detector (MPD) is utilized to decode collided packets. By means of transmission diversity and interference cancellation, the proposed scheme can be linked with the irregular repetition slotted ALOHA (IRSA), the RIM-IRSA protocol is proposed. The analysis of packet loss ratio (PLR) of the RIM-IRSA is given by using the density evolution method, and the optimal degree distributions are given. Simulation results show that the RIM-IRSA outperforms traditional IRSA protocol.
Finger Vein Recognition via Sparse Reconstruction Error Constrained Low-Rank Representation

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction error on sub-backbones of one training image are used and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition.
Experimental Tests and Stress Analysis of SnPb Solder Joints in a Ceramic PoP Device

The solder joint reliability has been a research focus for three-dimensional (3-D) integrations. SnPb (63Sn37Pb) solder still has an irreplaceable place in aerospace applications, mostly in 2-D packages, due to its accumulated experiences and good performance in reliability. In this paper, the mechanical performance and reliability of SnPn solder balls in a board-level assembled ceramic PoP 3-D device were studied. Firstly, the shear test and pull test for a single SnPb solder joint near the corner or edge of PoP were carried out. The varied shear height and solder joint diameter were considered in tests and followed a failure analysis. And the appropriate pull test setting was discussed. Secondly, a finite element analysis for the board-level assembled PoP was performed to reveal the critical solder joint. With the measured modulus of print circuit board (PCB) and a plastic model for SnPb solder, the finite element model of the assembly was established and the stress field by a thermal cycle with a range from 85°C to -55°C was computed. The results showed that stresses are much higher at the edge of the solder joint array, especially near to PCB. The influence of PCB's thickness on the two-layer solder joints in the PoP assembly are not negligible.
Low-Frequency ripple influence in the P&O MPPT dynamics response applied in the single-stage DABSR DC-AC converter for interfacing PV to single-phase grid

This paper proposes a novel strategy of control and modulation in an isolated single-stage Dual Active Bridge Series-Resonant (DABSR) DC-AC converter specifically for solar photovoltaic arrays connected to a single-phase grid (PV2SPG). The Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm is implemented and analyzed considering the influence of the low-frequency ripple on the PV side. Aiming to compensate the PV voltage, which varies with the temperature, a duty ratio (DR) control is introduced. Besides, the power flow is controlled by phase shift modulation. The DC-AC converter is compared with an analogous DABSR DC-DC converter that does not have the low-frequency ripple problem to validate the proposed control and modulation. The result simulations validate the proposed control and modulation.
Multichannel Substrate Integrated Waveguide Diplexer Made of Dual-Mode Cavities and Split-Type Dual-Band Response

A multichannel diplexer using substrate integrated waveguide (SIW) technology is developed and presented for the first time using two SIW dual-mode cavities coupled with multiple single-mode cavities. In this work, a tri-channel duplexing system is studied. The 1<sup>st</sup> and 2<sup>nd</sup> channels of the diplexer are synthesized and designed as split-type dual-band response and constructed by TE<inf>101</inf> modes in dual-mode cavities coupled with two TE<inf>101</inf>-mode bandstop cavities, while the 3<sup>rd</sup> channel is synthesized and designed as 3<sup>rd</sup>-order Chebyshev response and dominated by TE<inf>201</inf> modes in the dual-mode cavities coupled with one TE<inf>101</inf>-mode bandpass cavity. The isolation in the 1<sup>st</sup> and 2<sup>nd</sup> channels is realized by cut-off characteristic of the single-mode cavity of the 3<sup>rd</sup> channel, while the isolation in the 3<sup>rd</sup> channel is achieved by arranging one output port at central position of one dual-mode cavity because of the weakest fields for TE201 mode. An experimental prototype centered at 10.051, 10.713, and 12.987 GHz is synthesized, designed, fabricated, and measured to demonstrate the collaborative design concept.
Fingerprint with Particle Filtering for Positioning based on MDT

