Miracle-dz/UniFAR-FTUs huggingface dataset:
MDF-SLAM: Monocular Dense 3D Reconstruction Based on Depth Estimation

SLAM is one of the most critical technologies for mobile robots. Vision-based SLAM techniques have recently become popular. In this study, a high-density reconstruction technique based on monocular camera, MDF-SLMA, is proposed to solve the constraints of stereo and RGB-D cameras. The DSO-based tracking thread is used for RGB input images. To estimate the depth of key frames, an updated deep learning technique is used. Attitude optimization and density mapping are based on the results of depth estimation. Experimental results show that the proposed method can achieve monocular density mapping and reduce SLAM trajectory error. MDF-SLAM lays the foundation for mobile robots to detect their surroundings and perform complex tasks.
A New Method for Diagnosing Alzheimer's Disease and Monitoring Its Severity Using FDG-PET

This study aimed to use fluorodeoxyglucose (FDG)-positron emission tomography (PET) images of the brain for the computer-aided diagnosis of Alzheimer's disease (AD) and monitor its severity using a new KNN and image similarity-measure-based approach. The first objective was to diagnose AD automatically using an image similarity index derived from mutual information. The second objective was to determine the association between the similarity measure and neuropsychological assessments. 95 patients with AD and 102 Cognitively Normal (CN) FDG-PET data were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). We then found a distance metric indicative of the average similarity between any 3D image to CN and AD patients' images. The diagnosis is based on a threshold value for the distance. Then, the association of the distance metric values with Mini-Mental State Examination (MMSE) results were analyzed. The detection algorithm achieved an area under the ROC curve of 0.959 using a leave-one-out method. The correlation was 0.639 between MMSE and the distance metric. This is higher than previously reported values obtained with other indices. The new method does not need registration, segmentation, or feature extraction. Furthermore, no training is required for a growing or changing dataset.
Determinant of Quantization Step Size with Neural Networks for Amplify-Quantize-Forward Relay Channel

This paper deals with the method of determining the quantization step size in an amplify-quantize-forward (AQF) relay channel with quadrature amplitude modulation. We introduce an existing method for determining the quantization step size in the AQF relay channels and propose a method applying neural networks to solve the computational complexity problem of the conventional method. Simulation results confirm the superiority of the proposed method.
Dual-Input-Single-Output Boost Converter with Inductors Coupling For Dual Electromagnetic Energy Harvesters

Multi-source Electromagnetic energy harvesting is a promising technique for self-powered widely distributed information devices in IoT to improve the output power capability. A dual-input-single-output boost converter is proposed in this paper for dual electromagnetic energy harvesters at different source voltages and internal resistors. To ensure high power delivery, impedance matching is required. The correct duty ratio ranges are investigated for the proposed converter under DCM operation for both branches with same boost inductances. Furthermore, negative coupling between boost inductors will enable current ripple reduction and help for efficiency improvement as well as size reduction. The optimal coupling coefficient for dual electromagnetic energy harvesters at different source voltages and internal resistors is revealed as well as the effective region. The proposed prototype is designed with GaN HEMT power transistors at switching frequency of 250 kHz for input voltages of 5V and 2V, internal resistances of 50ohm and 100ohm, respectively. The optimal coupling coefficient is 0.12 with peak ripple reduction rate of 12.9%. The peak converter efficiency reaches to 94.1% with 1kohm load.
Mining Bursting Core in Large Temporal Graph

Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential for seeking real emergency events in temporal graphs. Unfortunately, most previous studies on community mining in temporal networks ignore the bursting patterns of communities. In this paper, we study the problem of seeking bursting communities in a temporal graph. We propose a novel model, called the ( l , δ)-maximal bursting core, to represent a bursting community in a temporal graph. Specifically, an ( l , δ)-maximal bursting core is a temporal subgraph in which each node has an average degree no less than δ in a time segment with length no less than l. To compute the ( l , δ)-maximal bursting core, we first develop a novel dynamic programming algorithm that can reduce time complexity of calculating the segment density from O (| T |) 2 to O (| T |). Then, we propose an efficient updating algorithm which can update the segment density in O ( l ) time. In addition, we develop an efficient algorithm to enumerate all ( l , δ)-maximal bursting cores that are not dominated by the others in terms of l and δ. The results of extensive experiments on 9 real-life datasets demonstrate the effectiveness, efficiency and scalability of our algorithms.
Adaptive Sensorless Control of High Speed Surface-Mounted PMSM Drives with Inductance Estimator based on Current Error

