Miracle-dz/UniFAR-FTUs huggingface dataset:
RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation leveraging Hybrid CNN-Transformer Approach with Dense Skip Connection

Orthopedic osteosarcoma is a prevalent malignant bone tumor. Preoperative planning, efficacy evaluation, and metastasis detection of osteosarcoma necessitate the use of magnetic resonance imaging (MRI). Due to the varying 10-cations, structures, sizes, and shapes of osteosarcomas among patients, as well as the high degree of tumor heterogeneity, segmenting osteosarcoma images manually presents a significant challenge for clinicians. In the meantime, the grayscale and texture features within the tumor in MRI images are not homogeneous, resulting in small grayscale differences between the tumor tissue and the surrounding normal tissue, which also presents a significant challenge for deep learning-based image segmentation methods. This paper proposes RTUNet++, a novel method for segmenting osteosarcoma MRI images. This method integrates the ResNet residual module, Transformer attention mechanism, and UNet++ Dense Skip Connection structure. We employ a CNN-Transformer hybrid architecture to prevent the loss of spatial feature data. Features with different semantic scales are aggregated at the decoder using multi-fusion dense jump connections to achieve flexible feature fusion. Consequently, in comparison experiments with various classical models, our proposed RTUNet++ achieves superior segmentation results on an osteosarcoma MRI image dataset.
Surface Defect Detection of Nuclear Fuel Pellets using Structured Light and Convolutional Neural Network

Surface defect detection of nuclear fuel pellets is crucial for the efficient and safe operation of nuclear reactors. In this study, a defect detection method based on structured light and convolutional neural network is proposed to address the challenges posed by small-sized fuel pellets, varying forms and sizes of pellet end face defects, and complex background environments. The method utilizes DLP technology and a Scheimpflug lens to construct a high-performance structured light imaging device. Subsequently, a deep network model named Defect-Deepf.abv. + is designed for processing structured light images. Building upon DeepLab $3+$, the network adopts MobilenetV2 as the encoder backbone and incorporates the CBAM attention mechanism, effectively enhancing defect segmentation accuracy. The experimental results show that our method achieves an F1 score of 95.6% on structured light images. Moreover, the detection time for a single pellet is less than 1 second, meeting the real-time quality inspection demands of the production line for nuclear fuel pellets.
Normalization for connectivity of brain regions in schizophrenia classification

It is a popular method to use resting-state functional magnetic resonance imaging (rs-fMRI) to obtain brain connectivity, which can study whether brain function is abnormal, and can be constructed by techniques such as correlation and entropy. Usually, the techniques need to normalize the signal first, and how to normalize has an important impact on the obtained results. Unfortunately, there is little discussion about it in existing methods. For this problem, this paper focuses on the normalization, analyzes its impact on the correlation and entropy of the brain region connectivity, and proposes a normalization method for the region of interest (ROI) signals of a brain, which can be used to improve the classification accuracy of schizophrenia (SCZ). In this experiment, a public dataset is used, and the experimental results show that the classification accuracy of the method proposed in this paper is about 10% higher than that of the traditional normalization method. After the two-sample T-test, the significant regions obtained by the proposed method in the parietal lobe (PAR), cortex (SUB), and temporal lobe (TEM) are different from those of the traditional method, which indicates that the proposed method have some information that the traditional one does not have.
Linking snowmelt‐derived fluxes and groundwater flow in a high elevation meadow system, Sierra Nevada Mountains, California

