Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Control of Autonomous Guided Vehicle Using Reinforcement Learning

Real-time wireless networked control of an Autonomous Guided Vehicle (AGV) from an edge cloud controller is an attractive approach to reduce hardware costs of AGVs, e.g., for industrial applications. We specify a networked control protocol for AGV and investigate how system performance and stability are affected by the reliability of the wireless link with fading. Particularly, there is a trade-off between the AGV speed, the control stability, and the channel quality. Our model takes into account end-to-end latency, which includes control loops and communication. Considering the model complexity, we employ a Reinforcement Learning (RL) approach in order to find the optimal speed of AGV to complete a mission path in shortest time. The proposed solution achieves system stability at par with widely used baseline state-of-the-art controllers, while reducing the AGV mission time by more than 30%.
Cloud Security Implementation using Homomorphic Encryption

With the advancement of computing and communication technologies, data transmission in the internet are getting bigger and faster. However, it is necessary to secure the data to prevent fraud and criminal over the internet. Furthermore, most of the data related to statistics requires to be analyzed securely such as weather data, health data, financial and other services. This paper presents an implementation of cloud security using homomorphic encryption for data analytic in the cloud. We apply the homomorphic encryption that allows the data to be processed without being decrypted. Experimental results show that, for the polynomial degree 26, 28, and 210, the total executions are 2.2 ms, 4.4 ms, 25 ms per data, respectively. The implementation is useful for big data security such as for environment, financial and hospital data analytics.
An Improved Full-Order Sliding-Mode Observer for Rotor Position and Speed Estimation of SPMSM

This paper proposed an improved full-order sliding-mode observer (IFSMO), applied to a sensorless control system of surface-mounted permanent magnet synchronous motor (SPMSM), to obtain a high-precision rotor speed and position. First, the IFSMO method, which combines the new sliding-mode function with the variable boundary layer function, is presented to suppress the chattering and accelerate the convergence speed of the system. Meanwhile, since the observer has the characteristics of a second-order low-pass filter, the high-frequency noise contained in the estimated back EMF signal can be effectively filtered out without an additional low-pass filter. Then, the proposed PLL is designed to obtain rotor position and speed. Finally, simulations and experiments of the SPMSM system based on the IFSMO method under different working conditions are implemented, which demonstrates the effectiveness of the proposed algorithm.
Gain Enhancement of Monopole Antenna Using Partially Reflective Surface for 5G Application

In this work, a Frequency Selective Surface reflector (FSS) has been presented to improve the gain of the antenna for 5G applications. The dimensions of the antenna are 50mm×40 mm×1.6mm and those of the FSS unit cell are 14.2mm×14.2mm×1.6 mm. Both of them are thoroughly presented in this paper. The antenna is integrated with the proposed FSS, they could achieve a high gain. It passed from 2.81 dB to 9.12 dB at 3.5 GHz and the bandwidth reach at 2.28 GHz.
Artificial and Convolutional Neural Network of EEG-Based Motor imagery classification: A Comparative Study

Electroencephalography (EEG) signal recorded during motor imagery (MI) has been frequently used in noninvasive Brain-Computer Interface (BCI) is a new type of device that allows direct communication between user's brain and machine. This paper proposes a novel solution for extraction and classification of left/right hand, both feet, and tongue movement by exploiting two approaches of deep learning such as artificial neural network ANN and convolutional neural network CNN. A wide range of spatial and frequency domain features are extracted from the EEG signals and to train an ANN and CNN networks to perform the classification tasks. The EEG signals of mental tasks are extracted and classified by these architectures. In addition, the proposed methods are validated by the EEG dataset of the BCI competition IV-2a and we compared them with each other. The results show that the CNN model surpasses the ANN model by an accuracy value of 60.55%.
Adaptive finite-time control of master-slave manipulators with time-varying delay

The work presented in this paper focuses on the finite-time control of the master-slave manipulators with time-varying delay. First, a nonsingular fast terminal sliding mode control (NFTSMC) is used for this system based on the knowledge of the upper bound of the uncertainties and disturbances. Despite of the presence of the uncertainties, disturbances, load variation and time-varying delay, the controller is robust and present a finite-time convergence. Second, an adaptive nonsingular fast terminal sliding mode control (ANFTSMC) is applied to master-slave manipulators to avoid the knowledge of the upper bound of the uncertainties and disturbances. This controller presents good performance compared to the first one. The two controllers are evaluated in simulation.
Gaussian Models for CSI Fingerprinting in Practical Indoor Environment Identification