Due to their high power density, high efficiency and wide speed range, high speed surface-mounted permanent magnet synchronous motors (SPMSMs) with model-based sensorless field-oriented control (FOC) are evolving rapidly. The accuracy of conventional sensorless control method for SPMSM based on $d$-axis current error mainly relies on estimated inductance. However, the actual inductance varies in a wide range with different loads, which greatly degrades the performance and narrows the application scope. Therefore, this paper proposes an inductance estimator, which utilizes both $d$-axis and $q$-axis current errors to construct a second-order inductance error equation. Consequently, the inductance can be estimated in an adaptive manner to improve the sensorless accuracy in various working conditions. The proposed inductance estimation method is validated on a 14krpm SPMSM, showing convergence from estimated to actual inductances at different loads even with ± 50% inductance variation. As a result, the current amplitude is reduced by more than 10% at full load, verifying the high accuracy and effectiveness of the proposed method.
Optimization of distributed generators to improve the performance of power system

Integration of distributed generation DG in the distribution network is considered one of the best solutions to resolve power loss, sustain balance between production and consumption and reduce greenhouse gas emissions. It is known that the non-optimal sizing and non-optimal location of DGs, can lead to increased power loss, and may impact the voltage profile, which can exceed the permitted limits. In this paper the proposed methodology uses Particle Swarm Optimization (PSO) algorithm to distinguish the optimal location and impact of DG in radial distribution network to reduce total active power loss and improve the voltage profile and hence reliability assessment of the system. The effectiveness of the proposed method is demonstrated by the application on IEEE 33-bus radial standard distribution test system.
A Comparative Study of Simulation Methods for Patch Antenna Strain Sensors

Passive wireless strain sensors based on microstrip patch antennas recently show significant promise for reliable health and performance monitoring in the aerospace and civil industries. Microstrip patch antenna consists of a metal patch, dielectric substrate, and metal grounding plate. When excited by the signal, an electro-magnetic resonant cavity is formed between the conductor patch and the grounding plate and radiates outward through the gap between the patch and the grounding plate. These antennas radiate at their resonant frequency. When the antenna experiences deformation, the antenna shape changes, causing a shift in the electromagnetic resonance frequency of the antenna. For this purpose, the antenna design is critical to influencing both RF characteristics and strain sensing behavior. In this work, a passive wireless strain sensor using a microstrip patch antenna with feed-inset operating at 2.8 GHz was designed and simulated in different simulation methods using simulation software CST Microwave studio and COMSOL Multiphysics. The simulation results were compared regarding the patch antenna's processing time, linearity, and sensitivity for strain sensing. Among these methods, strain and RF simulation of patch antenna using COMSOL software show better accuracy and linear response towards strain.
FEPC: Fairness Estimation Using Prototypes and Critics for Tabular Data

A machine learning (ML) fairness estimator, which is used to assess an ML model’s fairness, should satisfy several conditions when used in real-life settings. Specifically, it should: i) support a comprehensive fairness evaluation that explores all ethical aspects; ii) be flexible and support different ML model settings; iii) enable comparison between different evaluations and ML models; and iv) provide reasoning and explanations for the fairness assessments produced. Existing methods do not sufficiently satisfy all of the above conditions. In this paper, we present FEPC (Fairness Estimation using Prototypes and Critics for tabular data), a novel method for fairness assessment that provides explanations and reasoning for its assessments by using an adversarial attack and customized fairness measurement. Given an ML model and data records, FEPC performs a comprehensive fairness evaluation and produces a fairness assessment for each examined feature. FEPC was evaluated using two benchmark datasets (ProPublica COMPAS and Statlog datasets) and a synthetic dataset containing two features, one of which is biased and one of which is fair, and compared to existing fairness assessment methods. The evaluation demonstrates that FEPC satisfies all of the conditions, making it suitable for real-life settings, and outperforms existing methods.
Low-Cost and Hardware Efficient Implementation of Pooling Layers for Stochastic CNN Accelerators

With the astonishing achievements of Convolutional Neural Network (CNN) accelerators in real-time applications, the deployment of CNNs on hardware has become an attractive matter. Pooling layers in CNNs are employed for reducing the computation of convolutional layers. Nevertheless, their hardware implementation strategy can impact the accuracy and performance of accelerators. This paper presents a novel parallel Stochastic Computing (SC) based architecture of pooling modules in hardware for stochastic CNN accelerators. With this approach, the SC-based average pooling is reconfigurable with 1.28 times lower power consumption, and the max pooling layer achieves area reduction with the ratio of 4.36. Extending the application of stochastic CNN accelerators in different classification problems is also achieved by implementing AAD pooling with the proposed method. Eventually, the reliability of the proposed method is approved by testing our pooling layers in the VGG-16 structure with the CIFAR-10 dataset.
A Novel Spectral Feature Selection Method Based on Binary Genetic Algorithm for Efficient Detection of Endometrial and Ovarian Cancers: Preliminary Results

