Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deployable Containerized 5G Core Solution for Time Critical Communication in Smart Grid

Communication within substatation automation systems in a smart grid environment is often time-critical. The time required for exchanging information between intelligent devices should be within a few milliseconds. The International Electrotechnical Commission (IEC) 61850 standard has proposed the Generic Object Oriented Substation Event (GOOSE) protocol to achieve this goal. However the transmission of GOOSE messages is often limited to a small Local Area Network (LAN). In this demo we demonstrate the feasibility of using 5G for GOOSE-based time critical communication in a large-scale smart-grid environment and present a deployable 5G core solution using container-based virtualization technology. The radio part of the demo is emulated. The demo also shows that the delay introduced by the core network is in the order of sub milliseconds while the one-way delay without a real radio access network is less than 1 ms which is well below the total delay budget for GOOSE.
Three-User Cooperative NOMA Transmission

This letter presents a new downlink cooperative non-orthogonal multiple access (NOMA) transmission scheme to serve three users within only two time slots. The scheme involves a base station (BS), two direct-link users and one indirect-link user. The BS sends superposed signals to the two direct-link users which decode and forward the signals to the indirect-link user in an alternating fashion. Closed-form expression is derived for the sum-rate of the proposed scheme and a simple expression of sum-rate is also derived in the high signal-to-noise ratio (SNR) region. In the presence of strong inter-user interference (IUI), the proposed scheme can be applied by having each direct-link user to detect the IUI first and then cancel the IUI to decode the signals destined for the indirect-link user and itself. Simulation results show that the scheme with configurable decoding orders at the users is able to achieve a higher sum-rate than existing orthogonal multiple access (OMA) based alternatives.
Visual Representation of Slope Stability for Landslide Monitoring Systems

Different regions of a three-dimensional map of a landslide-prone slope can be colored green, yellow/orange, or red at different times to indicate regions of stability, marginal stability, and instability; such visual representations that vary with time can aid in understanding the processes contributing to slope stability or instability. More importantly, these representations serve as powerful tools to communicate pertinent research findings to the people living on the slope. We have deployed monitoring systems at landslide-prone hills in the Western Ghats mountain range of South India and the Himalayas of Northeast India. Pore water pressure data from piezometers of the monitoring systems are used in conjunction with other geotechnical parameters to evaluate the stability of these locations. In the absence of piezometer data, rainfall data can be used to obtain estimates of stability. The stability indices are combined with digital elevation data to generate graphic representations of slope stability. This paper details the steps involved in generating such representations and discusses their value in a landslide monitoring system.
The Design and Implementation of a RESTful IoT Service Using the MERN Stack

With the increasing number of Internet of Things applications (IoT), an enormous number of IoT devices are connected to the Internet and require processing from the Cloud. Although the Cloud theoretically has unlimited resources, it is still a significant challenge for the Cloud to perform a real time response. This project is a designed and developed RESTful IoT service for a quick communication between IoT devices and the Cloud, using the MERN stack. As a gateway, the proposed system gathers data from IoT devices such as wireless sensors, for the Cloud to further process on it. The Representational State Transfer (REST) model is adopted in the proposed system. It has a good performance in terms of response time and scalability. Experiments further verified the performance of the proposed system.
Network-Aware Data Transmission Scheduling for Saving Energy in Cellular Networks

Cellular data communication has been shown to be a major consumer of battery energy in mobile devices. Existing energy conserving efforts mainly consider issues about the tail time and signal strength, but ignore the network type. However, our measurement studies show that both the tail time and data transmission performance vary across heterogeneous cellular networks, indicating that the network type has a significant impact on energy consumption of cellular data communication. Moreover, scheduling conflict occurs in transmitting delay-tolerant data in different types of cellular networks. Considering the network type, tail time, and signal strength, we model the energy conservation problem in data transmission scheduling as an optimization problem. To address the problem, we design a network-aware data transmission scheduling algorithm NetDS to optimize the total energy consumption and ensure that the delay-tolerant data is transmitted before the deadline. Through predicting the network type and controlling delay-tolerant transmission aggregation, NetDS effectively balances the energy consumption affected by multiple factors. Experimental results show that NetDS is effective and outperforms well against existing methods.
Two transmission window slow light metamaterial

A two transmission window slow light metamaterial has been designed, which are not affecting each other. The unit cell of the proposed structure is made up of three parallel cut-wires in a different size. Owing to the diverse mutual coupling types of three cut-wires, the transmission windows of the EIT-like structure are unique, which opens a door to realize multiple channel slow light equipment for numerous potential applications.
Optimal Energy Resource Allocation Model in Provincial Electricity Market