This work proposes a general positioning framework by combining Radio Frequency (RF) fingerprinting with particle filter using the measurements collected by Minimization of Drive Testing (MDT), which has been specified as a general procedure to collect measurements as well as positions in Long Term Evolution (LTE). The focus of this research is to understand how positioning accuracy is changed by using particle filter on top of the fingerprinting algorithm. Performance of the proposed algorithm is evaluated with the measurements that are collected using MDT in real LTE network deployments. The results of this study indicate that the positioning accuracy can be greatly improved by combining fingerprinting with particle filter.
Evaluating Unsupervised Text Embeddings on Software User Feedback

User feedback on software products has been shown to be useful for development and can be exceedingly abundant online. Many approaches have been developed to elicit requirements in different ways from this large volume of feedback, including the use of unsupervised clustering, underpinned by text embeddings. Methods for embedding text can vary significantly within the literature, highlighting the lack of a consensus as to which approaches are best able to cluster user feedback into requirements relevant groups. This work proposes a methodology for comparing text embeddings of user feedback using existing labelled datasets. Using 7 diverse datasets from the literature, we apply this methodology to evaluate both established text embedding techniques from the user feedback analysis literature (including topic modelling and word embeddings) as well as text embeddings from state of the art deep text embedding models. Results demonstrate that text embeddings produced by state of the art models, most notably the Universal Sentence Encoder (USE), group feedback with similar requirements relevant characteristics together better than other evaluated techniques across all seven datasets. These results can help researchers select appropriate embedding techniques when developing future unsupervised clustering approaches within user feedback analysis.
An 800-mA Time-Based Boost Converter in 0.18µm BCD with Right-Half-Plane Zero Elimination and 96% Power Efficiency

This work presents a novel boost converter with time-based control for LED display applications. Using the inductor current, the right-half-plane zero of the CCM control-to-output transfer function is eliminated to improve dynamic performances with no extra off-chip component or power switch. To increase efficiency at light loads, a PFM operating mode with seamless PFM-to-CCM transition is included. The prototype implemented in a 0. 18µm BCD process provides a regulated output voltage of 5V with a 2.5-to-4.5V input voltage range. With a 3.5V input, the efficiency reaches a peak of 96%, while it is above 90% down to 10mA loads. The area occupation of the time-based PID controller is about 0.16 square millimeters with a quiescent current of 300µA. The converter bandwidth is increased by a factor of about 5 with respect to a standard compensation scheme.
High-Performance Lithium Polymer Battery Pack for Real-World Racing Motorcycle

This paper deals with the qualification testing to design a battery pack for a fully electric racing motorcycle. To obtain the best performance from the cells and considering the point that in this case the battery pack will be used in special racing condition, finding the best configuration and cells sequence to be connected together in that’s configuration is very important to make the battery pack. To avoid impedance mismatching inside the modules and the final pack, electrochemical impedance spectroscopy, capacity test, and impulse current test has been used to select appropriate cells to be connected in strings and make a uniform and high-performance battery pack with the best configuration based on the categorized cells. Experimental results obtained from the cell to cell variation and also impedance measurement for each module prove the uniformity in impedance.
A 0.0084-mV-FOM, Fast-Transient and Low-Power External-Clock-Less Digital LDO Using a Gear-Shifting Comparator for the Wide-Range Adaptive Sampling Frequency

This work presents a fast-transient and low-power, external-clock-less digital low-dropout regulator (DLDO) using a wide-range adaptive sampling frequency, $f_\text{SP}$. To generate such an $f_\text{SP}$, the proposed gear-shifting comparator (GSC) consists of two sub-comparators. First, in the transient state, a delay-line-based comparator (DLC) generating a high $f_\text{SP}$ swiftly reduces the error in the output voltage, VERR. Second, in the steady state, a subsequent edge-racing comparator (ERC) generating a low $f_\text{SP}$ slowly but accurately removes VERR. Using these two tightly-interlocking sub-comparators, the GSC can scale the $f_\text{SP}$ dynamically in a wide range so that it can make the DLDO concurrently achieve a fast response in the transient state and a small quiescent current in the steady state. Due to the unique capability of the ERC, the DLDO also can have a high-accuracy regulation. The DLDO was fabricated in a 65-nm CMOS process, and it achieved a 46-ns settling time against the 12-mA change of the load current with a 100-ps edge time. The quiescent current was only 10 $\mu \mathrm{A}$. The transient FOM was 0.0084 mV, which was the best among the state-of-the-art DLDOs.