Endometrial and ovarian cancers are the most common gynecological cancers. Early and accurate diagnosis of both diseases is essential for minimizing mortality rate as well as treatment costs. In this study, we propose a novel methodology for efficient detection of endometrial and ovarian cancers. The proposed approach is based on processing urine-based Fourier Transform infrared spectroscopy data by using machine learning powered by evolutionary feature selection. We propose a novel spectral feature selection method based on a binary genetic algorithm, which is combined with a number of well-known machine learning methods and applied on spectroscopy data obtained from urine samples. Our results show that the presented approach offers superior performance over other methods in the literature for the same purpose. It can therefore efficiently be used for accurate and automated detection of endometrial and ovarian cancers by utilizing a very small set of spectral features, which provides a significant benefit regarding physical implementation of the proposed pipeline as a biosensor kit.
A method for extending depth of focus in STED nanolithography

We present a theoretical approach to extend depth of focus (DOF), which is based on the integrated utilization of the cylindrical polarization effect, circular symmetrical phase mask and interference effect. Simulation results show that this method can generate over 4λ and 2λ DOFs when objective lenses with NA = 0.9 and 1.4 are introduced respectively, while maintaining the horizontal size beyond the diffraction limit. The DOF is over two times that by conventional approaches under the same conditions. Such a focal spot with extended DOF, although stemming from the STED microscope concept, is applicable in nanolithography.
Modification and Experimental Validation of a Logistic Regression Vehicle-Pedestrian Model

The paper deals with the analysis and modification of the expectation submodel of a game theory-based vehicle-pedestrian interaction model. The model modification concerns inclusion of additional explanatory variables, particularly the pedestrian position, in order to extend the model applicability and improve the overall model accuracy. The expectation model parameters are identified by using a real-world drone-recorded dataset, which includes several hours of vehicle-pedestrian interactions at an unsignalized crosswalk. The identified expectation model and the overall pedestrian model are validated against the experimental data.
Study and implementation of the down sampling module of SHVC extension

The programmable processors newest technologies, as for example the multicore Digital Signal Processors (DSP), offer a promising solution for overcoming the complexity of the real time video encoding application. In this paper, the SHVC down sampling module was effectively implemented on a single core of TMS320C6678 DSP for a Common Intermediate Format (CIF)input video sequence res-olution(352x288). Results show the implementation of the down sampling module on TMS320C6678 with verifying the presence of the three LCrCb components on the output down sampled video sequence.
Positioning and Posture Adjustment for a Rotational Cutting Biopsy Capsule

Capsule endoscopy has become a popular method for the diagnosis of colorectal disease because of its small size and friendly interactive performance. Capsule endoscopy has integrated biopsy function to improve diagnostic ability beyond only imaging. However, the randomness of pathological tissue distribution in the intestine requires the accuracy of positioning and attitude adjustment of the capsule endoscopy during sampling. Herein, accurate attitude adjustment is more challenging. In this paper, based on the magnetic charge model, a method that can control the position and posture of the capsule is proposed, referring to the modeling and simulation of the magnetic field distribution of the permanent magnet. Experiments based on the motion model of the biopsy capsule were used to study reciprocating linear motion and circumferential posture adjustment. The experimental results show that the proposed method can effectively control the biopsy capsule to achieve the desired position and posture to meet the requirement of sampling the inner intestine.
Image Degradation due to Interacting Adjacent Hot Pixels

Hot Pixels are cosmic ray induced digital imaging sensor defects that accumulate as the camera ages at rates that are highly dependent on pixel size. We previously developed an empirical formula projecting hot pixel defect density (defects/year/mm2) growth rates via a power law, with the inverse of the pixel size raised to the power of $\sim $3, multiplied by the square root of the ISO (gain). We show in this paper that this increasing defect rate results in a higher probability that two defects will occur within a 5x5 pixel square. Under these conditions, the color demosaicing and JPEG image compression algorithms required for picture creation greatly amplify the impact of these two defective pixels, spreading damage to a 16x16 pixel area and creating significant color changes resulting in a very noticeable image degradation. We develop an analytical generalized birthday problem formula in order to estimate the number of hot pixels needed to achieve a given probability of having two defective pixels within a 5x5 square. For a 20 Mpixel DSLR camera, only 128 hot pixels generate a 4.5% probability of such interacting defective pixels, or 1 in 22 cameras. For 4 micron pixels this would occur in 1.4 years at ISO 6400, and in 3.2 years at ISO 3200.
Loop Gain based Auto-Tuner for Power Converters in Weak Grids

The interactions between grids and power converters can easily lead to performance degradation or even instability, especially in weak grids where grid impedance can change frequently and significantly. A loop gain based auto-tuner is proposed in this work for power converters to maintain high control performance and robust grid-converter connection. The auto-tuner is designed to autonomously adjust controller’s parameters (e.g., proportional gain and integral gain) of power converters based on real-time estimated loop gain. Compared with impedance reshaping methods, the proposed solution requires no additional hardware and guarantees simultaneously high control performance and sufficient stability margins. Experimental results are given for validating the effectiveness of the proposed method.
Patch Enhancement for Melanoma Detection with Bag of Visual Words

