Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Rydberg atom cell sensitivity on the incoherent detection method

The Rydberg atom cell sensitivity on the incoherent detection microwave is analyzed as the probe laser noise is considered. First, the probe laser noise transfer characteristic is theoretically derived. Second, the minimum detectable microwave electric field intensity is obtained by simulation at different signal noise ratio of the input probe laser. These simulation results suggest that the microwave detection sensitivity of the Rydberg atom cell can be improved by two ways. One is to increase the input probe laser signal noise ratio. The other one is to decrease the Rabi frequency of the coupling laser. As the coupling Rabi frequency $\Omega_{c}=2\times 10^{8}\left(rad/s\right)$ and the probe signal to noise ratio is 20.5dB, the minimum detectable microwave electric field intensity is $1.976 (\mu V/\text{cm})$.
Attribute Analysis and Diagnosis of LUNG CT Images of COVID-19

Computed Tomography (CT) is an authoritative verification standard for patients with Corona Virus Disease 2019 (COVID-19). Automatic detection of lung infection through CT is of great significance for epidemic prevention and control and prevention of cross-infection. The accuracy of existing lung CT image segmentation methods is not high, and due to the privacy protection measures of hospitals, the number of COVID-19 lung CT data sets is too small, which is prone to over-fitting during training. In this paper, we propose a qualitative mapping model for the diagnosis and localization of COVID-19 lesions. The binary image processed by U-net network is used as input, and lung CT is segmented as four attributes, and attribute diagnosis is carried out with the help of correlation matrix and transformation degree function. Experiments show that this method not only avoids the over-fitting risk of data sets, but also increases the robustness of data. Experiments also prove that this design has higher accuracy than the simple neural network learning.
Research on Network Slice Resource Scheduling in Virtual Power Plant

Virtual power plant is regarded as the ultimate configuration of energy internet and the terminal example of energy cloud. Firstly, this paper analyzes the value of virtual power plant to the current power energy system and classifies the control structure of Virtual Power Plant, and analyzes their characteristics; Then it introduces several important characteristics of virtual power plant and its special requirements for communication network. Secondly, it expounds the important technology of network slicing and the advantages of network slicing using virtualization technology to flexibly and differentially match resources according to business requirements. Then it introduces the architecture of network slicing and the isolation scheme of network slicing in different networks in detail. On this basis, this paper considers scheduling the network slices in the access network with virtual power plant according to the service priority, virtually isolating the services in the access network, resisting the attacks that may impact the communication system, and meeting the quality of service of the user side on the premise of reasonably regulating the network resources.
Convergent Approximate Message Passing by Alternating Constrained Minimization of Bethe Free Energy

Approximate Message Passing (AMP) allows for Bayesian inference in linear models with non identically independently distributed (n.i.i.d.) Gaussian priors and measurements of the linear mixture outputs with n.i. i.d. Gaussian noise. It represents an efficient technique for approximate inference which becomes accurate when both rows and columns of the measurement matrix can be treated as sets of independent vectors and both dimensions become large. It has been shown that the fixed points of AMP correspond to extrema of a large system limit of the Bethe Free Energy (LSL-BFE), which represents a meaningful approximation optimization criterion regardless of whether the measurement matrix exhibits the independence properties. However, the convergence of AMP can be notoriously problematic for certain measurement matrices and the only sure fix so far is damping (by a difficult to determine amount). In this paper we revisit the AMP algorithm by rigorously applying an alternating constrained minimization strategy to an appropriately reparameterized LSL-BFE with matched variable and constraint partitioning. This guarantees convergence, and due to convexity in the Gaussian case, to the global optimum. We show that the AMP estimates converge to the Linear Minimum Mean Squared Error (LMMSE) estimates, regardless of the behavior of the variances. In the LSL, the variances also converge to the LMMSE values, and hence to the correct values.
Ultrafast Object Detection on High Resolution SAR Images

Synthetic Aperture Radar (SAR) images provide a good means of analyzing large areas despite fog, clouds, etc. High resolution SAR images enable the detection of a wide variety of relevant targets to detect potential threats and assess the situation. However, the vast amount of information and the peculiarities of SAR imagery require a huge amount of trained experts and/or time to comb through the data. In [1], we demonstrated an AI based approach to detect and classify objects in SAR images with a deep neural network and modern training techniques. In this publication, we investigate, how much the neural network can be reduced without sacrificing the classification performance. An ablation study is shown demonstrating the relevant parts of the neural network.
Variance Analysis for Radar-Boosted Estimator of Host Vehicle Motion