Quantifying snowmelt‐derived fluxes at the watershed scale within hillslope environments is critical for investigating local meadow scale groundwater dynamics in high elevation riparian ecosystems. In this article, we investigate the impact of snowmelt‐derived groundwater flux from the surrounding hillslopes on water table dynamics in Tuolumne Meadows, which is located in the Sierra Nevada Mountains of California, USA. Results show water levels within the meadow are controlled by a combination of fluxes at the hillslope boundaries, snowmelt within the meadow and changes in the stream stage. Observed water level fluctuations at the boundaries of the meadow show the hydrologic connection and subsequent disconnection between the hillslope and meadow aquifers. Timing of groundwater flux entering the meadow as a result of spring snowmelt can vary over 20 days based on the location, aspect, and local geology of the contributing area within the larger watershed. Identifying this temporal and spatial variability in flux entering the meadow is critical for simulating changes in water levels within the meadow. Model results can vary significantly based on the temporal and spatial scales at which watershed processes are linked to local processes within the meadow causing errors when boundary fluxes are lumped in time or space. Without a clear understanding of the surrounding hillslope hydrology, it is difficult to simulate groundwater dynamics within high elevation riparian ecosystems with the accuracy necessary for understanding ecosystem response. Copyright © 2010 John Wiley & Sons, Ltd.
A Miniature Oven-Controlled Crystal Oscillator (OCXO) with ppt Stability over Temperature Using High-Order Polynomial Temperature Control

This work presents an ASIC-based miniature oven-controlled crystal oscillator (OCXO) with ppt (part-per-trillion) stability over ambient temperature from -40 to 85°C using high-order polynomial temperature control. The proposed OCXO integrates the high-order polynomial temperature control circuit in an IC, thus realizing ppt stability in a miniature 9mm x 7mm footprint. The high-order polynomial temperature control is applied to compensate the complicated non-linear frequency stability error over ambient temperature contributed from higher thermal loss due to miniaturized package and more sensitive thermal effect of integrated circuit. As a result, the proposed OCXO with ppt stability in a miniature footprint provides highly accurate synchronization reference for base station, distributed unit (DUs), and centralized units (CUs) that need longer holdover capability.
Alveolar Bone and Mandibular Canal Segmentation on Cone Beam Computed Tomography Images Using U-Net

A dental implant is a treatment to replace missing teeth. Determining the proper dimensions of dental implants is measured by observing the distance from the mandibular canal (MC) to the alveolar bone (AB). It is crucial to pay careful attention to the location of the MC when planning for a dental implant in the posterior mandible to avoid injury. Therefore, segmenting AB and MC in dental implant planning is essential. While research on MC segmentation using deep learning has been conducted extensively, there has yet to be much research on AB and MC segmentation simultaneously with deep learning. This study proposes using U-Net as a high-performance segmentation technique for multiclass and binary segmentation to segment AB and MC regions. In the output branch of the U-Net architecture, two scenarios are designed, the first is to perform AB and MC segmentation simultaneously, while the second is to perform AB and MC segmentation separately. The study used 563 2D grayscale Cone Beam Computed Tomography (CBCT) images from the coronal slice. The model is trained and tested using K-fold cross validation. The test results show that AB and MC segmentation simultaneously produces the mean intersection of union (IoU) value of 0.85. Meanwhile, AB and MC segmentation separately produced the mean IoU of 0.98 for AB segmentation and 0.81 for MC segmentation. The results of the satisfactory AB and MC segmentation are expected to assist determine implant dimensions in dental implant planning.
A Fast Spectral Analysis Method Based on Sparse Signals in Frequency Domain

In the oscilloscope for electronic testing, the traditional fast Fourier transform (FFT) faces problems such as high computational complexity, low frequency resolution, and unsuitability for sparse signals. To solve these problems, we propose a spectrum analysis method based on sparse Fourier transform (SFT). SFT is a new algorithm that uses the characteristics of the signal itself to obtain the signal spectrum, which is more efficient than the traditional $\text{FFT}^{\underline{12}}$. The main steps of SFT include spectrum rearrangement, filtering, subsampled FFT, localization and $\text{evaluation}^{\underline{13}}$. We use the theoretical framework of SFT to design a spectrum analysis method suitable for oscilloscopes, breaking through the limitations of the spectrum analysis function on the number of operation points in the existing system, and greatly improving the efficiency in sparse signal testing. We verify the effectiveness and superiority of our method through experiments and look forward to its application prospects in the field of electronic testing.
MRI Images Based Brain Tumor Detection Using CNN for Multiclass Classification

