Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Nash Equilibrium Game for Radio and Computing Resource Allocation in Co-located MEC

The tower sharing approach has been widely used by Mobile Network Operators (MNOs) to save their Capital Expenditure (CAPEX) by sharing the physical infrastructure hosted by a third party tower provider. In addition, multiple Computing Resource Providers (CRP) are deploying their servers at towers by cooperating with tower providers to grant low latency, real time services to users. Thus, the resource allocation has become a challenging issue where users of different MNOs need to share the computing resources provided by CRPs. In this paper, the joint allocation of uplink, downlink and computing resources is considered to minimize the end to end latency of users where the offloading process is modeled as a network of queues. Since the resource allocation of MNOs and the CRP are coupled with each other, we formulate it as a Generalized Nash Equilibrium Problem (GNEP). We propose a penalty based algorithm to solve the formulated GNEP with an effective initialization approach to improve the performance of the algorithm. Then, we perform the simulation to analyze the performance of the algorithm.
Single-image reflection removal : from computational imaging to deep learning

Reflection removal aims at enhancing the visibility of the background scene while removing the reflections for images taken through the transparent glass. Though it is of broad application to various computer vision tasks, it is very challenging due to its illposed nature and additional priors are needed to make this problem tractable. Traditional reflection removal methods solve this problem by making use of different heuristic observations or assumptions. These assumptions are seldom satisfied in practical scenarios. In this thesis, we generalize the assumptions for the reflection removal problems by using different information or imposing new constraints. We first propose a method by exploring the blur inconsistency between the background and reflections. Then, we introduce the first benchmark dataset in this area and analyze limitations of existing methods based on this dataset. In the third work, we address this problem by using the sparsity prior and non-local image prior from the external source. Then, with the observation that most reflections only cover a part of the whole image, we propose a method to automatically detect the regions with and without reflections and process them in a heterogeneous manner. At last, we introduce a data-driven method by using the concurrent deep learning framework. Our methods have been evaluated by using the benchmark dataset proposed in our second work. These evaluations cover a diversity of common scenarios in our daily life; hence the experiments prove that our approaches are valid for a broad class of practical scenarios. The main contributions of this thesis are three folds: We thoroughly study the reflection properties observed in our daily scenarios; we propose the first benchmark evaluation dataset in this area and use it to analyze the limitations of existing methods; we propose various approaches to solve this problem from different angles. The efforts and achievements in this thesis promote the practical capabilities of reflection removal techniques and provide fundamental support for future researches. iii
Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

The continuous improvements in the area of medical imaging, makes the patient monitoring a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect data from human body through sensors, wireless connectivity etc. The junction of medicine and IT like medical informatics will transform healthcare, curbing cost, make more efficient, and saving lives. Various computerized techniques are implemented in the area of Artificial Intelligence (AI) for the application of medical imaging to diagnose the infected regions in the images and videos such as WCE and pathology. The famous stomach infections are ulcer, polyp, and bleeding. Stomach cancer is the most common infection and a leading cause of human deaths worldwide. In the USA, since 2019, a total of 27,510 new cases are reported including 17,230 men and 10,230 women. While the number of deaths is 11,140 consists of 6,800 men and 4,340 women. The manual diagnosis of these stomach infections is a difficult and agitated process therefore it is required to design a fully automated system using AI. In this article, we presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection. In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection. Later, pre-trained deep learning model named VGG16 is employing and re-trained using transfer learning. Features of re-trained model are extracted from two consecutive fully connected layers and fused by array-based approach. Besides, the best individuals are selected through the metaheuristic approach name PSO along mean value-based fitness function. The selected individuals are finally recognized through Cubic SVM. The experiments are conducted on Private collected dataset and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.
On Robust Model-Free Reduced-Dimensional Reinforcement Learning Control for Singularly Perturbed Systems

We present a robust design for reinforcement learning (RL) based optimal control of continuous-time linear time-invariant singularly perturbed (SP) dynamic systems in the presence of dynamic uncertainties. We consider the dynamic model of both the plant and the uncertainty to be unknown. Assuming that the uncertainty satisfies an input-to-state stability (ISS) condition, we propose a variant of the adaptive dynamic programming (ADP) method that learns a sub-optimal controller using measurements of only the slow states of the plant. The resulting RL controller is, therefore, significantly reduced-dimensional, and enjoys reduced learning time. We illustrate our design with simulations of a SP system and of a clustered multi-agent consensus network.
Compact UHF/VHF Monopole Antennas for CubeSats Applications

