Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Read Disturb Effect on RRAM based AI Accelerator with Multilevel States and Input Voltages

RRAM technology is a promising candidate for implementing efficient AI accelerators with extensive multiply-accumulate operations. By scaling RRAM devices to the synaptic crossbar array, the computations can be realized in situ, avoiding frequent weights transfer between the processing units and memory. Besides, as the computations are conducted in the analog domain with high flexibility, applying multilevel input voltages to the RRAM devices with multilevel conductance states enhances the computational efficiency further. However, several non-idealities existing in emerging RRAM technology may degrade the reliability of the system. In this paper, we measured and investigated the impact of read disturb on RRAM devices with different input voltages, which incurs conductance drifts and introduces errors. The measured data are deployed to simulate the RRAM based AI inference engines with multilevel conductance states and input voltages. Device-to-device variability is also taken into consideration to assess the accuracy drop. Two convolutional neural networks, LeNet-5 and VGG-7, are benchmarked with MNIST and CIFAR-10 datasets, respectively. Our results show that mapping weights with differential pairs yields better robustness to read disturb and variability effects.
The MATLAB Simulation and the Linear Quadratic Regulator Torque Control of a Series Elastic Actuator for a Rehabilitation Hip Exoskeleton

Almost the half of mechanical energy of a human gait cycle is generated by a hip joint. Therefore, this paper discusses a rehabilitation hip exoskeleton, starting with the modeling, simulation and ending with controlling its hip joints. A MATLAB based simulation environment with the use of Simscape Multibody toolbox was utilized to design and control the robotic hip exoskeleton. More details of adding a series elastic actuators (SEA) to the hip joints with the mathematical model are presented. However, the linearised mathematical model of the entire exoskeleton was found in simulation software which establish the basic need of controlling hip joints. The trajectory tracking is commonly used of controlling rehabilitation exoskeleton and to ensure a safe and reliable motion tracking methods, two desired torque signals were tested and analysed with the optimal linear quadratic regulator (LQR). The experiments with two torque signals–representing the sit-to-stand (STS) and the walking activity, demonstrated good performance of the motion gait tracking based on torque signals of a healthy person, which is carried out in the simulation environment. Furthermore, some studies in a human robot interaction are also mentioned in this paper.
Accuracy Comparison of Bluetooth Low Energy Indoor Positioning System based on measurement techniques

In this paper, we investigated the localization performance of indoor positioning techniques based on measurement techniques; the angle of arrival-based (AOA) and the received signal strength-based (RSS). We employed the Texas Instruments (TI) CC26X2R LaunchPad which operated in IEEE 802.15.1 standards. Four patterns of the anchor placement were assigned to evaluate the location accuracy performance of both indoor positioning systems. The results show that AOA-based can achieve higher location accuracy performance than the RSS-based. In particular, the accuracy performance of the RSS-based will depend on anchor placement while the AOA-based does not affect significantly. However, the AOA-based anchors are limited by proprietary protocols due to undefined continuous tone extension (CTE) implementation in BLE 5.1 standard. They can only communicate with tags that are from the same vendor. In contrast, the RSS-based anchors can work with any tags that follow the BLE 4.2 standard.
Electric Power Industry Challenges due to Increasing Shares of Inverter-Based Resources in Power Systems

The large-scale integration of variable renewable energy technologies around the world is forcing electric power systems into an unprecedented transition. This paper reports on power grid modernization and reliability issues related to the planning and operation of power systems with high levels of variable renewable energy. The contents of this paper stem from recent qualitative research in the form of a summary of an industry survey. The technical feedback from the interviewed 37 industry experts helped us identify 12 key areas of potential concern, which are discussed throughout this paper.
How Do Engineering Faculty, Staff, and Administrators Define Engineering Thriving? An Investigation of Key Themes

In this work-in-progress research paper, we explore key themes and patterns prevalent in definitions of thriving for undergraduate engineering students. Although there is growing research and acknowledgment of the breadth and complexity of thriving in engineering, the field has limited conceptual clarity regarding its range of definitions. Data for this research was collected from 47 engineering faculty and staff who are considered long-term members of the engineering education system who also play a vital role in creating environments conducive to thriving and forming relationships with students that facilitate thriving. Participants were asked to define engineering student thriving in an open-ended survey, where their responses were analyzed using thematic analysis. 29 codes emerged from the data, 18 of which align with prior research. 10 new codes emerged, relating to positive emotions (such as happy, excited, and passionate), learning (love of learning, growing, understanding and deep learning), wellbeing, identity, belongingness, and professional experiences. These findings highlight positive aspects of engineering thriving beyond the absence or reduction of suffering and hardship. Implications of these findings include developing measures with multi-dimensional focus and emphasizing the role of emotional support and identity development in engineering students.
Development of an Instrumentation Platform for Studying the Electrical Property of Soft Tissue under Mechanical Stimulation

