Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse-View Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks

X-ray tomosynthesis is a low-dose and relatively inexpensive 3D imaging technique that relies on limited-angle and sparse-view tomography. Unfortunately, tomosynthesis often leads to reconstructed images that are corrupted by ripple artifacts. The current state-of-the-art for artifact suppression in tomographic data involves the use of Convolutional Neural Networks for mapping corrupted reconstructions into artifact-free images. Recently, Deep Convolutional Framelet Networks (DCFNs) were proposed in which max-pooling layers in the U-net were replaced by fixed Wavelet decompositions. In this work, we show that replacing the regular convolutional blocks in the DCFNs by Mixed Scaled Dense (MSD) blocks for exploiting multi-scale features allows us to better represent and hence suppress tomosynthesis artifacts at different scales. Experiments using simulated data show that our Mixed Scale Dense Convolutional Framelet Network (MSDCFN) outperforms the state-of-the-art methods in the vast majority of the tomosynthesis scans evaluated.
An Extension of the Kinetic Battery Model for Optimal Control Applications

Optimal control of electric vehicle (EV) batteries for maximal energy efficiency, safety and lifespan requires that the Battery Management System (BMS) has accurate real-time information on both the battery State-of-Charge (SoC) and its dynamics, i.e. energy supply capacity, at all times. However, these quantities cannot be measured directly from the battery, and, in practice, only SoC estimation is typically carried out. Moreover, the so-called Equivalent Circuit Models (ECM) commonly utilized in BMS solutions only display a memory-less algebraic dependence of voltage and current on SoC, without an ability to predict battery energy supply capacity based on its recent charge/discharge history. In this article, we propose a novel parametric algebraic voltage model coupled to the well-known Man-well-McGowan dynamic Kinetic Battery Model (KiBaM), which is able to predict both battery SoC dynamics and its electrical response. We present an offline model parameter identification procedure that yields SoC-dependent model parameters from standard dynamic battery tests, and we introduce an algorithm based on the Extended Kalman Filter (EKF) for standard SoC estimation on the proposed model. Numerical simulations, based on laboratory measurements, are presented for prismatic Lithium-Titanate Oxide (LTO) battery cells. Such cells are prime candidates for modern heavy offroad EV applications.
A Real-Time Embedded System on Behavioral Recognition of Students in Classroom

This paper provides a system that balances speed and accuracy to recognize students’ behaviors. This system consisted of a skeleton extraction method and two behavior classification models. This paper used two stage classification models (CNN+SVM) which is new and different from other papers. We also make some modifications to the input of skeleton information so that they can represent the differences between similar behaviors more effectively.
A Study on Loss Functions and Decision Thresholds for the Segmentation of Multiple Sclerosis Lesions on Spinal Cord MRI

Multiple sclerosis (MS) patients often present hyper-intense T2-w lesions in the spinal cord. The severe imbalance between background and lesion classes poses a major challenge to Deep Learning segmentation approaches, requiring for ad hoc strategies. Careful selection of the loss function and adjustment of the conventional 0.5-thresholding may help mitigating this issue. Our results show the performance advantages of loss functions based on the Tversky Index and the benefits of threshold tuning over more standard settings and the state-of-the-art model for MS lesion segmentation on spinal cord MRI.
Effect of the Pliocene closure of the Panamanian Gateway on Caribbean and east Pacific sea surface temperatures and salinities by applying combined Mg/Ca and d 18 O measurements (5.6 - 2.2 Ma)

The divergence of the SST and SSS records between Caribbean Sites 999 and 1000, and east Pacific Site 1241 monitors the progressive closure of the Panamanian Gateway. The divergence of Caribbean-Pacific SSS is first recognized at Site 1000 (at 5.6 Ma) and ~1.4 million years later at Site 999 (at 4.2 Ma). This evolution reflects the decreasing inflow of relatively fresh Pacific water masses (Site 1241) into the Caribbean that led to the gradual establishment of the modern Pacific-Caribbean SSS-contrast. With the onset of Northern Hemisphere Glaciation at 2.7 Ma, sea level changes started to influence the throughflow through the Panamanian Gateway. Sea level lowstands with 50-80 m during Marine Isotope Stages (MIS) 96, 98, and 100 caused the glacial closure of the Panamanian Gateway. SSTMg/Ca in the east Pacific (Site 1241) followed the global cooling signal, while SSTMg/Ca in the Caribbean (Site 999) increased during glacials due to the cessation of the inflow of colder east Pacific watermasses.
Explaining Anatomical Shape Variability: Supervised Disentangling with A Variational Graph Autoencoder