It is not uncommon to experience highly dynamic channels in indoor environments due to time-varying signals as well as moving reflectors and scatterers. This greatly affects the performance of wireless sensing systems that use received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for indoor positioning and event detection. Solutions to this dynamic channel problem often involve laborintensive database maintenance and customized hardware. With this, we present Gaussian models that can withstand temporal and environmental dynamics in practical indoor environments using off-the-shelf devices in this paper. Although systems employing Gaussian models have been previously proposed in the literature, most systems use RSSI instead of CSI to represent the wireless channel. By using a Gaussian distribution to model CSI fingerprints, which offer more abundant information regarding the channel dynamics than RSSI, we can exploit the variance inherent in the wireless channels. Our experiments demonstrate that the Gaussian classifier incurs minimal delay of less than 4 seconds and achieves high classification accuracy compared to other techniques. In particular, it achieves up to 50% and 150% performance improvement over the time-reversal resonating strength (TRRS) and the support vector machines (SVM) methods, respectively.
Deep-Ultraviolet LEDs With All-Inorganic and Hermetic Packaging by 3D Ceramic Substrate

In this letter, an all-inorganic and hermetic packaging technology was proposed for deep-ultraviolet light-emitting diodes (DUV-LEDs). The package was fabricated by bonding the quartz cap on the three-dimensional (3D) ceramic substrate at low temperature. The bonding layer was formed by an inorganic adhesive and presented a reliable and dense state. The average air leakage rate of the packaging structure was <inline-formula> <tex-math notation="LaTeX">$3.79\times 10^{-8}$ </tex-math></inline-formula> Pa <inline-formula> <tex-math notation="LaTeX">$\cdot$ </tex-math></inline-formula> m<sup>3</sup>/s and the average tensile strength between quartz cap and ceramic substrate was 4.3 MPa. The peak emission intensity was at 280 nm and the peak width at half height was about 10 nm. At the current of 40 mA, the voltage was 5.63 V and the light output power was 3.0 mW. After the accelerated aging test, the light output power of packaged DUV-LEDs only decreased by 14.9%, while that of unpackaged reference decreased by 55.8%.
Multi-faults classification in WSN: A deep learning approach

Wireless Sensor Networks are deployed in harsh environments. Their key advantage is there flexibility and low cost. But they can face many failures which created the need to improve data accuracy. Many artificial intelligence techniques has demonstrated impressive results in fault detection and faults diagnosis. Lately, machine learning emerged as a powerfull artificial intelligence based technique to solve the problem of failures in WSN. In this paper, a multi-fault classification is evaluated using deep learning technique based on LSTM classifier and then compared with different machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP)and Probabilistic Neural Network (PNN). The performance of this mentioned techniques used for fault detection in WSNs were compared based on four metrics: Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC)and False Alarm (FA).
Downlink NOMA for Coexistence of Aerial and Terrestrial Users: Stochastic Geometry Analysis

Connecting aerial users (AU)s to cellular networks expands the potential of many applications. Nevertheless, existing cellular networks are not designed to efficiently serve AUs and terrestrial users (TU)s. Limited spectrum and high interference make the task even more challenging. Thus, in this paper, we examine the feasibility of fixed-power non-orthogonal multiple access (NOMA) scheme in meeting the demands of AUs and TUs by leveraging stochastic geometry. To this end, we propose a tractable framework for evaluating the coverage of AU and TU in cellular networks, where BSs are distributed using Poisson Point Process (PPP). We then derive the coverage probability of AU and TU. Using these analytical expressions, we further analyze the impact of different network parameters such as AU’s altitude, TU’s distance, and power allocation, and obtain key insights for designing an efficient NOMA scheme. Our results show that i) increasing AU’s altitude does not always degrade the signal-to-interference ratio (SIR), ii) fixed-power NOMA scheme cannot be employed solely to serve AU’s control and command (C& C) link, iii) pairing a near TU with a typical AU may prevent significant performance degradation, and iv) mitigating the interference of TU could improve the performance of AU.
Energy-Efficient Hybrid Symbol-Level Precoding via Phase Shifter Selection in mmWave MU-MIMO Systems

We address the symbol-level precoding design problem for the downlink of a multiuser millimeter wave (mmWave) multiple-input multiple-output wireless system. We consider a hybrid analog-digital architecture with phase shifter selection where a small-sized baseband precoder is followed by two successive networks of analog on-off switches and variable phase shifters according to a fully-connected structure. We jointly optimize the digital baseband precoder and the states of the switching network on a symbol-level basis, i.e., by exploiting both the channel state information (CSI) and the instantaneous data symbols, while the phase-shifting network is designed only based on the CSI. Our approach to this joint optimization is to minimize the Euclidean distance between the optimal fully-digital and the hybrid symbollevel precoders. It is shown via numerical results that using the proposed approach, up to 50 percent of the phase shifters can be switched off on average, allowing for reductions in the power consumption of the phase-shifting network. Adopting appropriate power consumption models for the analog precoder, our energy efficiency analysis further shows that this power reduction can substantially improve the energy efficiency of the hybrid precoding compared to the fully-digital and the state-of-the-art schemes.
Direct Experimental Performance Comparison of Two Microprocessors for the Efficiency Evaluation of Single Event Effects Mitigation Techniques