The electrical property of soft tissue is reported to be sensitive to the applied pressure. In this paper, an instrumentation platform is designed to provide simultaneous electrical-mechanical sensing while different mechanical compression conditions are applied. For this purpose, mechanical structure and control system are integrated to provide different pressing forces or pressing speeds. In addition, an electrode is designed to generate a uniform electric field, and an impedance spectrometer is involved for electrical impedance measurement. After the platform is made, characterization experiments are performed to demonstrate that the designed platform is qualified with position, speed, and force control accuracy of 100%, 98%, and 97% respectively. Subsequently, ex vivo animal tissue experiments are carried out for a preliminary test of the association between electrical and mechanical properties of soft tissue. According to the experimental results, a positive association between electrical impedance and the applied pressure is observed.
Robust Representations for Keyword Spotting Systems

Keyword spotting poses several challenges due to acoustic disturbances such as noise, reverberation, and speaker-to-speaker variations. Typical keyword spotting features such as Log-Mel spectrograms or Mel Frequency Cepstral Coefficients are highly sensitive to frequency perturbations. Denoising such features using an autoencoder improves performance in noise but often results in poor generalization for unseen speakers. This paper proposes an architecture that addresses the above challenges for keyword spotting systems. The audio features are combined with a latent representation extracted by a denoising autoencoder. In addition, this paper proposes a novel method for creating highly separable optimal latent representations from speech using a discriminative denoising autoencoder trained with a quadruplet loss metric learning approach. The proposed approach creates a discriminative latent representation, which when combined with the original input results in an architecture that is ideal for keyword spotting. The proposed architecture outperforms all approaches when tested in both clean and noisy environments with reverberation at various distances on unseen speakers.
Thermal Distribution Analysis of Modular Multilevel DC-DC Converters

Modular multilevel topology based DC-DC converters (MMDC) have attracted much attention in the industrial and academic areas. However, compared with the traditional MMC in high voltage dc transmission (HVDC), due to the new operating principles and modulation methods, the electrical and thermal stress distribution of MMDC is not clarified yet, which could threaten the reliability and lifetime of MMDC. Thus, it is essential to investigate the thermal performance of power devices in MMDC to provide guidance on thermal design and active thermal control. In this paper, the power losses of submodule devices are calculated and the thermal model is built first. Then, the concept of dc current modulation ratio is defined, based on which the relationship between power losses and thermal distribution is revealed. As the dc current modulation ratio decreases, the thermal distribution imbalance gets more serious. Finally, the analysis is verified by an MV MMDC simulation model and laboratory prototype.
Modular Multi-level LLC Resonant Converter for High-power and Medium-voltage Application

In this paper, a dc-dc resonant converter is proposed for high-power (HP) and medium-voltage (MV) application, which adopts series-connected half-bridge sub-modules to build the inverter unit of the conventional LLC converter. And the gate signals of each module have a tiny phase shift angle to reduce the voltage change rate of the converter and simplify the insulation design difficulty of magnetic component. Open-loop control method is employed to equalize the voltage of the sub-module capacitances, thus simplifying control. This paper analyzes the operating principle and voltage gain of the converter and determine the control strategy. Finally, the topology is verified by building a 21kW, 3kV to 500V simulation model and an experimental prototype with an efficiency of 96.77% under the condition of 3kV/12kW.
The Benefits of SiC/GaN Devices in T-Type Three-Level Inverter Hybrid Applications

Aiming at the efficiency improvement of high switching frequency and high power three-phase T-type three-level converters, a high-efficiency three-phase T-type three-level converter based on GaN/SiC devices is designed, which will be used in wind power systems. All operating modes and transition processes of the T-type stage fragment are analyzed to simulate the power loss of the semiconductor in one fundamental cycle. Investigate and model the behavior and losses of SiC and GaN devices, calculate the loss distribution and system efficiency of the topology, and optimize the switching frequency of the inverter. The device loss and system efficiency in three threephase T-type three-level topologies are compared and analyzed by LTspice and MATLAB. Several aspects, such as device switching characteristics, system efficiency, and volume of passive components, have the potential for optimization.
Contact Versus Noncontact Detection of Driver’s Drowsiness