Planar motion of a rigid body equipped with multiple radar sensors can be estimated from their Doppler and azimuth measurements. In automotive applications, such capability introduces an opportunity of using radar system as a self-contained source of information about vehicle speed, yaw rate and sideslip. In this paper we apply variance analysis methods to investigate relative merits between such radar-based estimates and the traditional approach that uses wheel speed and inertial measurements. Significant differences in characteristics of these two methods are observed in experimental results. Variance analysis is therefore simultaneously applied to the results of a generic fusion scheme with interacting multiple models.
Classification of Crop Disasters Based on DenseNet

In agricultural production, crops are vulnerable to various diseases, resulting in yield reduction. Therefore, it is important to judge the condition of crops and deal with it timely in agricultural production. To address these issues, we use convolution neural network to establish a model for rapid classification of crop diseases. Based on the fine-tuning and migration learning training strategies with different weights, the DenseNet-201 network structure is used to train the data sets of collected crop disease images. The SE (Squeeze-and-Excitation Networks) module is added to the DenseNet network structure to solve the loss caused by the different importance of different channels of the feature map in the convolution pooling process, and the Leak ReLu function in the DenseNet network is used to improve the model fitting ability.The experimental results show that even for the complex data sets, the model can still achieve good results, and have a high accuracy of the target crop diseases.
Modifying Value Stream Mapping for the Early Integration of RFID into the Factory Planning Process

RFID is a tool for the automated identification of products and load units used in industrial environments. Therefore, a thorough planning process is necessary for the successful implementation of RFID systems into production and logistical environments. Although existing planning methods for RFID already ask for the consideration of RFID from the beginning of the factory planning process, no concepts exist on how to integrate these systems into early site models. This paper, therefore, presents an adapted value stream mapping concept, the informational value stream mapping method, in which the aspects of informational waste are added to the specifications and key figures of the material and information flow. For this, a new parameter, the information flow resistance, was defined to locate and grade the informational waste in the planned processes. The results of the adapted method can then be used to plan the introduction of RFID systems into the material and information flow processes, ensuring the decrease of informational waste inside these processes.
Implementation of High Voltage Converter Circuit Based on Transformer Switching Method for Biphasic and Monophasic External Defibrillator

High voltage converter on external defibrillator was built using transformer switching method. An input of 12V DC supply voltage was converted to a maximum 2000V. The voltage conversion result was used for energy charging process of storage capacitors. The proposed defibrillator can adjust the energy by changing duty cycle of the Pulse Width Modulation (PWM) signal on Insulated Gate Bipolar Transistor (IGBT). In this case, IGBT is used to control the current and voltage in the transformer. The maximum energy generated by the conversion process is 500 joules and has met the requirement of the defibrillation. In addition, this device was designed to discharge energy in both biphasic and monophasic modes. The mode can be set according to the needs of the defibrillation process.
Comparison of Deep Learning Methods for Landslide Semantic Segmentation Based on Remote Sensing Images

Comprehensive identification of geological hazards is still one of the most important work in China's disaster prevention, and convolutional neural network is an important tool to realize early intelligent and automatic identification of geological hazards. In order to compare the accuracy of different network structures in landslide remote sensing image recognition, in this paper, U-Net, PSP-Net, Deeplabv3+, Mask R-CNN models are used to realize automatic recognition of landslide remote sensing images, with IOUs of 0.61, 0.67, 0.52 and 0.71 respectively. The results show that the backbone feature extraction network combined with the deep residual structure has better feature extraction ability of landslides, weakening the influence of factors unrelated to landslides. The structure with multi-scale feature fusion can extract landslide areas of different scales, which effectively improves the accuracy of landslide identification in remote sensing images.
IoT Based Cases Management and Analysis for Intelligent Dynamic Discipline Inspection with Supervision

Recently, to adapt to the new situation and requirements of the times, the discipline inspection commission uses high-tech such as intelligent discipline inspection platform solutions to achieve precise supervision in key areas and timely early warning of emerging integrity risks. An early warning mechanism can be set up to warn, prevent and control emerging and tendentious integrity risks in key areas. In particular, the development of Internet of Things (IoT) has been widely applied in many application scenarios, which can be also applied to the intelligent discipline inspection platform, in order to give some new aspects and views. Hence, in this paper, we study IoT based intelligent dynamic discipline inspection and supervision cases management and analysis, whereas we analyze the system inspection and supervision performance, in terms of the inspection and supervision outage probability. We also give the analytical form for the system inspection and supervision outage probability of wireless monitor. Finally, some simulation results are provided to verify the proposed analytical studies for the considered discipline inspection commission systems.
Quadcopter Design With Waypoint Mission Using PID Control System