This work proposes a modular geometric deep learning framework that isolates shape variability associated with a given scalar factor (e.g., age) within a population (e.g., healthy individuals). Our approach leverages a novel graph convolution operator in a variational autoencoder to process 3D mesh data and learn a meaningful, low-dimensional shape descriptor. A supervised disentanglement strategy aligns a single component of this descriptor with the factor of interest during training. On a toy synthetic dataset and a high-resolution diffusion tensor imaging (DTI) dataset, the proposed model is better able to disentangle the learned latent space with a simulated factor and patient age, respectively, relative to other state-of-the-art methods. The relationship between age and shape estimated in the DTI analysis is consistent with existing neuroimaging literature.
Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially-designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, OC-SVM classifier, and security analysis. The experimental results show that ADFFDA obtains the best authentication performance w.r. t representative approaches, by achieving a mean equal error rate of 0.01%.
An Efficient Joint Decoding Scheme for Outer Codes in DNA-Based Data Storage

In this paper, we consider a simplified system model for DNA-based data storage, where each DNA string is either correctly transmitted, or being erased, or being corrupted by random substitution errors, and all strings are randomly shuffled with each other. Any linear block error-correction code can be used as the outer code to encode bits among different strings. We derive soft/hard information of all bits, which allows to independently decode each bit within a string, leading to an independent decoding scheme. To improve the decoding performance, we measure the reliability of each string based on the independent decoding result, and perform a further step of decoding over the most reliable strings, leading to a joint decoding scheme. Simulations with low-density parity-check codes confirm that the joint decoding scheme can reduce the frame error rate by more than 3 orders of magnitude compared to the independent decoding scheme.
SWIN-Fovea: A Remote Sensing Object Detection Model Based on Swin Transformer and FoveaBox

Recently, convolutional neural networks (CNNs) have achieved excellent results in object detection of remote sensing images. However, pure CNN-based object detection models have difficulty in learning explicit long-range relational modeling due to the inherent locality of convolutional operations. To solve these problems, we proposed a hybrid architecture for object detection by combining transformers and CNNs. In which the backbone network employs a Swin Transformer-based FPN, while the detection head comprises a CNN-based FoveaBox classifier and regressor. We named the proposed model Swin-Fovea and conducted extensive experiments on the DIOR dataset for remote sensing object detection. The experimental results suggested the superiority of our approach over other advanced methods.
Selective harmonic mitigation-pulse amplitude modulation technique for 7-level inverters

This paper proposes a fundamental-switching-frequency selective harmonic mitigation method based on pulse amplitude modulation (PAM) that, in a single phase 7-level cascaded H-bridge inverter, reduces the amplitudes of all harmonics in the output voltage waveform, except those having order $n=16 k \pm 1, k=1,2,3, \ldots$. After explaining its mathematical formulation, the manuscript compares results obtained with the proposed method and those with a selective harmonic elimination pulse amplitude modulation. The results highlight the better filtering capability of the new approach. Further comparisons with a procedure based on the total harmonic minimization show that the new technique reduces two more low-order harmonics.
IMep: Device-Free Multiplayer Step Counting With WiFi Signals

Currently, most of the mature WiFi-based step applications can only count the steps of a single individual, and their methods cannot capture amplitude information about each person's overall actions in multiuser scenes. It is still a challenging task to build a multiplayer step counting system in a device-free manner. In this paper, we present IMep, a novel device-free system based on WiFi that can obtain the amplitude information of each person's overall step actions and count the steps of multiple people simultaneously. Our main strategy is to establish a Multiplayer Stepping Amplitude Relation Model (MSARM) and design a Multiplayer Amplitude Decomposition Algorithm (MADA) that uses Block Term Decomposition (BTD). Moreover, we put forward a new Moving Energy Method (MEM) that captures each person's step number more clearly and accurately. The experimental results show that, IMep can function successfully in an environment of up to 7 people. The accuracies in three different room settings are 95.57%, 94.66%, and 89.94%, respectively, suggesting that IMep is effective in multi-person scenarios.
Task Offloading and Resource Allocation Method for Edge Computing in Intelligent Coal Mining

In the mining industry, the intelligentization of coal mining is the future development trend. However, the traditional centralized Internet of Things network model struggles to meet the interaction demands of intelligent devices in the context of intelligent coal mining. To address the the problem, we study the optimization strategies for underground edge computing based on the 6G network’s distributed architecture. We construct an intelligent coal mining edge computing architecture, considering the revenue maximization problem for multi-tasking with different requirements in underground scenarios. Finally, we propose a Joint Task Offloading and Resource Allocation for Revenue Maximization algorithm. Simulation experiments verify the advantages of our algorithm, including fast convergence speed and high system revenue.
Observation of Spallation in an Isolated Aluminum Nanoparticles and 2D Molybdenum Oxide System by Rapid Plasmonic Heating