With an estimated number of injuries in the millions, accidents caused due to drowsy driving remain a significant source of financial costs and loss of life. Accurate detection of driver’s drowsiness could provide a clear avenue towards eliminating a great majority of the associated accidents and losses. Existing research on the subject could be defined as either contact-based or noncontact-based alertness detection. This paper utilizes a novel multimodal driver’s alertness dataset consisting of 45 subjects via seven recorded channels, including four contact-based and three noncontact-based channels, to investigate the performance of said modalities in detecting driver’s drowsiness as well as provide a novel comparison between the results of multiple contact and noncontact methods. Our results highlight the viability of noncontact methods to detect driver’s drowsiness as an implementable technology in automobiles.
Group Delay based Methods for Detection and Recognition of Whispered Speech

The present study demonstrates the effectiveness of the group delay function for detection and recognition of whispered speech. The group delay function in its spectral form is able to differentiate phonated from the whispered speech. In particular, the mean height-bandwidth product of the formants in the lower and mid frequency regions of the short term spectrum of speech is used herein for whisper speech detection. The mean height-bandwidth vector is obtained herein across all frames and is further smoothed using a moving average filter. The smoothed temporal version on this vector is able to detect the phonated to whisper change points. Towards this end, the current study investigates the cepstral, linear prediction (LP), minimum variance distortionless response (MVDR) and numerator of the group delay based smoothing techniques for whisper change point detection. Experiments on whispered speech detection are performed on the CHAINS database. Experimental results are compared to various methods for whispered speech detection available in literature.
Quantification of resolution for a dynamic focusing OCM microscope

Achieving high lateral resolution still remains a challenge for in vivo Optical Coherence Microscopy (OCM) biological imaging. While to address this challenge, the numerical aperture (NA) of the microscope objective in the sample arm of the OCM interferometer may be increased, it introduces trade-offs in terms of loss in the depth of focus over which lateral resolution can still be maintained. As a critical step to offset this problem, we recently presented the optical system design of a dynamic focusing (DF) optical coherence microscope with a built-in liquid lens for re-focusing through the sample depth with no moving parts at in vivo speeds. We present experimental measurements of the modulation transfer function (MTF) acquired from the fabricated research prototype. The measurements were obtained though the edge detection method as a function of the voltage applied and at various positions in the field of view (FOV) within a 2mm cubic sample. Results demonstrate a resolution of 2 µm across the voltage range and the FOV, which validates the expectation by design of a quasi-invariant resolution of less than 3μm over a 2mm×2mm lateral cross-section across the 2mm depth of skin-equivalent tissue. Images of a tadpole sample acquired with the probe at different focal depths are also shown to demonstrate gain in resolution with focusing through different depth zones.
Linear programming as an optimization tool in survivable optical networks

For a source-destination pair to communicate in a connection-oriented wavelength-routed optical network, a connection in the optical layer between the two nodes must be established. This process, also known as Routing and Wavelength Assignment (RWS), is realized by selecting a path between the two end nodes and allocating a suitable wavelength. The aim of the RWA process is to find routes and assign wavelengths for connection requests in a way that minimizes the consumption of network resources, while at the same time ensuring that no two lightpaths are assigned the same wavelength on a shared fiber link. Routing and wavelength assignment in wavelength-routed WDM networks is a major design issue, especially when survivability is a requirement. To minimize resources in such networks operating under static traffic environment, the problems of routing and wavelength assignment must be solved jointly as a single problem. This study proposes a different approach to formulate the problems of routing and wavelength assignment as Integer Linear Programming (ILP) problem. Unlike other formulations, where the routing sub-problem and wavelength assignment sub-problem are considered separately, this approach addresses the RWA problem compounded. Although this approach increases the number of variables in the problem, it guarantees the optimal solution. Furthermore, the problem may in many cases be solved using simpler linear programming techniques.
Rotational corrugation in associative desorption of D2 from Cu(111)

We report rotationally state-resolved measurements of the average steric orientation of D2 molecules desorbing from a Cu(111) surface. The desorbing flux of D2 molecules is probed by laser-induced fluorescence with linearly polarized tunable vacuum ultraviolet radiation in the B1? u+(v',J',M') ? X1? g+(v'',J'',M''). Molecules desorbing in the vibrational ground state show for all rotational states measured (J'' = 1 ? 8) a quadrupole alignment factor, A(2)0, compatible with a spatially isotropic distribution. A strong azimuthal, polar, and lateral corrugation of the dissociative interaction potential can cause this behavior.
: The development of deductive reasoning: How important is complexity?

