Miracle-dz/UniFAR-FTUs huggingface dataset:
Percolation Nature of Threshold Switching: an Experimental Verification

This paper details a process used to create an interconnect in a conducting systems, such as amorphous or polycrystalline semiconductors. An experimental verification on the plasticity that supports the percolation conduction mechanism is provided. The plasticity observed in the sample could be harnessed in the development of new electronic devices that require flexibility and adaptability, such as wearable electronics and bendable screens. Overall, TEM characterization, in combination with SAED analysis, revealed a highly oriented crystalline structure in the sample. In addition, the results of this study have implications for the design of new memory devices that are based on a percolation conduction mechanism, which could potentially lead to the development of more efficient and reliable non-volatile storage technologies.
Experimental Analysis of Phase Error in Centralized and Distributed SDR Systems

Understanding the behavior of phase errors between radio frequency (RF) chains in software-defined radios (SDRs) is crucial to the success of implementing many phase-sensitive applications, such as beamforming. Even if SDRs are provided the same clocking signal, initial local oscillator (LO) phase offsets across devices will inevitably be different. Despite its known effect on many wireless applications, there are only few works that experimentally discuss random phase errors in SDRs. To address this issue, we perform experiments and analyze the results of tens of experiments in an attempt to understand the nature of this phase offset. In particular, we target the USRP (Universal Standard Radio Peripheral) N310 platform as it provides up to 4 simultaneous transmit/receive chains that could be attractive for beamforming applications. We first model the system used in this study and demonstrate how phase errors can affect distributed beamforming gains. Then, we introduce our experimental setup, procedures and analysis of the results of the measured phase error. We do so first between two chains of the same/different transceiver boards within the same USRP, and then between chains of distributed USRPs that are geographically separated. We calculate the mean and standard deviation of this phase error, investigate its behavior over time, and demonstrate how the distribution of this error can vary based on whether it is measured in a centralized or a distributed fashion.
SEAM-STRESS: A Weakly Supervised Framework for Interstitial Lung Disease Segmentation in Chest CT

We propose Self-supervised Equivariant Attention Mechanism with up-Sampling, adapTive thREsholding, and augmented loSSes (SEAM-STRESS) for weakly supervised abnormality segmentation in chest CT images, covering a wide range of abnormal patterns. We introduce a novel point-level loss that allows the utilization of sparse annotations as a weak supervisory signal, outperforming models trained only with image-level labels. Furthermore, we introduce a post-processing adaptive background thresholding strategy to further improve the segmentation masks. Experiments on our internal dataset show the effectiveness of our framework in localizing and segmenting chest CT abnormalities.
Wireless Power Transfer with Resonant Pulsed Current Converter

This paper presents a resonant pulsed current converter as a new architecture for inductive-type wireless power transfer applications. The operation principle of inductive power transfer with no direct connection is discussed in relation to the resonant pulsed current. The current and voltage behaviors at the circuit components are analyzed, including the resonant function of the organic compensation network from the converter topology. Simulation results demonstrate that the non-pulsating input current with low ripple can be achieved. Moreover, the resonant pulse current in the primary side is 3.5 times the input current, resulting in a higher induced current capability and offers 98.2 % efficiency at 1 kW output power.
Effects of land use patterns on carbon sinks in terrestrial ecosystems in Xinjiang

Net ecosystem productivity (NEP) is a measure of ecosystem carbon revenue and expenditure, and changes in land use patterns have a significant impact on ecosystem carbon sink function in Xinjiang, yet the exact impact remains uncertain. In this study, we analyzed the dynamic changes of land use patterns by extracting landscape indices and applied the integrated empirical modal decomposition (EEMD) method to analyze the nonlinear trends of land use pattern changes in Xinjiang, China, from 1981 to 2019, using the Xinjiang region as the study area. In addition, the response relationship between the two factors is further explored by Pearson correlation analysis after de-trending them based on EEMD. The following are the main results of the study:(l) In most areas of Xinjiang, patch fragmentation and shape complexity increased overall, patch connectivity and maximum dominant patch area decreased, and the diversity and homogeneity of land use types increased. (2) For areas of significant change, AWMPFD, PD, SHDI and CONTAG were significantly negatively correlated with NEP, while MSIEI and LPI were significantly positively correlated with NEP.
Single Layer 1-bit and 3-bit Coding Metasurfaces for Broadband RCS Reduction