In this paper, two compact monopole antennas working at 149 MHz (VHF) and 398 MHz (UHF) are proposed for CubeSats applications. Two monopole antennas are integrated on a double layer ceramic substrate. The overall dimensions of the folded antenna are 101 mm <inline-formula> <tex-math notation="LaTeX">$\times 40$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times 8.9$ </tex-math></inline-formula> mm which is very small. Meander-line technique and foldable structure are proposed to achieve a compact design. The introduction of decoupling structure solves the problem of high coupling caused by small size. Computer simulations and measurements are conducted. The results demonstrate that the proposed design is a very promising candidate for CubeSats applications.
Studies of Oogenesis in Natural Populations of Drosophilidae. I. Relation of Ovarian Development and Ecological Habitats of the Hawaiian Species

The fecundity potential of Drosophila populations endemic to Hawaii has been investigated by analyzing the ovarian development of species in their natural habitats. Species differ in mean number of ovarioles per fly and/or number of mature eggs per ovariole. This diversity is probably an adaptive response of the population to the larval food supply which is determined by the nature, abundance, and nutritional content for larval growth of the oviposition substrates. Three groups of species were recognized on the basis of their fecundity potential and their use of comparable ovipositing substrates: species with a low potential, breeding on flowers (pollen); species with a medium potential, breeding on decaying leaves (bacteria); and species with a large potential, breeding on decaying stems (yeast). Adaptive mechanisms in oviposition behavior (single egg or cluster oviposition) and in the development and function of ovarioles (alternating ovariole development or synchronous ovariole development) which coordinate the reproductive potential of the species with the nutritional content of their breeding substrates are discussed, as is the importance of environmental parameters in determining realized fecundity.
Î±-L-Fucose in Histology: A Part of the Cancer Glycome Hiding in Plain Sight

Copyright: © 2012 Listinsky JJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Alpha-L-fucose (“fucose”) is a 6-carbon deoxy-sugar that is found to be contained in major blood group antigens, in Lewis minor blood group antigens and on many epithelial cells. Fucose is incorporated into numerous tumor-associated carbohydrate antigens, and appears to have important functional significance, as well as biomarker significance, in common human cancers [1,2]. Data suggest that diagnosis and prognosis would be aided by detection of fucosylated molecules in tissue samples. Unfortunately, the names of fucosecontaining cancer antigens rarely provide clues that the sugar is, in fact, present. As a result, fucose remains nearly anonymous in the histopathology literature.
A Dynamical Game Approach for Integrated Stabilization and Path Tracking for Autonomous Vehicles

A new game theory based framework is proposed for path tracking and stabilization of autonomous vehicles. In the developed framework, vehicle body and corner traction control strategies are formulated in terms of players in a differential game. An integrated stability and path tracking control based on a non-cooperative differential game is developed. It includes bidirectional slip effect and wheel dynamics, which reflect more accurate longitudinal and lateral dynamics in harsh maneuvers and scenarios with sudden changes in the path planners trajectories. The open-loop and closed-loop Nash equilibrium control strategies are obtained by solving a two-player linear-quadratic differential game for the dynamical system of the overall tracking error. The performance of the proposed control strategy is validated with software simulations in various driving conditions.
Anti-SEU design of SRAM based on FinFET process

DICE is an effective cell architecture to mitigate single effect upset (SEU), however, it still has many problems due to the weakness during read and write. At the same time, owing to the continuous reduction in channel length of traditional planar CMOS transistors, it brings series of challenges, such as the short channel effect, reduced carrier mobility, etc. Therefore, it is necessary to propose a new type anti-irradiation SRAM cell based on advanced process. Regarding the issues above, the article based on FinFET process proposes a single-end read structure to solve the stability problem of traditional DICE cell, and uses PMOS instead of NMOS as the pass transistors to improve the write margin (WM). The results of Hspice simulation show that the innovative DICEPM cell structure has strong anti-SEU capability, high read static-noise margin (RSNM) and write margin (WM).
Stability Analysis of a Large-scale Single-Lane Connected Vehicle Model with Multiple Sensing, Communication, and Human Reaction Delays

