Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspectives of the Using mm-Range Radiometry in Telecommunication Systems of Information Transmission

The perspectives of the using mm-range radiometric receivers in the systems of wireless communication are explored. To measure the level of radiation and conduct radio monitoring in a telecommunication channel, it is proposed to use the method of microwave radiometry. The functional diagram of the modulation radiometer with the transformation of the input signal and the parameters of the developed and certified radiometric systems are given.
CloudDLP: Transparent and Automatic Data Sanitization for Browser-Based Cloud Storage

Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities.
Employee Attitudes and (Digital) Collaboration Data: A Preliminary Analysis in the HRM Field

The digital transformation of organizations is making workplace collaboration more and more powerful and work always "observable"; however, the informational and managerial potential of the generated data is still largely unutilized in Human Resource Management (HRM). Our research, conducted in collaboration with business engineers and economists, aims at exploring the relationship between digital work behaviors and employee attitudes. This paper is a work-in-progress contribution that presents a preliminary phase of data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data. In the exploratory data analysis step, we analyze data in their original table format and elaborate it according to the user who performed the action and the performed action. Then, we move to a graph representation in order to make explicit the interaction between users and the objects of their actions. Finally, we introduce the concept of employee-attitude-oriented pattern as a mean to derive significant views over the overall graph and discuss Social Network Analysis (SNA) approaches that can be exploited for our purposes.
Mathematical Expressions and Analysis for Sine-Triangle Clamped PWM Harmonics

Clamped techniques for pulse width modulation (PWM) produce higher AC voltage for a given DC bus and lower losses due to reduced switching. This yields better power quality and higher power density. Harmonic analysis tools are needed to assess power quality of any PWM technique. As fundamental frequencies of power converters such as variable-speed drives are usually changing while source power frequency remains fixed, interharmonic analysis is critical. New industry standards are beginning to impose requirements governing interharmonics. Previously, lumped harmonic analysis of sine-triangle clamped PWM was used, via analysis of time-based simulation, because no final analytical mathematical expressions had been developed to address non-integer carrier frequency ratios to the fundamental frequency. This paper introduces a process for interharmonic evaluation, and derives new analytical expressions for the harmonics of sine-triangle clamped PWM. The new analytical results are validated against the extant simulation-based results.
An improved Adaptive BeamForming Algorithm for 5G Interference-coexistence communication

Coexistence of multiple wireless systems in a 5G network can cause interference in the same frequency band and deteriorate the performance of the received signal. In this paper, a novel algorithm is proposed in antenna array processing to handle interference-coexistence communication. We adopt a linear filter which is called Linearly Constrained Minimum Variance (LCMV) filter. On the basis of traditional singly linearly constrained least mean square (LC-LMS), we introduce a log-sum penalty on the coefficients and add it into the cost function. We derive the iterative formula of filter weights. By simulations in antenna environment with signal of interest, noise and interferences, we prove that the convergence rate of the new method is faster than traditional one. Moreover, the mean-square-error(MSE) of the proposed method is also verified. Experiment results demonstrate that our method has lower MSE than the traditional LC-LMS algorithm. The proposed adaptive beamforming scheme can be applied in 5G system to deal with the coexistence of signals and interferences.
Robust PI control of a grid-connected voltage source inverter for virtual inertia response in weak grid conditions

Chemical battery energy storage systems (BESS) providing frequency compensation techniques such as virtual inertia response (VIR) can be a solution to tackle the frequency volatility ac-island grids suffer when inverter-based generation such as PV is integrated. However, the inverter connecting the BESS may suffer performance and stability issues when due to a low short circuit ratio (SCR) the inverter phase-locked loop (PLL) and the grid impedance negatively interact. To address this issue this work proposes a robust proportional-integral controller (R-PI) for the inverter inner-loop current control. The R-PI is synthesized by the structured H-infinity method using MATLAB and the performance is compared with a regular proportional-integral controller (PI) designed by the conventional frequency shaping method. A sensitivity analysis that includes the dynamic impact of the PLL is conducted for varying SCR. The analysis is verified by time domain simulations in MATLAB and shows that both the R-PI and the PI maintained the inverter stability under the worst-case SCR of 2. However, the benchmarked transient performance of the regular PI was compromised under the simulated SCR variation of 7.2 to 2, while the R-PI was capable of maintaining this performance throughout the SCR variation.
The Eins3D project — Instantaneous UAV-Based 3D Mapping for Search and Rescue Applications