In order to alleviate the contradiction between the gradual expansion of power supply and demand and realize the optimal allocation of power resources in a wider range, this paper focuses on an integrated scheduling scheme that is formulated as a decentralized energy management problem to obtain the optimal solution by means of the alternating direction method of multipliers (ADMM) algorithm. Firstly, a two-level electricity market framework including provincial electricity market (PEM) and regional electricity market (REM) is proposed. Secondly, the constraints to be satisfied in the power trading plan of PEM and REMs are constructed, and the centralized dispatching model of the whole network is established. Moreover, the centralized scheduling model is reconstructed by using ADMM algorithm, and a completely decentralized scheduling model is established. Finally, the effectiveness of the proposed model and method is verified by a practical example.
Development of Compact Gamma Camera using SoC-FPGA based Modularized DAQ

Traditional gamma cameras have large volume and heavy weight because of their large photomultiplier tube volume and low spatial resolution. Therefore, gamma cameras have the disadvantage of limited use due to their size and weight. In this study, we aim to reduce the total weight and size of the gamma camera for various applications. The proposed gamma camera uses a 3D metal printer to fabricate a collimator, and is designed to implement a high-resolution gamma camera using a discrete GAGG scintillator and SiPM array. The dimensions of the designed gamma camera are 86 × 65 × 78.5 mm3, and its weight is 934 g. The initial performance of the gamma camera design was first discovered as a measure of the preamplifier circuit with symmetric charge division. The data acquisition device used in the gamma camera was designed for various applications and can be expanded up to 16 channels. We also used the system on a chip field-programmable gate array for hardware and software flexibility. Currently, we are quantitatively studying the initial performance of the proposed compact gamma camera.
Modeling Data-Driven Liver Cancer Prediction with Medical Knowledge on Chinese Population

According to the report in 2018, liver cancer is the top ten of malignant tumors. Although various techniques were developed to assess the liver cancer risk, but there is no specific model targets to the characteristics of Chinese population. In this study, we collected a total of 49,076 Chinese patients with cancer risk using questionnaires, which are designed by the domain experts. We extracted 14 features of liver cancer from the raw 107 questions under the instruction, and compared six competitive methods for risk prediction. The experimental results indicated the Support Vector Machine (SVM) outperform other methods of medical knowledge. By leveraging the combination of the appropriate features and methods, it is found that our model is efficient in modeling liver cancer risk prediction on Chinese population.
A Prediction Method of Peak Time Popularity Based on Twitter Hashtags

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.
Detecting Tourist's Preferences by Sentiment Analysis in Smart Cities

Smart tourism is one of the significant areas of smart cities. One of the important tasks of smart tourism systems is to intelligently and automatically extract a user's preferences. This paper investigates the automatic extraction of users' preferences in smart tourism. To this end, users' comments published in social networks are exploited as a rich data source that implicitly includes their preferences. The proposed method extracts users' preferences through semantic clustering the comments and sentiment analysis. Evaluation results indicate high values of precision, recall, and f-measure parameters in tourist preference extraction.
Linear-to-Circular (LTC) Antenna With Polarization Conversion Metasurface

In this work, a linear-to-circular (LTC) polarization conversion metasurface (PCMS) was proposed and studied. By placing PCMS close to a patch antenna, the PCMS converted a linearly polarized wave generated from the patch antenna into a circularly polarized wave. The PCMS antenna was designed to operate at the same frequency of about 13GHz with the patch antenna. With an axis ratio less than 3dB in 12.7–14GHz, the PCMS antenna possessed a much wider axis ratio than that of the conventional patch antenna by computer simulation. To verify the simulated results, the PCMS antenna was fabricated and measured. The simulated and measured results showed good agreements. Based on the designing technique, a setup of polarized conversion antenna could be developed and applied in the future.
Calculation of Optical Force at Nanoscale Using Finite Element Method and Maxwell Stress Tensor

The manipulations of nanoparticles based on the optical force can avoid mechanical damage to the targets, and the intensity of the optical force can be precisely controlled by the light source. In this paper, a numerical method combining finite element method (FEM) with Maxwell stress tensor (MST) method is presented to calculate the optical force produced by laser source. Gaussian beam is adopted as the excitation to simulate the practical laser source. An example of hybrid nanosphere target is analyzed to show the performance of the proposed method for the calculation of optical force.
Lightweight Authenticated-Encryption Scheme for Internet of Things Based on Publish-Subscribe Communication