On a connected vehicle model with sensing, communication and human reaction delays, here we present an approach to investigate the stability of the equilibrium flow dynamics on the plane of model parameters. The approach is next utilized to reveal how the number of vehicles as well as penetration rates of sensors and communication lines in the arising vehicle network could influence the stability regions with respect to model parameters.
Coverage Analysis of Distributed Beamforming With Random Phase Offsets Using Ginibre Point Process

In this paper, we study a wireless network where multiple distributed transmitters control the phases of their signals so that they can be constructively combined at a client receiver. Unlike centralized beamforming with co-located and phase-synchronized antennas, geographically separated transmitters experience phase offsets caused by the individual local oscillators. In practical scenarios, the transmitters should not be placed too close to each other in order to alleviate mutual coupling effects and extend the coverage region. In this regard, we model the spatial distribution of the transmitters as a $\beta $ -Ginibre point process that models the repulsive feature. We investigate two types of transmission strategies: (i) Transmitter selection in which the client selects the transmitter providing the highest received power at the client, and (ii) Coherent beamforming in which multiple transmitters send their signals simultaneously to the client in the presence of phase offsets among the transmitters. We introduce the exact expression of the coverage probability of the transmitter selection method. Also, we derive an approximation of the coverage probability of the coherent beamforming scheme by leveraging two scaling factors that respectively capture the impacts of the phase offsets and the degree of repulsion on the coverage probability. From numerical simulations, we validate the accuracy of our analysis.
Decision-Making for Intrusion Response: Which, Where, in What Order, and How Long?

Generating fine-grained response policies is a fundamental problem for Intrusion Response Systems (IRSs). Although existing schemes determine countermeasures and defense points efficiently, they ignore the deployment orders and execution durations of the selected countermeasures, which may impact response performance. To address this problem, by considering four attributes (i.e., attack damage, deployment cost, negative impact on QoS, and security benefit), we propose a decisionmaking framework for IRSs to reach fine-grained decisions to balance attack damage and response cost. We formulate decisionmaking as a single-objective optimization problem. To efficiently solve this problem, a Genetic Algorithm with Three-dimensional Encoding (GATE) is proposed to not only select countermeasures and defense points, but also determine deployment orders and execution durations. Simulation results demonstrate the efficiency of our approach.
Research on 5G Wireless Network Deployment in Tourist Cities

A 5G planning method for tourist cities is proposed, which elaborates the planning method from the aspects of service capacity forecast, link budget, networking strategy, base station deployment scheme, and antenna selection in combination with the characteristics of tourist city, and finally completes a tourism-oriented The general model of urban 5G base station planning provides reference for similar planning in the future.
Exploring Consensus Mechanisms in Distributed Systems Based on Directed Graph Method

With the development of society, decentralized distributed network is a future development direction of the network. However, how to build consensus mechanism is one of the most important problems in distributed network. Since the end of last century, a group of scientists have explored this issue from different perspectives and proposed some effective algorithms or methods. In this paper, how to build consensus mechanism in distributed system is analyzed based on matrix theory and algebraic graph theory, and a distributed consensus mechanism is formulated. This paper also discusses how to eliminate the impact of malicious nodes in a distributed network, and proposes several methods to eliminate the influence based on the consensus mechanism. At last, the simulation results are given to verify the accuracy and availability of the conclusions.
Beamforming and Power Allocation in Dynamic TDD Networks Supporting Machine-Type Communication

This paper investigates beamforming and power allocation problems in the dynamic time division duplex (TDD) cellular networks. Based on the dynamic TDD coexistence scheme, the network comprises uplink and downlink networks serving machine-type devices (MTDs) and human-type devices (HTDs), respectively. The design goal is to minimize the total system power consumption by optimizing the transmit/receive beamforming and uplink power under MTD and HTD quality-of-service (QoS) constraints. The resulting optimization problem is challenging to solve because the variables to be designed are tightly coupled in the constraints. By using the uplink-downlink duality (UDD) and alternating optimization (AO) algorithm, we propose a novel algorithm to overcome the difficulty in this work. Numerical results demonstrate the superiority of the proposed algorithm.
ISS Interval Observers for Nonlinear Switched Systems under Constrained Switching