The overview of a situation in a search and rescue disaster is the key aspect of an effective assistance. In the recent years the utilization of multicopter with various photogrammetry systems is an upcoming trend and an open field of research. This paper discusses the technical aspects of an automated integral system that will support rescuers during the strategic mission planning and will give situational awareness by instantaneous 3D mapping. The approach combines sensors including a 3D Laserscanner, a thermal camera and an attitude system as a payload unit on an Multicopter. The continuous data fusion and the down link are providing an instant 3D environment map that is continuously revised and updated.
Multi-Antenna Jamming in Covert Communication

Covert communication conceals transmission of messages from Alice to Bob out of a watchful adversary, Willie, which tries to determine if a transmission took place or not. While covert communication in a basic, vanilla settings where all variables are known to Willie results in the well known square-root law, when a jammer is present and assists Alice by creating uncertainty in Willie’s decoder, this transmission may have a positive rate.In this work, we analyze the case where the jammer is equipped with multiple antennas and obtain the optimal transmission strategy of the jammer in order to maximize his assistance to Alice, in terms of maximizing a ratio between Willie’s and Bob’s noise variance. We show that the optimal strategy of the jammer is to perform beamforming towards a single direction with all his available power. This direction though, is not trivial, since it reflects an optimal tradeoff point between minimizing the interference at Bob and maximizing the interference at Willie.
Applications of deep learning to MRI images: A survey

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance Imaging (MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are presented. Finally, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed.
Entropic Central Limit Theorem for Rényi Entropy

We establish a central limit theorem for Rényi entropies when the Rényi parameters belong to (0, 1) for a large class of random vectors. This complements a celebrated result of Barron (1986). As an application, we show that a general Rényi entropy power inequality fails when the Rényi parameter is in (0, 1).
Heuristics for Placing the Spawn Points in Multiplayer First Person Shooters

Level design in first person shooters is a critical and complex process with many facets. Among them, the placement of the spawn points, i.e., the positions of the level where players starts the game after being killed, has a huge impact on the game experience. In this work, we propose a novel approach to this problem that combines a set of design rules with a graph-based analysis of the level. As a results, we design a set of heuristics that, based on the topological features of the graphs extracted from the levels, can be used for the placement of spawn point. Finally, we test our approach with a small user study on three different levels, comparing our heuristic placement with an uniform strategy. Although preliminary, our results are promising and suggest that the level design principles are effectively captured by our heuristics.
Suitability of Optical Wireless Communication receivers for Virtual Reality Applications

Virtual Reality has experienced massive commercial adoption due to the cost reduction of head mounted displays and the improvement of immersive user experience. Nevertheless, there are lots of initiatives and experiments in course for improving even more the quality of experience. One of the main faced obstacle by these systems is the user’s degrees of freedom. Static player positions (seated or standing without walking) and cable connections are being replaced by fully interactive wireless systems. Future scenarios will comprise a common space in which several players can freely move, interacting in the virtual domain. In this situation, high bandwidth wireless communications will play an important role. This paper evaluates feasibility of using Optical Wireless Communications for VR systems, attending at the impact of the receiver’s orientation on the link performance.
SVM-RBM based Predictive Maintenance Scheme for IoT-enabled Smart Factory

Fault diagnosis of facility maintenance is very important. Unexpected equipment failures during the process lead to significant losses to the plant. In this paper, in order to detect defects and fault patterns, Support Vector Machine (SVM) which is one of the machine learning algorithms, classifies the data received from the equipment as normal or abnormal. After learning only normal data by using Restricted Boltzmann Machine (RBM). We propose a model to identify the data, and then we analyze the faults of facilities in real-time.
Optimal microgrid operation considering battery degradation using stochastic dual dynamic programming