Melanoma is a type of skin cancer caused by the ultraviolet radiation of Sun. Melanoma will become severe if it is not detected early, and it may spread to other body organs, most commonly the lungs, brain, liver, and bones. Dermatologists look for tell-tale signs of melanoma on pigmented skin lesions (moles) to detect it or, in some cases, differentiate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in a series of biopsies that are unnecessary. Furthermore, this type of imprecision may allow a melanoma case to spread undetected. This study develops an automatic melanoma detection system to overcome this challenge. The proposed method is based on Bag of Visual Words (BoVW) with a new patch enhancement scheme, which incorporates both traditional and cutting-edge methods. Experimental comparisons between the proposed method and the well-known convolutional neural network models demonstrate the effectiveness of the developed system.
Blurred Boundaries: What It Means To Be an Early Career Faculty Member During a Pandemic

Early career faculty are undergoing a stressful transition period and actively negotiating their professional identity. The COVID-19 pandemic has changed the daily activities in early career faculty’s personal and professional lives and thus complicated the negotiation process. This study explores how engineering faculty members redefine and reconceptualize what it means to be in their early career during the COVID-19 pandemic. Through an emergent qualitative coding technique, we identified two themes: 1) the blurring of personal and professional boundaries, and 2) the renegotiation of different identities. The findings offer insights into how to better support early career faculty and allow them to balance these different dimensions of their academic identities.
A finite-difference scheme to model Switched Complementary Linear Systems

Developing efficient numerical techniques to model complex systems is valuable, and their main contribution is to reduce computational costs. This work presents a numerical approach that deals with this difficulty by incorporating a concise formulation to understand the dynamic of complementarity switched systems using the finite difference method. We introduce a discrete-time numerical version of the Switched Complementary Linear System and convert it into a mixed complementarity problem. In addition, we compute the numerical solution of the dynamics of a DC-DC boost converter by combining our proposal with the Feasible Directions Algorithm for Mixed Nonlinear Complementarity Problems.
REAL TIME TUNEABLE ANALOGUE PID CONTROLLER REALIZATION

This paper proposes a real time tunable analogue PID controller realisation witch can be used as a conventional PID, an adaptative PID or an intelligent PID “iPID”. The integral and derivative of the PID input signal are continuous time signals and never sampled. This avoid discretization issues such as aliasing phenomena and the critical sampling period choice. The operative PID circuit part is totally analogue. Few digital potentiometers and digital switches are used. This allows to tune the parameters values of the controller and select PI, PD, PID configuration. The analogue circuit part is designed with a new original circuit architecture. A prototype of the circuit is implemented. Experimentation results show good similarity to the theoretical simulations.
A Split-Phase Enhanced Hybrid Active NPC Topology for PV Applications with Short-circuit Fault Tolerant Capability

With the recent trend to utilize an optimal combination of both Si and SiC switches in a converter for cost-effective systems, the reliability of converter remains a point of concern. This paper presents a fault-tolerant Si/SiC-based hybrid active neutral point clamed (ANPC) configuration with the aim to achieve enhanced reliability for such systems with high efficiency and lower cost. The proposed converter comprises of a split-phase structure with decoupled conduction paths for positive and negative directions of load current to minimize the conduction losses and avoid the flow of circulating currents existing in such split-phase configurations. Further, the utilized split-phase structure provides protection against the short-circuit faults. This paper also proposes a novel switching scheme that enables reduction in the device losses to achieve high efficiency and reduced stress on the switching devices. Moreover, the proposed topology mitigates the issue of leakage current in the transformer-less Photovoltaic (PV) applications. In this paper, the operating principles of proposed topology with its fault-tolerant operation is described in detail. Further, analytical and simulation investigation studies are presented with the comparative investigation against the all SiC devices-based ANPC configuration. Finally, performance of the proposed converter is validated using the constructed experimental prototype.
Design and Research of a Cable-driven Flexible Ankle Power-assisted Exoskeleton

Aiming at the shortcomings of rigid exoskeleton which can not adapt to the multi degrees of freedom of human joints and is inconvenient to wear, a lower limb assisted exoskeleton driven by cable is proposed in this paper. The exoskeleton is driven by the cable, which simulates the contraction of gastrocnemius to realize the assistance to the lower limbs during walking. Experiments show that after wearing exoskeleton, the pressure peak at the heel is reduced by 1kg, which plays a role in helping the ankle. The exoskeleton also retains the degree of freedom of lower limb joints, fits the human body better, and has the comfort of far super rigid exoskeleton.