Spallation of aluminum nanoparticles (Al NPs) in the presence of abundant oxidizing species, molybdenum trioxide (MoO3) 2D flakes, is initiated by rapid photothermal heating. Al NPs of various sizes, 50, 80, and 120 nm diameters, and alumina shell thicknesses from 2–4 nm were studied. Photothermal heating was accomplished by coupling of focused blue laser (446 nm wavelength) to an optical grating substrate and to the plasmonic resonance of the Al particles. The heating rate was estimated through COMSOL simulations to be 108 K/s, sufficient for the occurrence of the Melt Dispersion Mechanism (MDM). In some cases, additional heating was provided by the reaction of Al NPs and MoO3. Scanning electron Microscope (SEM) images provided observation of reaction products at reaction sites on the molybdenum oxide flake. Evidence of reactions was seen at both the original particle location and at the secondary sites where spalled particles were located. Product particles were observed with a wide range of sizes, implying that the reaction is a complex process resulting in spallation, and secondary reactions and sintering of spall particles. The inclusion of reaction heating enables spallation of Al NPs with less photothermal heating, and a greater spatial distribution of spalled particles, implying a more energetic reaction mechanism. This mechanism is important for mesoscale nanothermite reactions.
Water stress effects on growth, yield and quality of wheat (Triticum aestivum L.)

Understanding the effects of water stress on wheat growth, yield and quality is essential for good irrigation management. In South Africa most of the wheat production areas are vulnerable to drought stress during crop development. That causes substantial reduction in grain yield, depending on the developmental stage at which water stress occurred. Supplemental irrigation is the main strategy for adaptation and stabilisation of yield under water stress. However, agriculture is the leading single water-use sector locally, consuming about 60% of total available water. Therefore, the need to improve water use efficiency (WUE) in crop production is clear, since South Africa is classified as a water-scarce country. Experiments were conducted under a rain shelter at Hatfield Experimental Farm, University of Pretoria, in the 2010 and 2011 seasons. The main objective of the study was to evaluate the effects of water stress at different stages on growth, yield, and quality of three wheat cultivars, namely Duzi, Steenbras and SST 843. Water stress was imposed by withholding water at either of three growing stages. The first treatment was stressed during tillering stages to flag leaf (stem elongation (SNN)), followed by water stress from flag leaf to the end of flowering (flowering stage (NSN)), and lastly water stress from grain filling to physiological maturing (grain-filling stage (NNS)), whereas optimal supply of water was maintained throughout the season by weekly irrigating to field capacity for the control treatment (NNN). Irrigation treatments and cultivars influenced growth, yield and quality, depending on the developmental stage at which irrigation was withheld. The control treatment (NNN) and the treatment stressed in the flowering stage (NSN) had highest and lowest grain yield respectively in both seasons. Water stressed treatment NSN reduced grain yield by 33% and 35% in the 2010 and 2011 seasons respectively, when compared with the control treatment (NNN). Reduction of grain yield due to stress in the flowering stage (NSN) was ascribed to reduction in the number of seeds per ear, number of ears per unit area, ear length, and flagleaf photosynthesis rate (Pn). In the flowering stage (NSN) water stress reduced Pn by 59%
An Investigation into Permanent Magnet Hysteresis Losses in Reverse-salient Permanent Magnet Synchronous Motors

Hysteresis losses occurring in Permanent Magnets can be significant when the operating point of the magnets crosses the B-axis (H = 0 line), resulting in large minor loops. In traditional Interior Permanent Magnet Synchronous Motors, it has been extensively studied and observed that these losses aren’t significant under regular operating conditions. The conditions responsible for these losses may exist under the regular operating conditions of a Reverse-salient Permanent Magnet Synchronous Motor for which, no discussion on these kinds of losses has been done before. This paper bridges the aforementioned knowledge gap by investigating the existence of such losses in a Reverse-salient Permanent Magnet Synchronous Motor to understand if there is a cause for concern.
A survey on channel measurements and models for underground MIMO communication systems

The high reliability of the communication system is critical in metro and mining applications for personal safety, channel optimization, and improving operational performance. This paper surveys the progress of wireless communication systems in underground environments such as tunnels and mines from 1920 to 2022, including the evolution of primitive technology, advancements in channel modelling, and realization of various wireless propagation channels. In addition, the existing and advanced channel modeling strategies, which include the evolution of different technologies and their applications; mathematical, analytical, and experimental techniques for radio propagation; and significance of the radiation characteristics, antenna placement, and physical environment of multiple-input multiple-output (MIMO) communication systems, are analyzed. The given study introduces leaky coaxial cable (LCX) and distributed antenna system (DAS) designs for improving narrowband and wideband channel capacity. The paper concludes by figuring out open research areas for the future technologies.
La tecnología de realidad virtual al servicio de la comunicación y difusión de la Cueva de Santimamiñe.