Brain Tumor has ranked as the 10th most common cause of death in recent years. An early brain tumor diagnosis, detect the faster treatment which improves survival of patients. A biopsy is a medical procedure that helps to use brain tumor were diagnosed and treated. The Artificial Intelligence supports the radiologist in tumor diagnosis without invasive measure. Specifically, algorithms based deep learning CNN techniques are used for the detection of brain tumor. In this paper, we proposed 9 layers convolution neural network for identifying and categorizing tumor on the basis of texture, location and shape. The CNN models are trained and tested using the Kaggle (Brain Tumor Classification - MRI) dataset. The experimental result shows that improved accuracy and can be used to assist the decision making to doctor.
Glaciers and climate change

This text brings together meteorology and the theory of glacier flow, providing a fundamental understanding of how glaciers respond to climate change. Attention is paid to the microclimate of glaciers and the physical processes regulating the exchange of energy and mass between glacier surface and atmosphere. Simple analytical and numerical models are used to: * investigate glaciers sensitivity to climate change * estimate response times * make an interpretation of historical glacier records * assess the contribution of glacier melt to sea-level rise Modern developments in glacier research, including satellite measurements are discussed in detail, making this a valuable reference source.
On the Secrecy Gain of Isodual Lattices from Tail-Biting Convolutional Codes

The secrecy gain of Construction A isodual lattices obtained from rate 1/2 binary tail-biting convolutional codes is considered. The secrecy gain criterion has been proposed in lattice coding for the Gaussian wiretap channel to characterize the secrecy-goodness performance. The higher the secrecy gain, the smaller the eavesdropper’s success probability of correctly guessing the transmitted message. This work performs exhaustive code searches for even lengths up to 108 to find the best isodual codes obtained from rate 1/2 binary tail-biting convolutional codes of certain memory constraints in terms of secrecy gain and investigates the corresponding isodual lattices. Numerical results indicate that the best results found via this tail-biting technique perform similarly to the best-known isodual codes from the conventional pure double-circulant code construction up to length 40. This approach offers two advantages: (1) it provides reasonably good codes of “any” even lengths, and (2) practical maximal-likelihood decoding is available for these codes.
Ear Detection and Extraction Using a Developed Features Descriptor and Geometric Transformation

Ear recognition for biometric identification purposes is increasingly popular in several recent fields. However, a lack of searches focuses on ear area detection and extraction from the whole scene in real-time conditions. This paper introduces SURFR as a proposed method to gather some prominent features of the human pinna outer helixes, which have been stored as a feature vectors dataset, to detect, extract, and then pick up the desired area of the ear to be identified later. The interest points of features were extracted using the SURF descriptor for both the stored outer-helix dataset and the captured side-face image. Later, the matched features of the stored dataset and the captured person's side-face image have been gathered in two matrices, which were entered to calculate the geometric transformation based on the RANSAC method. The proposal has experimented with UBEAR, AMI, UMSIT, and USTB datasets, in addition to our image data. By leveraging our proposal, results have achieved the real-time demands with an accuracy rate of 95–97 % for detection and extraction respectively.
Video Motion Magnification Based Approach for Improved Recognition of Spontaneous Facial Micro Expressions

Facial expression recognition is studied extensively over decades. Recognition of subtle emotions exhibited as micro-expressions is the most challenging task. Being spontaneous, micro-expressions usually exhibited only temporally and entail minute spatial changes and last over very short duration of time, this makes micro expression recognition (MER) significantly challenging compared to non-spontaneous expression recognition. Since the micro-expressions exhibition is at very low intensity level, in order to enhance distinguish-ability, Eulerian Video Magnification (EVM) method is opted for accentuating subtle facial expression. Local Binary Patterns with Six Intersection Points (LBP-SIP) method, is used as a feature extraction technique for dynamic texture analysis in spatial-temporal domain. MER classification process is carried out using Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel. For this proposed method 76.53% MER accuracy is achieved. The process shows enhancement in overall computational efficiency and robustness in high dimensionality (concatenated feature), which is advantageous for practical purpose.
Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance

Abstract The relationship between the leaf area index (LAI) of temperate coniferous forests in the western United States and Thematic Mapper (TM) data corrected for atmospheric effects and Sun-surface-sensor geometry was influenced by canopy closure, understory vegetation and background reflectance. Strong inverse curvilinear relationships were observed between coniferous forest LAI and both TM bands 3 (0-63-0-69μm) and 5 (1-55-1-75μm). The inverse relationships are explained by increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI. A strong positive relationship was observed between LAI and TM band 4 (0-76-0-90μm) radiance in stands with greater than 89 per cent canopy closure. Open stands with low overstory LAI had elevated band 4 radiances caused by understory vegetation and/or a highly reflective granite background. Old growth stands with incomplete overstories had low band 4 radiance.
Experimental modeling of CO2‐fluid‐rock interaction: The evolution of the composition and properties of host rocks in the Baltic Region

The objective of this study was to determine the influence of the possible CO2 geological storage in the Baltic Region on the composition and properties of host rocks to support more reliable petrophysical and geophysical models of CO2 plume. The geochemical, mineralogical, and petrophysical evolution of reservoir sandstones of Cambrian Series 3 Deimena Formation and transitional clayey carbonate caprocks of Lower Ordovician Zebre Formation from two offshore structures in Latvia and Lithuania and two onshore structures in Latvia, induced by laboratory‐simulated CO2 geological storage, was studied for the first time in the Baltic Region. The geochemical, mineralogical, and petrophysical parameters were measured in 15 rock samples, before and after the alteration experiment. The diagenetic alterations of reservoir rocks were represented by carbonate cementation in the top of the onshore South Kandava structure, and quartz cementation and compaction, reducing the reservoir quality, in the deepest offshore E7 structure in Lithuania. The shallowest E6 structure offshore Latvia was least affected by diagenetic processes and had the best reservoir quality that was mainly preserved during the experiment. Carbonate cement was represented by calcite and ankerite in the transitional reservoir sandstones of very low initial permeability in the upper part of the South Kandava structure. Its dissolution caused a significant increase in the effective porosity and permeability of sandstones, a decrease in the weight of samples, bulk and matrix density, and P and S wave velocities, demonstrating short‐term dissolution processes. Only slight geochemical changes occurred during the experiment in offshore reservoir sandstones. Minor dissolution of carbonate and clay cements, feldspar and some accessory minerals, and possible minor precipitation of pore‐filling secondary minerals associated with slight variations in rock properties, demonstrating both short‐term and long‐term processes, were suggested. As a novelty, this research shows the relationship between diagenetic alterations of the Cambrian Series 3 Deimena Formation reservoir sandstones and their changes caused by the CO2 injection‐like experiment.
Geochronology and geodynamics of Scottish granitoids from the late Neoproterozoic break-up of Rodinia to Palaeozoic collision

Thirty-seven granitoids from Scotland have been dated using the sensitive high-resolution ion microprobe zircon method. Granitoids were intruded during: (1) crustal stretching at c. 600 Ma after Rodinia broke up (A-types); (2) the Grampian event of crustal thickening when the Midland Valley Arc terrane collided with Laurentia at c. 470 Ma (S-types); (3) erosion and decompression of the over-thickened Laurentian margin at c. 455 Ma (S-types); (4) subduction of Iapetus Ocean lithosphere under Laurentia starting at 430 Ma (I-types); (5) roll-back beginning at 420 Ma (I-types); (6) bilateral slab break-off and lithospheric delamination at 410 Ma (I- and S-type granites) when Baltica hard-docked against the Northern Highland terrane and Avalonia soft-docked against the Grampian Highland terrane. Far-field Acadian events at 390 Ma were recorded by I-type granites intruded along active sinistrally transpressive faults. I-types formed in lower crustal hot zones above subduction zones, whereas S-types formed in lower crustal hot zones above lithospheric windows through which hot asthenosphere had risen.
Automatic Creation of TVS SPICE Models for ESD System Level Simulations