Intermittent energy sources demand temporal storages to balance generation and load, and batteries stand out as an alternative. However, the lifetime is limited, and cycling depth affects the battery degradation rate. Current stochastic multi-stage methods lack proper representation of battery degradation. This paper proposes a stochastic multi-stage model for optimizing battery operation in a microgrid considering battery degradation with a piece-wise linear cost function with uncertain wind power production and load. The model is solved using Stochastic Dual Dynamic Programming (SDDP) and is demonstrated on a 4-bus test case with limited import and export capacity to illustrate the battery degradation cost's impacts on the battery cycling strategy. The results show that the importance of a stochastic method is more pronounced when battery degradation is modelled.
IoT-HC: A Novel IoT Architecture for the Hybrid Cloud

In the last few years, the use of Cloud and Internet of Things (IoT) technologies is dramatically growing, opening new application possibilities spanning from smart cities to the prevention of natural disasters. A flexible integration of the Cloud and IoT environments allows users and applications to combine the benefits of the two worlds. Specifically, robustness, versatility and high computation power are better supported by the Cloud, while better real-time adaptation and local control are peculiar characteristics of the IoT infrastructure. Even higher flexibility can emerge when the cloud layer combines and integrates a public cloud component, offered by an external provider, and a private cloud, managed internally to the organization, thus giving rise to the so-called hybrid cloud. The integration of a hybrid cloud with an IoT layer opens a wide range of new possibilities and applications but also presents heterogeneity and complexity issues that must be carefully addressed. This paper offers a contribution in this field by presenting a novel threelayered IoT architecture for the Hybrid Cloud, namely IoT-HC, which is devoted to enabling distributed sensing and actuation, controlling IoT networks, elaborating the collected data both at edge and cloud level, and deciding which data has to be sent to private or public cloud components for storage and high-level elaboration.
Autocorrelation Function of Full-Response CPM Signals and Its Application to Synchronization

Two types of the autocorrelation functions of the full-response continuous phase modulation (CPM) signal are investigated in the paper, the autocorrelation functions are proven to be periodic functions when the modulation index of the CPM signal is $h=1$ . Then the autocorrelation functions are expanded in the form of Fourier series, and two synchronization schemes are proposed to estimate the synchronization parameters from the coefficients of the Fourier series, including the carrier frequency offset, the carrier phase and the timing offset. The performances of the synchronization algorithms are shown by simulations and compared with the modified Cramer-Rao bounds (MCRB) and other synchronization schemes. The simulation results show that the first type of the proposed scheme has better synchronization performances than the available schemes.
Quantitative Data Analysis: Small Unmanned Aerial Systems at Hurricane Michael

This paper summarizes the 80 flights by small unmanned aerial systems (sUAS) at Hurricane Michael from October 11th to October 14th (2018) flying on behalf of the Florida State Emergency Response Team. The 80 flights fulfilled 26 missions for Debris/Damage/Flood Estimation, Strategic SA/Recon/Survey, and Ground Search covering response and recovery operations. The flights at Michael do not differ from other recent hurricanes, most notably Harvey. Besides the inclusion of night flights searching for victims, which appear to be a new task, Michael was very similar in terms of flight duration, when first person view (FPV) and preprogrammed (PP) control styles were used, and choice of platforms. In addition, the Hurricane Michael data set is available at < hrail.crasar. org > for further analysis.
CoFilter: High-Performance Switch-Accelerated Stateful Packet Filter for Bare-Metal Servers