The quadcopter is one of the Unmanned Aerial Vehicles that can fly vertically. Problems with the drone such as unresponsive controls and choosing the right control system which has an impact on the instability of the drone. Proportional, Integral, Derivative (PID) has better performance to be applied to the quadcopter. This study applies PID control to the quadcopter control which is tested with a waypoint system. Quadcopter design uses various components such as flight controller, brushless motor 920 kv, electronic speed controller. The results of the calculation of the constant PID value are applied to the drone flight controller and the test is carried out by carrying out a waypoint tracking mission with the PID controller. Testing with a rectangular route waypoint shows that the quadcopter can fly according to the predetermined waypoint with the largest roll deviation value is $20.93^{o}$, at pitch by $24.01^{\mathrm{o}}$, and the largest deviation change at yaw is $205^{\mathrm{o}}$ which means the quadcopter can carry out waypoint tracking missions using PID control with high stability.
A Lightweight Network for Expression Recognition Based on Adaptive Mixed Residual Connections

Facial expressions occupy a large proportion of the communication between people. It is a technology in which computers try to understand human emotions by analyzing face information, and it has become a hot topic in the field of computer vision. However, there are few reliable expression recognition databases in the expression recognition task. The recognition is not robust enough under uncontrolled conditions, and it takes a long time to train a facial expression recognition model using the deep learning method. This paper uses ResNet18 as the backbone network to construct An Adaptive Mixed Residual Connected Expression Recognition Lightweight Network. Aiming at the characteristics of inter-class similarity and intra-class difference in the expression feature information, the network designs a dual-branch mixed residual connection method to ensure the completeness and accuracy of the extracted features, and designs the R_Ghost layer to reduce the huge amount of parameters caused by the deepening of the network. Finally, the adaptive feature fusion module is used to connect the feature information of each branch, and select the appropriate level output for the final expression classification. The experimental results show that the proposed method can achieve better recognition efficiency than existing algorithms on the dataset FER2013 under uncontrolled conditions using a single network for training and without additional training data. With a model covariance of 10.92M, the recognition accuracy reaches 73.50%. It also achieved good recognition accuracy of 88.34% on the Oulu datasets.
Integration of Massive MIMO and Machine Learning in the Present and Future of Power Consumption in Wireless Networks: A Review

The steady increase in data traffic rates and systems’ complexity have contributed to the information and communication technologies (ICT) sector’s increased energy consumption and CO2 emissions. These pose a significant challenge to the telecommunication industry and the environment. This challenge has necessitated considering energy efficiency as a critical design pillar in 5G and future wireless networks. As a result, current research efforts for future wireless networks focus on minimising energy usage and improving efficiency. This work investigates several energy optimisation techniques in the present and future wireless networks, their contributions, advantages, and limitations. Based on the review of different techniques, we discuss the architecture of the massive MIMO (mMIMO) technique, including its operation and requirements. We also present the performance evaluation of mMIMO using different precoding algorithms, which is crucial for energy efficiency in future wireless networks. We further review incorporating intelligence using a Machine Learning (ML) approach in switching off underused mMIMO arrays to minimise energy usage. Finally, we discuss several critical open research issues in mMIMO and ML that make future research and implementation possible in next-generation wireless networks.
ACTDet: Augmented Cross Grained Transformer for Industrial PCB Detection

Printed circuit board plays a pivotal role in the development of industry. Automatic detection of targets in printed circuit board can greatly save the human resources and enhance the speed of detection. However, due to the inherent characteristic of printed circuit board, the targets are usually small objects with various illumination and shapes. We propose a general architecture, named ACTDet, to tackle the problems of small objects detection in printed circuit board. In specific, we design a cross grained transformer to leverage the low-level and high-level features and enhance the inherent interaction of different feature maps with various receptive fields. A number of data augmentation strategies are adopted to enrich training data, avoid overfitting, and improve robustness of detector. What’s more, a channel-guided attention block is utilized to reweight the contribution of each channel and encourages the network to learn the most important features. Extensive experiments illustrate the effectiveness of the proposed ACTDet network and ACTDet achieves state-of-the-art performance in our proposed well-annotated dataset.
Collisionless Shocks and TeV Neutrinos before Supernova Shock Breakout from an Optically Thick Wind

During a supernova explosion, a radiation-dominated shock (RDS) travels through its progenitor. A collisionless shock (CS) is usually assumed to replace it during shock breakout (SB). We demonstrate here that for some realistic progenitors enshrouded in optically thick winds, such as possibly SN 2008D, a CS forms deep inside the wind, soon after the RDS leaves the core, and therefore significantly before SB. The RDS does not survive the transition from the core to the thick wind when the wind close to the core is not sufficiently dense to compensate for the $r^{-2}$ dilution of photons due to shock curvature. This typically happens when the shock velocity is $\lesssim 0.1 {\rm c} \, (\frac{u_{\rm w}}{10\,{\rm km/s}}) (\frac{\dot{M}}{5 \cdot 10^{-4} \, {\rm M}_\odot {\rm /yr}})^{-1} (\frac{r_\ast}{10^{13}\,{\rm cm}})$, where $u_{\rm w}$, $\dot{M}$ and $r_\ast$ are respectively the wind velocity, mass-loss rate and radius of the progenitor star. The radiative CS results in a hard spectrum of the photon flash at breakout, which would produce an X-ray flash. Cosmic ray acceleration would start before SB, for such progenitors. A fraction of secondary TeV neutrinos can reach the observer up to more than ten hours before the first photons from breakout, providing information on the invisible layers of the progenitor.
Constraints on the broad line region from regularized linear inversion: velocity–delay maps for five nearby active galactic nuclei

