Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity Analysis and Receiver Design in the Presence of Fiber Nonlinearity

The majority of today's global Internet traffic is conveyed through optical fibers. The ever-increasing data demands have pushed the optical systems to evolve from using regenerators and direct-direction receivers to a coherent multi-wavelength network. Future services like cloud computing and virtual reality will demand more bandwidth, so much so that the so called capacity-crunch is anticipated to happen in near future. Therefore, studying the capacity of the optical system is needed to better understanding and utilizing the existing fiber network. The characterization of the capacity of the dispersive and nonlinear optical fiber described by the nonlinear Schr{\"o}dinger equation is an open problem. There are a number of lower bounds on the capacity which are mainly obtained based on the mismatched decoding principle or by analyzing simplified channels. These lower bounds either fall to zero at high powers or saturate. The question whether the fiber-optical capacity has the same behavior as the lower bounds at high power is still open. Indeed, the only known upper bound increases with the power unboundedly. In this thesis, we first study how the fiber nonlinear distortion is modeled in some simplified channels and what is the influence of the simplifying assumptions on the capacity. To do so, the capacity of three different memoryless simplified models of the fiber-optical channel are studied. The results show that in the high-power regime the capacities of these models grow with different pre-logs, which indicates the profound impact of the simplifying assumptions on the capacity of these channels. Next, we turn our attention to demodulation and detection processes in the presence of fiber nonlinearity. We study a two-user memoryless network. It is shown that by deploying a nonlinearity-tailored demodulator, the performance improves substantially compared with matched filtering and sampling. In the absence of dispersion, we show that with the new receiver, unlike with matched filtering and sampling, arbitrarily low bit error rates can be achieved. Furthermore, we show via simulations that performance improvements can be obtained also for a low-dispersion fiber. Then, we study the performance of three different dispersion compensation methods in the presence of inter-channel nonlinear interference. The best performance, in terms of achievable information rate, is obtained by a link with inline per-channel dispersion compensation combined with a receiver that compensates for inter-channel nonlinearities. Finally, the capacity analysis is performed for short-reach noncoherent channel, where the source of nonlinearity is not the fiber but a square-law receiver. Capacity bounds are established in the presence of optical and thermal noises. Using these bounds we show that optical amplification is beneficial at low signal-to-noise ratios (SNRs), and detrimental at high SNRs. We quantify the SNR regime for each case for a wide range of channel parameters.
Efficient and Robust Distributed Digital Codec Framework for Jointly Sparse Correlated Signals

In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer, as appropriate. Then, these quantized values are mapped to the elements of a finite field, except 0, while the zero coefficients are mapped to 0. Subsequently, compressed sensing over finite fields is applied to obtain measurements. We name such an order first quantization then compressed sensing. The two measurement signals are then converted to bit sequences, modulated, and transmitted through separate additive white Gaussian noise (AWGN) channels. At the central receiver, which has access to both channels, following demodulation, an innovative joint belief propagation (JBP) algorithm is performed for joint recovery. In this algorithm, we introduce a new type of constraint node, i.e., correlation constraint nodes, which connect two factor graphs that separately represent the CS encoding matrix of each signal. The experimental results prove that under the same framework the proposed scheme provides significant performance improvements compared to the scheme that ignores the correlated information between jointly sparse signals, especially when the correlation coefficient is high. Then, to answer the question of which order is better, we construct the first compressed sensing, then quantization framework, for fairness, two cutting edge jointly greedy pursuit algorithms are separately adopted at the joint decoder. Through simulations, we validate that the proposed framework provides more effective and robust transmissions.
Selecting Elliptic Curves with Minimal Estimation of Pairing Computation

In recent years, more and more applications based on bilinear pairing computation have been constructed. The very first issue is to pick up a pairingfriendly elliptic curve, which is efficient and secure. Many schemes have been published to generate secure curves. However, when it comes to implementation, the performance is a major concern. We estimate the cost of computing the reduced Tate pairing on the elliptic curves of short Weierstrass form with curve parameters, including the field size q, the subgroup order r, and the embedding degree k. From a large amount of curves, we can simply determine the curve on which the pairing computation is much efficient. We also generate some types of curves to perform pairing computation on SageMath and measure the real time cost. The result shows our estimation is accurate.
Analysis of the Tutorial Action and its Impact on the Overall Development of the Students. The Case of the University of Castilla La Mancha, Spain