As one of the most critical cloud services, Bare-Metal Servers (BMS) introduce stringent performance requirements on data center networks (DCN). Stateful packet filter is an integral DCN component of ensuring connection security for BMS. However, the off-the-shelf stateful packet filters either are costly for cloud DCNs or introduce significant performance bottlenecks. In this article, we present <italic>CoFilter</italic>, which leverages low-cost programmable switches to accelerate the stateful packet filter for BMS. <italic>CoFilter</italic> uses (1) <italic>stateful process partition</italic> to enable complex stateful packet filtering logic on programmability-limited switching ASICs, (2) <italic>state compression</italic> to track tens of millions of connections with constrained hardware memory, and (3) <italic>per-tenant packet rate limit and tenant-aware flow migration</italic> to achieve efficient performance isolation among different tenants. Overall, <italic>CoFilter</italic> implements a high-performance stateful packet filter via the co-design of programmable switching ASIC and CPU. We evaluate <italic>CoFilter</italic> under various data center traffic traces with real-world flow distributions. The evaluation results show that <italic>CoFilter</italic> remarkably outperforms NetFilter, i.e., forwarding packets at line rate (13x throughput of NetFilter), keeping packet delay within 1us, and freeing a significant quantity of CPU cores, with rather small memory usage, i.e., accommodating over <inline-formula><tex-math notation="LaTeX">$10^7$</tex-math><alternatives><mml:math><mml:msup><mml:mn>10</mml:mn><mml:mn>7</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="cao-ieq1-3136575.gif"/></alternatives></inline-formula> connections with only 16MB SRAM.
Optimal battery systems designs for Distribution Grids: What size and location to invest in?

This paper concerns the development of a cost-effective approach to deploy the flexibility that electrical battery storage can offer to support the secure operation of medium voltage electrical networks under varying operating conditions. To this aim, and unlike related studies of the current state-of-the-art, the approach presented in this paper is based on a multiperiod optimization model that simulates half-hourly operational decisions. The optimization model includes a non-linear power flow equation based representation of the system, branch capacity constraints (instead of energy flows - the traditional state-of-the-art practice), different feasible battery sizes (currently available in the market), high RES penetration levels, the time of use electricity prices (half-hour dynamic prices), load data of customers (defined from real-world profiles), and reference battery costs (from existing literature). The objective is to decide the location and size of the battery storage within the medium voltage electrical network by minimizing the difference between operational and capital expenditures. The location of the battery storage is modeled with binary variables, whereas the size is modeled as a parameter first and then as a continuous variable. The value of the optimization model is demonstrated in a case study built upon a modified version of the IEEE 33 bus test system.
Predictive Current Control of Six-Phase Permanent Magnet Synchronous Machines Based on Virtual Vectors with Optimal Amplitude and Phase

This paper presents an innovative predictive current control (PCC) strategy based on virtual voltage vectors with optimal amplitude and phase for six-phase asymmetrical permanent magnet synchronous machines (PMSMs) with isolated neutrals. This strategy combines two virtual vectors and two zero vectors over a sampling period in order to reduce the torque ripple and enhance the tracking of references in the fundamental subspace, which are responsible for the production of flux and torque. Several simulation results are provided in order to compare the performance of the six-phase PMSM drive under the proposed control strategy with other finite control set model predictive control strategies available in the literature.
Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data

Heightmap generation is currently a tedious topic with the majority of generation using Perlin noise which forms a reliable, but sometimes repetitive output. In this paper, a method of generating height maps from real-world digital elevation data taken from specific regions of the planet is proposed. Raw elevation data sourced from NASA’s SRTM (30m) data set is transformed into a height map format, this data is then passed into a two type unsupervised model. The method uses a type of generative adversarial network to learn the spatially-invariant features within the input regions. Producing a network model that can output an extensive amount of varying, but visually and structurally similar height maps to that of the input regions. The visual validity of outputs from the network was tested using data from 262 human participants, with over 90.15% of generated samples being correctly assigned to the original input data with a significance of P <0.001.
Object Detection Method Based on Aerial Image Instance Segmentation by Unmanned Aerial System in the Framework of Decision Making System

The article analyses the capabilities of unmanned aerial system application in the framework of decision making in the crisis situations that require the object detection on aerial images acquired by the unmanned aerial system. To increase the operational capability and credibility of the automotive vehicles detection at the aerial images acquired by the unmanned aerial systems for more efficient use of acquired information in the framework of decision making support model Mask R-CNN was selected. This model is more appropriate for solving the problem of multiclass classification and object detection of small-size objects on the image. To improve this model, the article recommends using small-size anchors taking into account height-to-width aspect ratio according to greater amount of classes that along with test time augmentation usage enables to augment the mAP.