The resource-constrained nature and large-scale adoption of Internet of Things (IoT) have a significant challenge for securing IoT applications. This necessitates a robust and lightweight security architecture and schemes as the existing traditional Internet security architecture and protocols require huge resources and lack of end-to-end security mechanism. In this research, a resource efficient end-to-end security scheme has been proposed by offloading computations and storage of security parameters to fog nodes in the vicinity. In addition, a symmetric-key payload encryption has been used to minimize the overhead of message communication in the resource-contested IoT environment. The analysis shows that the proposed scheme outperforms Transport Layer Security (TLS) in resource usage while it maintains equivalent authenticated end-to-end communication between communicating IoT nodes. The proposed end-to-end security scheme saves more communication bandwidth and incurs less overhead as compared to existing TLS-based security schemes. In particular, the proposed system uses less number of handshakes and achieves a decrease in the number of transmitted messages (approximately 184 bytes as compared to compared TSL message size of 332 bytes) for every handshake. Further, it has been demonstrated through experiments that the proposed security method incurs less overheads as compared to the TLS bandwidth consumption considering a single connection session during message subscription.
Teaching Analytics: Current Challenges and Future Development

Teaching Analytics is an emerging and challenging field of research that can have a great impact on educational design methodologies as well as on deliveries by teachers to students. In this work, we analyze the most relevant literature on the subject. Four axes for work were identified: first, integration of learning and teaching analytics; second, multimodal analysis or analysis of data collected by devices in real time in the classroom; third, digital literacy in analytics for teachers; and fourth, planning and evaluation of teaching activities. Several limitations and challenges were found for the advancement of the teaching analytics area, but the most relevant limitation is the temporary separation between the delivery and the analysis of their results, in addition to not having a standard measure of student performance. The most important challenge is the registration and multimodal analysis in real time for the taking of action of the teacher in an immediate form.
Characterization of TOF-PET detectors for a second generation radiofrequency-penetrable PET insert for simultaneous PET/MRI

Simultaneous PET/MRI combines two powerful and complementary modalities, providing multiparametric information to assess the anatomic as well as biochemical basis of disease. However, the high cost of the current commercial integrated PET/MRI systems limit the long-term potential, accessibility, and availability of PET/MRI. A portable PET insert provides a cost- effective alternative to achieve simultaneous PET/MRI for the sites that are already equipped with MR systems. Therefore, we are developing a radiofrequency (RF)-penetrable MR-compatible time-of-flight (TOF)-PET insert that can acquire simultaneous PET/MR data using the MR systems built-in body coil for RF transmission. In this study, we characterized performance of two detector modules in the MR bore under four different MRI conditions: B0 only, echo planar imaging (EPI), gradient echo (GRE), and fast spin echo (FSE), each running for 15 minutes. The one-hour study results for coincidence timing resolution, energy resolution, coincidence count rate and ASIC chip temperature of two detector modules demonstrate the PET performance stability as well as the MR compatibility of the PET detectors under different MRI sequences. The global coincidence timing and energy resolution achieved was 264.21 ± 4.82 ps FWHM and 13.66 ± 0.08 % FWHM, respectively.
Design and Realization of Double TEC Air Conditioning for Outdoor Intelligent Control Cabinet

Indoor temperature and humidity of the outdoor intelligent control cabinet are of great influence on the safe and stable operation of intelligent electronic devices in the control cabinet. Influence of atmospheric environment factors on components in intelligent control cabinet is analyzed in this paper, and working principle and characteristics of TEC air conditioning are introduced. On the basis of these, a new temperature control scheme of the air conditioning is proposed to meet the long-term safety and stable operation requirements of outdoor intelligent control cabinets, in which the conditioning is divided into two parts and the linkage rotation system is adopted. Moreover, double TEC air conditioning control method of intelligent control cabinet is studied. The effectiveness of TEC air conditioning outdoor control cabinet in heat dissipation and dehumidification in high temperature are verified by test results.
DualRes-UNet: Limited Angle Artifact Reduction for Computed Tomography

In some special cases of computed tomography (CT), limited angle problem occurs due to the limitation of imaging configuration and system design, which could cause severe artifacts in reconstructed CT images. In this work, we explore a Convolutional Neural Network (CNN) architecture for limited angle artifact reduction in CT imaging, named as DualRes-UNet. In our network, we adopt continuous down-sampling layers similar to U-Net to obtain a large receptive field view so that it can capture high level structure of object. Like concatenation operations, the proposed DualRes modules introduce high resolution features into the up-sampling process, which are beneficial to preserve more details and textures in final images from CNN. Preliminary experiments of 120~150 degrees of limited-angle CT were conducted, which demonstrate that our DualRes-UNet can effectively suppress the limited angle artifact and improve the quantitative accuracy of CT images.
A Detection Method of Harmonic and Interharmonic Using Window Interpolation and Complex Modulation Refinement Algorithm