This paper proposes two types of one-layer circularly polarized 1-bit and 3-bit metasurfaces for radar cross section (RCS) reduction. The unit cell used in this paper has a period of P = 0. 3λ and a polarization conversion rate (PCR) of over 92.4% in the 11.02GHz to 26.32GHz frequency range. To represent the coding bits of the 1-bit and 3-bit coding sequences, the unit cells that make up the metasurface are combined into a small array of 4×4 unit cells. For the 1-bit coding metasurface, the "0" and "1" bits of the 1-bit coding sequence were represented by the unit cell and its mirror image at ±45°. The size of the 1-bit coding metasurface is 200 mm × 200 mm and made of 40 × 40 unit cells. For the 3-bit metasurface, when rotating the unit cell resonator by an angle 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°, the coding bits "000", "001", "010", "011", "100", "101", "110", and"111", are achieved, respectively. The dimensions of the 3-bit metasurface are 160 mm × 160 mm and consisting of 32 × 32 unit cells. Simulation and experimental results indicate that proposed 1-bit and 3-bit metasurfaces are effective in reducing RCS by over 10dB in frequency range 11.02GHz-26.32GHz, and RCS reduction fractional bandwidth 82.95% compared with PEC metal plate.
Cooperative Motion Planning for Connected and Automated Vehicle Platoons at Unsignalized Intersections

Against the backdrop of the rapid development of computer-related technologies, Connected and Automated Vehicle (CAV) have become a hot research issue in the field of transportation science and engineering. In the field of autonomous driving, there are insufficient research on the coordinated movement of multi-vehicle formations and the coordinated passage of intersections. In this paper, we propose a multi-vehicle cooperative formation motion planning method at non-signal intersections, based on the optimal control theory, and numerically solve the optimal with complex constraints by using the computational optimal control method. The control motion planning model proposes an optimal control framework with completeness and versatility. Among them, a two-vehicle formation model is proposed, and the joint simulation experiment of AMPL and MATLAB is carried out in the scenario of four formations cooperating through the intersection without signal lights, and the simulation results are compared and analysed, and finally the formation can reasonably travel farther within a certain period of time.
Offload Shaping for Wearable Cognitive Assistance

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance.
Semi-Supervised Domain-Adaptive Pulmonary Artery Segmentation via Uncertainty Guidance and Shape Strengthening

Artery segmentation is often required for visualization and quantitative analysis of pulmonary diseases during surgical planning. By intravenous injection of contrast agents and carefully adjusting the trigger delay time, arteries or veins could be enhanced in computed tomography angiography (CTA). On the other hand, contrast between vessels and background is relatively low in non-contrast CT (NCCT), and it is difficult to distinguish arteries and veins. Additionally, manual or semi-automatic annotation of pulmonary vessels is time-consuming and labor-intensive. In this paper, we propose a novel semi-supervised domain-adaptive pulmonary artery segmentation framework for NCCT by using annotated CTA and a limited number of annotated NCCT images as training samples. Specifically, an uncertainty-driven Bayesian convolutional neural network-based adaptation module is proposed to predict the uncertainty map to guide pulmonary artery segmentation in NCCT. To explicitly learn tubular structures in different domains (i.e., CTA and NCCT), we propose a shape-strengthening module (SSM), as a discriminator, to strengthen vessel shape and boundaries in both CTA and NCCT domains. Experiments show that the proposed method performs better than state-of-the-art methods for pulmonary artery segmentation on NCCT images.
A Six-stub Strip Line Impedance Tuner

Solid-state sources have gained significant momentum and have been widely adopted across various fields due to their numerous advantages that surpass those of magnetron tubes. This paper proposes a six-stub strip line impedance tuner, which can be applied for solid-state sources to achieve maximum power transfer and prevent damage from large reflection. The tuner is composed of three parts, which are tuning unit, impedance conversion unit and feeding interface unit. The tuning unit is designed based on the principle of equivalent circuits, the stubs are designed in a symmetrical manner to increase the value of the equivalent capacitance and enhance power capacity. A quarter-wavelength impedance transformer is used to transform the impedance to 50 Ω, and then a well-designed 50 Ω feed port is used to match the source end. The depth of the stubs is calculated using an equivalent circuit method, and simulation results validate that |S11| < -15 dB, achieving a good matching effect. This tuner can be used for automated tuning in industrial applications.
Integrating Prostate Specific Antigen Density Biomarker Into Deep Learning Prostate MRI Lesion Segmentation Models

Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance. Deep convolution neural networks have been widely utilized for lesion segmentation. However, these methods fail to achieve a high Dice coefficient because of the large variations in lesion size and location within the gland. To address this problem, we integrate the clinically-meaningful prostate specific antigen density (PSAD) biomarker into the deep learning model using feature-wise transformations to condition the features in latent space, and thus control the size of lesion prediction. We tested our models on a public dataset with 214 annotated mpMRI scans and compared the segmentation performance to a baseline 3D U-Net model. Results demonstrate that integrating the PSAD biomarker significantly improves segmentation performance in both Dice coefficient and centroid distance metric.
Balancing Available Energy Distribution in Massive MIMO SWIPT Sensor Networks

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology for the Internet of Things (IoT) and energy-constrained devices. In such networks, sensor nodes (SNs) harvest energy from the access point’s (AP) downlink transmission and utilize the harvested energy for uplink data transmission. However, ensuring a fair distribution of available energy among the SNs remains a significant challenge. This paper aims to address this challenge by proposing an optimization algorithm for a SWIPT network with an AP and multiple SNs. We formulate and solve a maxmin optimization problem to fairly distribute the available energy among the SNs. The performance of the proposed algorithm is evaluated through simulations, demonstrating a more even and fair distribution of available energy among the SNs compared to the equal power case. The results highlight the effectiveness of the proposed optimization algorithm in improving the performance and fairness of SWIPT networks, paving the way for future research in this area.
Analysis of the zero dynamics of three-phase current source inverters using a complex-valued approach

This paper analyzes the zero dynamics of three-phase current source inverters fed from a voltage source and connected to a local resistive load or the electrical power grid. The zero dynamics is the remaining dynamics of a system when it exactly meets the control objectives. It is important for understanding the system behaviour, and its limitations, and designing efficacious controllers. The analysis concludes that, when the power source is modelled from its Thévenin equivalent circuit, some stability problems occur in the dc link if the control scheme only tracks the output voltage. The paper includes an alternative nested-loop controller that overcomes the internal dc-current instability.
Deep Learning Multi-objective Optimization for Smart Manufacturing via Elitist Genetic Algorithm

This paper proposes a deep learning multi-objective optimization (DL-MOO) method for smart manufacturing to generate wire electrical discharge machining (WEDM) parameters via an elitist genetic algorithm with the deep neural network (DNN) and the transfer learning mechanism. Given multiple objectives, the proposed method can generate WEDM manufacturing parameters to optimize the multiple objectives at the same time. Practical experiments are conducted to validate the proposed method.
Spatio-Temporal Quantitative Ultrasound Imaging for Breast Cancer Identification

In this paper, a real-time ultrasonic attenuation coefficient imaging system identifying cancerous breast lesions is presented. In real-time quantitative medical imaging, spatio-temporal consistency is an important functional requirement, as the inconsistent frame-to-frame quantitative images can degrade diagnostic accuracy. However, the fidelity of an isolated single frame quantitative image is often degraded by the low signal-to-noise ratio due to the short detection time of the RF signal. In order to overcome such problem, the fact that sequential frame-to-frame images have high spatial correlation is utilized. Consequently, a quantitative imaging network featuring an iterative image refinement scheme is designed to inference the difference between adjacent frames images to enhance the spatio-temporal consistency. The proposed system is evaluated through numerical simulation and in-vivo breast cancer measurements. In the in-vivo study, the proposed system demonstrates statistical significance (p-value < 0.05) in the differential diagnosis of breast cancer while achieving 79% less temporal variation.
The Electrical Properties of a-IGZO TFT Prediction Based on Variational Autoencoder Model

In this study, we integrated the Polynomial Regression module with a Variational Autoencoder (PolyReg-VAE) model to foster the inference of transfer curves before further deriving the electrical properties of a-IGZO TFT. This approach is able to predict the electrical characteristics of a-IGZO-TFT with different process parameters in a short time, reducing the cost and the time of developing new technology.
A Flexible and Ultra-Thin Ultrasonic Sensor for Continuous Carotid Pulse Wave Monitoring