Current conceptions of the nature of human reasoning make it no longer tenable to assess children's inference by reference to the norms of logical inference. Alternatively, the complexity of the mental models employed in children's inferences can be analysed. This approach is applied to transitive inference, class inclusion, categorical induction, theory of mind, oddity, categorical syllogisms, analogy, and reasoning deficits. It is argued that a coherent account of children's reasoning emerges in that there is correspondence between tasks at the same level of complexity across different domains, and that the inferences of younger children, while impressive and important, are consistently simpler than those of older children.
Developing CALI: An Inventory to Capture Collaborative Active Learning and Inclusive Practices in Introductory CS Courses

This full paper presents CALI 1.0: an inventory to capture the various components of collaborative active learning and inclusive practices in introductory CS courses, as a basis for studying their impact on a student’s ability to engage with course material and be successful in the course. Collaborative active learning is defined as a pedagogy where learners work with one another on activities that involve actively engaging with the course material through discussions, problem-solving, case studies, role plays, and other methods. CALI consists of three separate indices: Structure, Sociality, and Inclusiveness. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. In this paper, we present the inventory and results from a focus group of faculty to gauge responses to using this inventory to capture the teaching components of their courses. We present CALI as a tool that can be used to study how teaching practices, e.g. inclusive and collaborative active learning pedagogies, impact course experiences for students in different demographic groups as well as a reflective tool for use by faculty while designing their courses.
TLCD: A Transformer based Loop Closure Detection for Robotic Visual SLAM

Loop closure detection (LCD) can effectively correct errors in visual odometry. It is thereby a critical part in robotic visual simultaneous localization and mapping (SLAM) system, which is widely used in modern robotic systems such as sweeping robots and drones. In this paper, we propose a transformer-based loop closure detection algorithm (TLCD), which employs a distillation transformer as backbone to extract global features, and is combined with a sequence matching as back-end processing of principal component analysis (PCA) algorithm. TLCD can accurately provide Precision-Recall curve based on several public datasets including CityCentre and New-College datasets. Results show that TLCD’s average accuracy is up to 16.91% higher than the traditional LCD method. It is also about 3.18% higher accuracy than the state-of-the-art convolutional neural network (CNN) based LCD method.
Classification of paragroup actions in subfactors

We define “a crossed product by a paragroup action on a subfactor” as a certain commuting square of type II1 factors and give their complete classification in a strongly amenable case (in the sense of S. Popa) in terms of a new combinatorial object which generalizes Ocneanu’s paragroup. In addition to the standard axioms of paragroups, we have the intertwining Yang-Baxter equation as the new additional axiom. We will show that Rational Conformal Field Theory in the sense of Moore-Seiberg and orbifold construction in the sense of D. E. Evans, the author, and F. Xu produce paragroup actions on
Multi-Physics Coupling Analysis and Optimization Design of SiC MOSFET Power Module Package Insulation

Wide bandgap semiconductors such as SiC and GaN have superb properties in breakdown strength, temperature resistance, and efficiency. Due to the widespread use of wide bandgap devices, the power density is required to be as high as possible and the key approach is to increase the working voltage and decrease the packaging size. The packaging insulation of high-voltage power modules such as SiC MOSFET should withstand high multi-physics stress and guarantee reliability. The electric field distribution is highly related to the electrical conductivity of packaging insulation since the electric stress in the packaging insulation is either constant DC voltage or unipolar square wave voltage due to the intrinsic body diode of SiC MOSFET. However, the non-linear conductivity and its coupling relationship with the electric field and temperature have not been investigated before. In this paper, a multi-physics analysis of electric field and temperature in high-voltage SiC MOSFET power modules is proposed. The non-linear electrical conductivity of packaging insulation materials (encapsulation and substrate ceramic) is measured and analyzed to derive an expression. Then a half-bridge SiC MOSFET is modeled in multi-physics software and the non-linear conductivity is considered to simulate the transient distribution of electric field and temperature in the packaging, especially at the key triple point positions. To use the multi-physics in the packaging insulation, an optimization procedure is proposed for future high-voltage power module design and fabrication.
A New Switching Strategy for a GaN-based Three-Level Active Neutral Point Clamped Converter

This paper investigates a suitable switching strategy for a GaN-based three-level active neutral point clamped (3L-ANPC) converter. The 3L-ANPC operation is considered at 800V DC bus with the use of 650V GaN devices. The different switching modes for obtaining the zero state result in short-loop and long-loop commutations. A parallel operation of zero states termed full-mode is preferable for reducing conduction losses. However, this results in a multi-loop commutation producing high voltage stress across the inner devices. This paper proposes a modified full-mode switching strategy to mitigate this issue by a timed switching sequence involving clamping devices. The proposed switching mode involves active switching of the clamping device in 3L-ANPC for coupling and decoupling the long loop path. The commutation process for the proposed switching mode is explained in detail in this paper. Simulation results are presented using the device Spice model and parasitic inductances of a designed 3L-ANPC phase leg. The voltage stress and loss results determined from simulation results are compared with existing switching modes. A three-level double pulse test circuit is presented, and experimental test results are provided for the proposed switching mode.