The development and implementation of an algorithm for the automatic generation of a complete system-efficient ESD design (SEED) model for transient voltage suppressors (TVS), is presented. The algorithm uses transmission line pulse measurement data and S-parameter measurements to compute values for a small-signal model, IV curve model, and transient model. These models are combined into a single schematic to simulate the complete behavior of the device under test (DUT). The algorithm is constrained to TVS diodes without snapback and varistors, the resulting model matches the measured device behavior with high accuracy.
A Robust Adaptive Terminal Sliding Mode Control for Lane-keeping in Road Vehicles

In this paper, a novel adaptive terminal sliding-mode control (ATSMC) method is developed for road vehicles with uncertain dynamics. It is shown that the designed adaptive laws can recursively update the controller parameters based on the estimation values of the unknown parameter bound in the Lyapunov sense, and then the lateral position error can be driven to zero in a finite time. In comparison to the conventional sliding-mode-based vehicle lateral control method, the proposed ATSMC not only ensures fast error convergence, but also eliminates the requirement for known bounded information in controller design. Simulation results are presented to demonstrate the good performance of the proposed ATSMC scheme.
An Approach to Network Service Placement Reconciling Optimality and Scalability

The inevitable transition from physical dedicated hardware devices towards lightweight containerized reusable software modules with Network Function Virtualization (NFV) introduces countless opportunities while presenting several unprecedented challenges. Satisfying NFV expectations in post-5G networks heavily depends on the efficient placement of network services. In this paper, after modeling the placement problem and proposing the exact resolutions using Integer Linear Programming (ILP) and Column Generation (CG), we propose our deterministic placement solution, capable of obtaining optimal results with the scalability of a heuristic-grade approach. Our method is organized as a Branch and Bound (BnB) structure, applying Artificial Intelligence (AI) search strategies (especially A*) to address the problem of network service placement. We believe that it is suitable for a range of applications in online placement scenarios, whether we concentrate on the quality of the results or on the strict time constraints. We are interested in the popular objective of Service Acceptance (SA) maximization and have carried out several extensive evaluations. The obtained results confirm the effectiveness of our solution.
Laser Frequency-Offset Locking Over a 1577 km Fiber Link Based on LSTM Neural Network

SummaryWe demonstrated a digital frequency offset locking system based on long short-term memory (LSTM) neural network and locked two free-running lasers at 1550.12 nm with 291.25 MHz offset after transferring through a 1577 km fiber link. Within 1.63 hours of locking, the root-mean-square (RMS) error of frequency offset is 47.1592 kHz. Compared to traditional digital locking systems using proportional-integral-derivative (PID) control, our locking system can effectively resist frequency offset drifting between lasers caused by temperature variation and requires no complicated parameter tuning process.
Micropulse Lidar Cloud Mask Value-Added Product Technical Report

Lidar backscattered signal is a useful tool for identifying vertical cloud structure in the atmosphere in optically thin clouds. Cloud boundaries derived from lidar signals are a necessary input for popular ARM data products, such as the Active Remote Sensing of Clouds (ARSCL) product. An operational cloud boundary algorithm (Wang and Sassen 2001) has been implemented for use with the ARM Micropulse Lidar (MPL) systems. In addition to retrieving cloud boundaries above 500 m, the value-added product (VAP) named Micropulse Lidar Cloud Mask (MPLCMASK) applies lidar-specific corrections (i.e., range-square, background, deadtime, and overlap) as described in Campbell et al. (2002) to the measured backscattered lidar. Depolarization ratio is computed using the methodology developed by Flynn et al. (2007) for polarization-capable MPL systems. The cloud boundaries output from MPLCMASK will be the primary lidar cloud mask for input to the ARSCL product and will be applied to all MPL systems, including historical data sets.