Pulse wave signals can reflect the status of the human cardiovascular system, and continuous pulse wave monitoring can guide disease diagnosis. While current ultrasonic devices can perform reliable diameter measurements, their rigid ultrasonic probes rely on precise control by the operator, which inevitably compresses the blood vessels and leads to erroneous recording. In this paper, a flexible piezoelectric ultrasonic sensor was proposed to continuously measure pulse waves by tracking the motion of the vessel wall. The flexible sensor is light (0.9 g), ultra-thin (600 $\mu\mathrm{m})$, self-adhesive to the skin, and does not compress human tissue. On this basis, a measurement system was designed to monitor the carotid arteries of volunteers. The results showed that the flexible ultrasonic sensor could continuously measure the diameter pulse wave with high reliability.
Multi-robot Cooperative Transport Simulation System

Multi-robot systems have gained significant attention in recent years due to their potential for collaborative handling and operations. Designing an effective simulation system for multi-robot environments is crucial for enabling research and development in this field, while minimizing risks and costs associated with real robot testing. This paper presents a Multi-Robot Collaborative Transport (MRCT) simulation system that integrates the Robot Operating System (ROSI) with the Gazebo software environment. The system employs the FastDDS communication framework for task allocation and control, enabling efficient distribution of tasks among robots. A dual-robot model is developed for collaborative handling, with two robots equipped with manipulators working together to accomplish transport tasks. The performance of the simulation system is evaluated by comparing the trajectory of transported objects with that of the dual-robot system. The results demonstrate the exceptional performance and accuracy of the MRCT system.
Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns With Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain–Computer Interface (BCI)

Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain–computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by <inline-formula><tex-math notation="LaTeX">$\text{9.94}\%$</tex-math></inline-formula>, increased the correlation to true RT on an average by <inline-formula><tex-math notation="LaTeX">$\text{7.38}\%$</tex-math></inline-formula>, and reduced the MAPE by <inline-formula><tex-math notation="LaTeX">$\text{7.09}\%$</tex-math></inline-formula>.
Spatial Pattern and Driving Factors of Newly Abandoned Farmland in China during 2015-2019

Due to rapid urbanization and continuous rural labor force depletion, varying degrees of abandoned farmland have emerged in regions of China, causing land resource wastage and posing significant threats to economic development and food security. In this paper, spatial distribution characteristics and driving factors of newly abandoned farmland in China during 2015-2019 were analyzed based on kernel density analysis, multiple linear regression, and causal inference using ArcGIS and DoWhy. Results showed that: (1) 1,330,545.19 hectares of abandoned farmland were newly added nationwide during 2015-2019, with a 0.99% increase in abandonment rate. Tibet exhibited the highest increase in abandonment rate; (2) Newly abandoned farmland distribution varied with increasing elevation, categorized into decreasing, first decreasing then increasing, and first increasing then decreasing trend. And the largest area of newly abandoned farmland was found in slopes during 0°-2°, and the area generally decreased as the slope increased. Shady aspects exhibited higher abandonment rates than sunny aspects. Moreover, the smaller the field area, the denser the newly abandoned farmland; (3) Elevation was the dominant factor influencing the newly abandoned farmland. For each meter increase in elevation between 1.50 m and 4184.41 m, the national area of newly abandoned farmland increased by 5.71 hectares.
Methods for Predictive Performance Improvement of Deep Learning Systems for Aerial Building Roof Detection with Multispectral Images

In this paper, we present a dilated convolutional neural network-based deep learning system for the challenging problem of building accurate roof pixel detection from city aerial multispectral images in the visible and infrared spectral bands. The proposed system uses an automatic spectral band selection strategy and an automatic histogram-based threshold-finding method to improve the network's prediction accuracy performance. Current existing systems train a single model on all the cities of interest data at once. These systems also usually use only RGB bands and avoid post-processing of the predicted images for increased prediction accuracies. In this paper, we present the following solutions to these object detection challenges, 1) we propose and implement a separate model for each city dataset, 2) we propose an optimal band selection strategy to choose the most informative visible-infrared bands from multispectral images, 3) we propose an adaptive and automatic histogram-based optimal threshold finding technique for building roof object segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the system's performance by 20.77% in building roof pixel prediction in any of the cities considered. We show that our system also has a 35.6% increase in F1 score compared to its state-of-the-art counterpart, the winning implementation of the SpaceNet building detection national challenge, which took place in 2017.