We present the direct experimental vulnerability comparison of two microprocessor designs with different Single Event Effects (SEE) mitigation techniques at different heavy ion fluxes. The trade-off between the performance, fault-tolerance and power consumption is considered. We introduce the Mean Fluence Between Failures (MFBF) metric for the vulnerability comparison.
Information Disclosure Game on Sharing Platforms

Sharing platforms have facilitated the redistribution of underused resources by providing convenient online marketplaces for individual sellers and buyers. However, the sellers on these platforms may not fully disclose the information of their shared commodities, due to strategic behaviors or privacy concerns. Sellers’ strategic information disclosure significantly affects buyers’ user experiences and platforms’ reputation. This paper presents one of the first analytical studies on information disclosure and pricing strategies of competing sellers on a sharing platform. In particular, we propose a three-stage game framework to capture sellers’ strategic behaviors and buyers’ decisions. Although the corresponding optimization problem is non-convex, we are able to completely characterize the complex market equilibria. We demonstrate that full disclosure by all sellers or non-disclosure by all sellers will both lead to intense price competition. We prove that the former all-disclosure case is never an equilibrium even when all sellers have good commodity qualities and low privacy costs, while the latter non-disclosure case can be an equilibrium under which all sellers get zero profit. Interestingly, we also reveal that buyers’ estimation biases encourage information disclosure as they mitigate the competition among sellers.
On the Parallel Reconfiguration of Virtual Networks in Hybrid Optical/Electrical Datacenter Networks

Recently, hybrid optical/electrical datacenter networks (HOE-DCNs) have been considered as a promising DCN architecture, because they merge the merits of electrical packet switching (EPS) and optical circuit switching (OCS). This paper considers the reconfiguration of virtual networks (VNTs) in an HOE-DCN to address the dynamic nature of emerging network services. Specifically, we study the problem that given the original and new virtual network embedding (VNE) schemes of several VNTs, how to schedule parallel virtual machine (VM) migrations in batches to realize the VNT reconfiguration within the shortest time. We design two algorithms to reconFigure the inter-rack network in an HOE-DCN in steps, schedule VMs to migrate accordingly, and allocate bandwidth to the VM migrations. The first algorithm uses the one-shot approach, where all the VM migrations are conducted in parallel within the shortest possible time. We formulate a linear programming (LP) to solve the bandwidth allocations in it exactly. Next, to relieve the bandwidth competition introduced by the one-shot approach, we propose the second algorithm by leveraging the multi-shot approach, i.e., invoking multiple batches of parallel VM migrations such that the reconfiguration time can be further reduced. Extensive simulations verify the effectiveness of our proposals.
Energy-efficient Velocity Control for Massive Numbers of Rotary-Wing UAVs: A Mean Field Game Approach

When a disaster happens in a metropolitan area, wireless communication systems in the area are highly affected, degrading the efficiency of the search and rescue (SAR) mission. An emergency wireless network must be deployed quickly and efficiently to preserve human lives. Teams of low-altitude rotarywing unmanned aerial vehicles (UAVs) are useful as on-demand temporal wireless networks because they are generally faster to deploy, flexible to reconfigure, and able to provide good communication services with short line-of-sight links. However, rotary-wing UAVs’ limited on-board batteries require that they need to recharge and reconFigure frequently during a mission. Therefore, we formulate the velocity control problem for massive numbers of rotary-wing UAVs as a Schrödinger bridge problem which can describe the frequent reconfiguration of UAVs. Then we transform it into a mean field game and solve it with the Gprox primal dual hybrid gradient (PDHG) method. Finally, we show the efficiency of our algorithm and analyze the influence of wind dynamics with numerical results.
Binary Indicated Numbers with Bit-Level Integrated Scalability Support (BINBLISS)

The Tactile Internet is a prominent example for the need to re-evaluate the trade-off between compression and real-time needs for smaller messages from/to sensors and actuators. Considering the ultra-low latency requirements in the millisecond range, we developed a use-case agnostic low-level approach to compress the amount of data that need to be sent. Our approach combines threshold-based delta-coding for individual values (BIN) and bit-plane level flexible compression through precision reduction (BLISS). We demonstrate how realistic data savings can be obtained with low compression losses and overheads using common Tactile Internet data sets through application of our approach. We find that our agnostic and case-independent approach yields data savings over the 50% range for low compression losses for Tactile Internet scenarios.
VSFBS: Vulnerability Search in Firmware Based on String