When there is interharmonics in the vicinity of the fundamental or harmonics, the serious spectral interference will affect the detection accuracy of harmonics and interharmonics. In this paper, a comprehensive analysis method combined window interpolation with multiple modulation zoom spectrum is proposed. Based on the phase difference between the maximum and the second largest lines in the mainlobe of the windowed signal, the method determines whether the peak line has mainlobe interference. If mainlobe interference exists, the multiple modulation zoom spectrum is refined in a certain frequency range with the maximum spectrum as the center frequency, and the harmonic and interharmonics parameters near the frequency band are obtained. If there is no mainlobe interference, the harmonic and inter-harmonic parameters at the frequency band are obtained directly by windowed interpolation. The other harmonics and interharmonic components of the signal are obtained by repeating the above steps. Finally, the simulation results show that the proposed method can effectively dispose the complex signals of harmonics and interharmonics with closing frequencies.
Combinatorial Auction-Based Fog Service Allocation Mechanism for IoT Applications

The resource allocation and pricing mechanism in fog computing is a reputed problem in the last two decades. The fog service provider plays a major significant role in providing resources to the Internet of Things (IoT) devices (e.g., smart/android cell phones) to the users to deploy their applications on various platforms anywhere anytime. Therefore, the fog users have problem-related resource allocation as well as management in receiving the guaranteed computing resources on specified time. In this article, we propose two types of truthful mechanisms for resource allocation and pricing, viz., (a) Fixed Price based Fog Node Allocation Mechanism (FixP - FogNA) and (b) Combinatorial Auction based Fog Service Allocation Mechanism (CAuc - FogSA). After that, we provide the comparison between the two algorithms, and determine the best algorithm between these two. We further investigate that the combinatorial auction based mechanism can essentially enhance the allocation of the resources with high proficiency and creating higher income for the fog providers. Finally, our study is highly useful in various applications such as doctor-patient allocation, drug-disease connectivity, network broadcasting, and many more.
Robust low sidelobe beamforming algorithm based on convariance matrix reconstruction

Steering vector (SV) mismatch and the covariance matrix (CM) mismatch are two main factors which affect the performance of minimum variance distortionless response (MVDR). To enhance the robustness when encountering a mismatch problem, we use reconstructed interference-plus-noise CM and corrected SV to improve MVDR. In addition, inequality constraints are added to obtain low sidelobe level. Simulation results show our proposed method can accurately control the sidelobe level and suppress interference, while achieves good robustness to CM mismatch and SV mismatch.
Optimal Allocation of Virtual Inertia and Damping for Energy Storage

The high share of renewable energy sources and retirement of conventional synchronous generators make that rotational inertia and damping in power system is much less than before. The frequency may experience higher magnititude of oscillations when the contingency happens. Some inverter-interfaced power electronic devices such as energy storage can emulate virtual inertia and damping to respond to the frequency deviation in power system. However, there is still a problem that where and how much virtual inertia and damping is emulated to have the desired transient frequency performance such as desired damping ratio and frequency overshoot for a small disturbance in power system. Authors in this work employ mix-integer differential algorithm to solve this nonlinear optimization problem, and the appropriate energy storage in power system can be selected and the corresponding virtual inertia and damping can be determined.
Primary User Transmit Mode Classification Based Spectrum Sensing in Cognitive Radio Network

Machine learning (ML) aided spectrum sensing is viewed as an alternative promising solution for cognitive radio (CR) systems in recent years. In this paper, a novel spectrum sensing scheme is proposed on the basis of various machine learning algorithms. Driven by the large number of spectrum observations collected from secondary user equipments (SUEs) in the cellular cognitive radio networks (CCRN), the proposed scheme first identify, by means of various ML classification algorithms, the transmit mode (TM) of the primary user (PU) transmitters in the CCRN to gain a coarse knowledge of the PU behaviors over the licensed spectrum. With the TM of the primary user transmitters (PUTs) ascertained, geo-location oriented spectrum opportunities can be subsequently pinpointed for SUEs in the CCRN. Simulations verify that the proposed scheme increases the spectrum access opportunity under the premise of minimizing the interference to the PUs.