The conservation and restoration of heritage sites are one of the main concerns related to heritage management. Sometimes this problem hinders from diffusing its richness to the society. That was the case of Santimamine Cave that was forced to close the entrance to the public due to the big damage caused by the visitors. Thanks to the technological advances in virtual reality and a meticulous working process, the accessibility in an innovative and in a non-intrusive way has been achieved.
Mobility Support for MIMO-NOMA User Clustering in Next-Generation Wireless Networks

Non-Orthogonal Multiple Access (NOMA) is a promising technology for future-generation wireless systems, with potential to contribute to the improvement of spectral efficiency. NOMA groups users into clusters, based on channel gain-difference. However, user mobility continuously changes the channel gain, which often requires re-clustering. In this article, we study a set of re-clustering methods: arbitrary, one-by-one and Kuhn-Munkres assignment algorithm (KMAA), that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to automatically dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. Experimental results show that the KMAA method improves efficiency and capacity through minimizing the number of re-clustering events, improving resource utilization, and lowering signaling overhead. Other sets of results highlight the throughput and outage probability gains of the KMAA method across a wide range of mobility scenarios. We also provide an analysis of the KMAA algorithm when applied to MIMO-NOMA, encompassing link resiliency and maintenance of average gain-difference, among users in clusters.
Improved Transient Response in Inverter-Based Resources using Deep Reinforcement Learning

In this work, a deep reinforcement learning (DRL)-based controller is presented to improve the performance of an existing control process in inverter-based resources (IBR). A DRL algorithm is utilized to learn a model-free value function which further improves the transient response of an IBR over fast response time frames. In addition, the developed controller provides black-box control, that is, it performs the desired control action without accessing or modifying the internal parameters of the existing controller in an IBR. The developed controller is tested in case studies utilizing EMT simulations in PSCAD/EMTDC and its performance, resilience, and adaptiveness are verified.
Channel Estimation Based on Model-Driven Residual Networks for Zero-Padded OTFS

Orthogonal time frequency space (OTFS) has been a potential option for high-mobility communication scenarios due to its strong Doppler resilience. In our previous work, considering the practical rectangular pulse shaping and fractional Doppler shifts, a novel pilot pattern for Zero-Padded OTFS channel estimation was proposed, which can significantly reduce the pilot power of the embedded pilot scheme based on compressed sensing. To further improve the accuracy of the channel estimation, this paper presents a simplified model-driven residual network (ResNet) to refine the initial results. The proposed ResNet has a smaller input size and thus, lower training complexity by leveraging the beneficial features of the delay-Doppler (DD) domain channel. Simulation results demonstrate that the proposed simplified ResNet outperforms the preliminary estimator in terms of normalized mean square error (NMSE) performance. And the former can obtain more than 5 dB and 4.5 dB gain over the latter in the embedded pilot case and our proposed pilot pattern, respectively. The results also show that the channel estimation of the proposed ResNet can improve signal detection performance.
Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia

The last decade of rich data-driven research on functional magnetic resonance imaging (fMRI) has provided novel insights into human brain function and aberrant behavior in brain disorders. Independent component analysis (ICA) is a widely-used technique for data-driven analysis of fMRI data. Spatial ICA is the most prominent variation of ICA and provides replicable and interpretable intrinsic connectivity network (ICN). It assumes common spatial activation across time. However, very recent studies indicate that there is utility in adopting a dynamic spatial activation modeling approach. Characterizing dynamics for both temporal and spatial domains means we have a multitude of decompositions of the already high-dimensional, multi-dataset, and multi-subject fMRI data. Hence making sense of the derived data becomes a significant issue. Here we use topological data analysis (TDA) to identify topological descriptors of the windowed spatially dynamic components of fMRI data. We discover and summarize differences in the spatial dynamics of controls and schizophrenia patients (SZs). We discover that SZs generally have lower Betti numbers and higher Wasserstein distance between spatiotemporal brain states, which provide intuitive summaries of the reduced dynamism SZs exhibit in resting-state fMRI studies.