Searching homologous vulnerabilities in firmware from different platforms is key for protecting the Internet of Things (IoT) devices. Due to the hard work to acquire the source code, the existing works mainly focused on binary code, and many methods have been used to find vulnerabilities in binary code, like 1) numerical features are used to achieve efficient prefiltering, however, a large number of experiments showed that this method is which is lack of adaptability in some platforms; 2) structural features are used to achieve the accurate matching, but it will bring low efficiency. Based on the shortcomings of the existing methods, we proposed a lightweight method to search vulnerabilities of firmware in a cross-platform model. The main idea of this method is to exploit readable string literals inside functions combined with local call graph and use these string literals as a feature of each function in form of text, then MinHash LSH and MinHash LSH Forest algorithms are used to achieve similarity calculation of this feature according to different online search scenarios. Finally, we employed an optimization stage based on numeric features to improve the rank of similar function, which allows us to search for a similar function more accurately. The experimental results on the open library OpenSSL showed that our method has more robust performance in terms of filtering accuracy, and it is about 1 order of magnitude faster than the two existing methods in terms of efficiency.
Refinement of proteins at subatomic resolution with MOPRO

Crystallography at subatomic resolution permits the observation and measurement of the non-spherical character of the atomic electron density. Charge density studies are being performed on molecules of increasing size. The MOPRO least-squares refinement software has thus been developed, by extensive modifications of the program MOLLY, for protein and supramolecular chemistry applications. The computation times are long because of the large number of reflections and the complexity of the multipolar model of the atomic electron density; the structure factor and derivative calculations have thus been parallelized. Stereochemical and dynamical restraints as well as the conjugate gradient algorithm have been implemented. A large number of the normal matrix off-diagonal terms turn out to be very small and the block diagonal approximation is thus particularly efficient in the case of large structures at very high resolution.
Report and Abstracts of the 17th Meeting of IIM, the Interuniversity Institute of Myology:Virtual meeting, October 16-18, 2020

In 2020, due to the COVID-19 pandemic, the annual meeting of the Interuniversity Institute of Myology (IIM), took place on a virtual platform. Attendees were scientists and clinicians, as well as pharmaceutical companies and patient organization representatives from Italy, several European countries, Canada and USA. Four internationally renowned Keynote speakers presented recent advances on muscle stem cells regulation, skeletal muscle regeneration, quantitative biology approaches, and metabolic regulation of muscle homeostasis. Novel, unpublished data by young trainees were presented as oral communications or posters, in five scientific sessions and two poster sessions. On October 15, 2020, selected young trainees participated to the High Training Course on “Advanced Myology”, organized together with the University of Perugia, Italy. The course, on a virtual platform, showcased lectures on muscle development and regulation of muscle gene expression by international speakers, and roundtables discussions on “Single cell analysis of skeletal muscle” and “Skeletal muscle stem cell in healthy muscle and disease”. The Young IIM Committee, composed by young trainee winners of awards in the past IIM Meeting editions, was directly involved in the selection of keynote speakers, the organization of scientific sessions and roundtables discussions tailored to the interests of their peers. A broad audience of Italian, European and North American participants contributed to the different initiatives. The meeting was characterized by a friendly and inclusive atmosphere, facilitating lively and stimulating discussions on emerging areas of muscle research. The meeting stimulated scientific cross-fertilization fostering novel ideas and scientific collaborations aimed at better understanding muscle normal physiology and the mechanisms underlaying muscle diseases, with the ultimate goal of developing better therapeutic strategies. The meeting was a success, and the number of meeting attendees was the highest of all IIM Meeting editions. Despite the current difficulties imposed by the COVID-19 pandemic, we are confident that the IIM community will continue to grow and deliver significant contributions to the understanding of muscle development and function, the pathogenesis of muscular diseases and the development of novel therapeutic approaches. Here, abstracts of the meeting illustrate the new results on basic, translational, and clinical research, confirming that our field is strong and healthy.
X-ray diffraction study of a pseudo single crystal prepared from a crystal belonging to point group 2

A pseudo single crystal (a composite of matrix resin and powder crystallites, wherein the individual crystallites are aligned three-dimensionally) was prepared from a powder of sucrose, and its X-ray diffraction pattern was studied. A powder of sucrose crystals suspended in a UV-curable resin precursor was subjected to magnetic alignment in order to align the individual crystallites three-dimensionally, and then the resin precursor was photopolymerized to maintain this orientation. The pseudo single crystal thus obtained produced a well resolved X-ray diffraction profile that is similar to that produced by a twin crystal. The origin of the twin orientation was explained in terms of the interplay between the symmetry of magnetic susceptibility axes and the crystal symmetry (point group 2) of sucrose. The crystal structure of sucrose solved using the pseudo single crystal was in excellent agreement with that solved using a single crystal.