In this paper, interval observers are synthesized for globally Lipschitz nonlinear switched systems. Interval observers are designed to ensure positivity and input-to state (ISS) stability of the error dynamics. The first result synthesizes interval observers where some of the modes of the nonlinear error dynamics can be destabilizing. ISS is guaranteed if the activation time of the unstable modes is constrained. The second result uses coordinate transformations. Impulses are applied when switching between each modes coordinate frame to preserve positivity. These impulses tend to be destabilizing, so ISS is guaranteed if switching does not occur too frequently.
An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event

The year 2020 opened with a dramatic epidemic caused by a new species of coronavirus that soon has been declared a pandemic by the WHO due to the high number of deaths and the critical mass of worldwide hospitalized patients, of order of millions. The COVID-19 pandemic has forced the governments of hundreds of countries to apply several heavy restrictions in the citizens’ socio-economic life. Italy was one of the most affected countries with long-term restrictions, impacting the socio-economic tissue. During this lockdown period, people got informed mostly on Online Social Media, where a heated debate followed all main ongoing events. In this scenario, the following study presents an in-depth analysis of the main emergent topics discussed during the lockdown phase within the Italian Twitter community. The analysis has been conducted through a general purpose methodological framework, grounded on a biological metaphor and on a chain of NLP and graph analysis techniques, in charge of detecting and tracking emerging topics in Online Social Media, e.g. streams of Twitter data. A term-frequency analysis in subsequent time slots is pipelined with nutrition and energy metrics for computing hot terms by also exploiting the tweets quality information, such as the social influence of the users. Finally, a co-occurrence analysis is adopted for building a topic graph where emerging topics are suitably selected. We demonstrate via a careful parameter setting the effectiveness of the topic tracking system, tailored to the current Twitter standard API restrictions, in capturing the main sociopolitical events that occurred during this dramatic phase.
Model predictive control with guarantees for discrete linear stochastic systems subject to additive disturbances with chance constraints

We propose a stochastic model predictive control scheme for linear discrete-time and time-invariant systems with chance constraints and additive normal distributed disturbance with known covariance. The proposed approach allows to balance between performance optimization and robustness by adjusting the probability p of the chance constraints. The Controllability Gramian and the Riccati inequality are used to determine a set that contains all disturbances with a probability p. The proposed approach guarantees asymptotic stability of the nominal system, probabilistic convergence of the real system as well as constraint satisfaction and recursive feasibility in terms of probability. A proof of concept example considers the control of a wind turbine, where the wind acts as a normal distributed disturbance.
Transmission of Bursaphelenchus Lignicolus (Nematoda: Aphelenchoididae) By Monochamus Alternatus (Coleoptera: Cerambycidae)

A total of 21 beetle species, all of which were associated with deteriorated pine trees, were examined for the presence of Bursaphelenchus lignicolus. Dauerlarvae of this nematode were recovered from adults of four species of the Cerambycidae; Monochamus alternatus, Acanthocinus griseus, Arhopalus rusticus, and Corymbia succedanea. Between 75%-100% of the field population of M. alternatus in the diseased pine forests was contaminated, carrying an average number of 15,000 living nematodes per insect. Dauerlarvae were transmitted to pine tissues via feeding site of the insect on living branches. It appears that M. alternatus plays an important role in the spread of the nematode and on associated wilt disease in pine forests.
Success Probability in Wirelessly Powered Networks with Energy Correlation

In the analysis of large-scale wirelessly powered networks, the energy correlation is often ignored for analytical tractability. Accounting for the energy correlation, this paper introduces and promotes the Poisson disk process (PDP) as a model for the active RF-powered nodes that succeed in harvesting energy. To show that the model leads to tractable results in several cases of interest, we derive the density and second moment density of the PDP and find the key property that the PDP can be fully characterized by its first- and second-order statistics. Tight bounds for its probability generating functional (PGFL) are also provided. To show that the model is relevant for wirelessly powered networks that exhibit positive energy correlation, we fit the PDP to a given energized point process incorporating practical energy harvesting factors and derive the information transmission success probability. It turns out that the resulting PDP can closely model the distribution of actual energized RFpowered nodes in terms of the success probability and other statistics while preserving analytical tractability.