Author(s): Skielboe, Andreas; Pancoast, Anna; Treu, Tommaso; Park, Daeseong; Barth, Aaron J; Bentz, Misty C | Abstract: Reverberation mapping probes the structure of the broad emission-line region (BLR) in active galactic nuclei (AGN). The kinematics of the BLR gas can be used to measure the mass of the central supermassive black hole. The main uncertainty affecting black hole mass determinations is the structure of the BLR. We present a new method for reverberation mapping based on regularized linear inversion (RLI) that includes modelling of the AGN continuum light curves. This enables fast calculation of velocity-resolved response maps to constrain BLR structure. RLI allows for negative response, such as when some areas of the BLR respond in inverse proportion to a change in ionizing continuum luminosity. We present time delays, integrated response functions, and velocity-delay maps for the $\rm{H}\,\beta$ broad emission line in five nearby AGN, as well as for $\rm{H}\,\alpha$ and $\rm{H}\,\gamma$ in Arp 151, using data from the Lick AGN Monitoring Project 2008. We find indications of prompt response in three of the objects (Arp 151, NGC 5548 and SBS 1116+583A) with additional prompt response in the red wing of $\rm{H}\,\beta$. In SBS 1116+583A we find evidence for a multimodal broad prompt response followed by a second narrow response at 10 days. We find no clear indications of negative response. The results are complementary to, and consistent with, other methods such as cross correlation, maximum entropy and dynamical modelling. Regularized linear inversion with continuum light curve modelling provides a fast, complementary method for velocity-resolved reverberation mapping and is suitable for use on large datasets.
Board-level shielding with magnetic absorber sheet

The ongoing trend to miniaturize electronic devices with the aim of providing more advanced functionalities and greater performances makes the detection of electromagnetic interference (EMI) a recurring problem. These requirements result in design restrictions that make it complicated to use basic EMI shielding techniques. Consequently, innovative shielding solutions are needed. Thereby, the board-level shielding (BLS) solutions are widely used in printed circuit board (PCB) applications to reduce the intra-system EMI of integrated circuits (ICs) and improve the immunity of the electronic devices. This contribution focuses on analyzing a magnetic absorber sheet combined with a metal layer as a board-level shield solution. Using this combination is expected to obtain higher shielding effectiveness than using only a magnetic sheet by obtaining the absorbing properties of the magnetic material and the losses mechanism of a metal, such as reflection and eddy current losses. One of the main advantages of using this hybrid structure is that it does not require redesigning the electronics since it can be stuck over the EMI source. Therefore, a magnetic material with different thicknesses is combined with metal layers. These hybrid combinations are compared with the performance of a shielding cabinet in order to determine its performance. The results presented are based on the spectral and spatial magnetic near-field distribution measurement.
U-Net Variations for Spontaneous Intracranial Hemorrhages Detection on CT Images

Brain injuries represent one of the most severe medical problems that affect people health worldwide. These injuries are classified in different types, being the intracranial hemorrhages (ICH) the most critical. A delayed treatment of ICH may lead to irreparable brain damage such as permanent disability, significant patient quality-of-life change, and even worse, death. Brain computed tomography (CT) examination is the preferred method to diagnose ICH. However, manually reading brain CT images can be time-consuming, delayed, and prone to human error, which may worsen the patients’ health. In this context, a new intracranial hemorrhages detector based on a deep CNN architecture that automatically identifies and segments ICH in CT scan images is proposed. Based on the standard U-Net, two architectures named, Unet1 and Unet2, have been developed and validated on a public database. The Unet2 model trained with 1,000 epochs and a batch size of 32 obtained the best results. The mean of interception over union score of 0.85 and Dice coefficient of 0.89 during the training stage revealed a capacity to successfully segment more than the 85% of the lesion area. Additionally, the model was tested with unseen images in training and obtained a mean of interception over union and dice coefficient scores of 0.86 and 0.91, respectively. The latter results demonstrate the generalization strength of the model to accurately identify and segment the lesion area and the suitability to be implemented in the final ICH detector.