The results of a study aimed to analyze the perception of university students of the degrees of Early Childhood Education and Primary Education of the University of Castilla La Mancha (Spain), on how university tutoring affects the improvement of the integral development of students. The integral development includes three dimensions: personal, academic and professional, after the implementation of the framework of the European Higher Education Area (EHEA). A quantitative study was carried out by applying a questionnaire to 493 students. The results show the usefulness of university tutoring to respond to academic, teaching, personal, professional and, to a lesser extent, bureaucratic needs. The usefulness of tutoring for the monitoring of active methodologies and as a space for improving relations between teachers and students is also observed.
A Japanese case of mitochondrial 3‐hydroxy‐3‐methylglutaryl‐CoA synthase deficiency who presented with severe metabolic acidosis and fatty liver without hypoglycemia

Mitochondrial 3‐hydroxy‐3‐methylglutaryl‐CoA synthase deficiency (mHS deficiency) is a rare autosomal recessive inborn error of ketogenesis caused by a mutation in the HMGCS2 gene, which is characterized by non‐(hypo)‐ketotic hypoglycemia, lethargy, and hepatomegaly during acute infection and/or prolonged fasting. Clinical presentations are similar to fatty acid oxidation defects; however, diagnosis of mHS deficiency is difficult because of poor biochemical markers. We report the case of a 12‐month‐old Japanese boy with mHS deficiency who presented with a coma, and hepatomegaly, but no hypoglycemia after a febrile episode and poor oral intake. Metabolic acidosis and severe fatty liver were observed. Serum acylcarnitine analysis revealed a slightly decreased free carnitine (C0) level and an increased acetylcarnitine (C2) level. Urinary organic acid analysis revealed hypoketotic dicarboxylic aciduria, and increased excretions of glutarate, and, retrospectively, 4‐hydroxy‐6‐methyl‐2‐pyrone. Although the patient did not present with hypoglycemia, the severe fatty liver and elevated free fatty acids to total ketone bodies ratio strongly suggested an inborn error of ketogenesis. In the analysis of the HMGCS2 gene, compound heterozygous mutations of c. 130_131ins C (L44PfsX29) and c. 1156_1157insC (L386PfsX73) were identified, which led to the diagnosis of mHS deficiency. He had recovered without any complication by the therapy, including intravenous glucose infusion. Unlike the previously reported cases of mHS deficiency, our case did not present with hypoglycemia and the fatty liver lasted over several months. mHS deficiency should be taken into consideration when a patient has severe metabolic acidosis and fatty liver with no or subtle ketosis, even without hypoglycemia.
Real-time robust complementary visual tracking with redetection scheme

Abstract. Recently, correlation filter-based trackers have been widely investigated due to their high efficiency and robustness. However, most of them use a fixed cosine window to deal with boundary effects and ignore the reliability of tracking results, which results in poor tracking performance when the target endures severe occlusion and large appearance variation. In order to deal with these issues, we propose a tracking framework with an adaptive cosine window, which is composed of a reliability estimation module and a redetection module. First, we incorporate the object likelihood map into the traditional fixed cosine window to form an adaptive cosine window, which can enlarge the searching region and effectively cope with boundary effects. Second, the peak-to-sidelobe ratio of HOG-based correlation response map and the color score of each frame are adopted to estimate the reliability of tracking results. Third, we introduce the Siamese tracker to redetect targets in case of tracking failures. Finally, a target pyramid scheme is built to deal with scale variation. Extensive experiments on the OTB-2013, OTB-2015, TemplateColor, UAV123@10 fps, and VOT-2015 demonstrate that our proposed method outperforms favorably against the state-of-the-art methods with real-time tracking speed.
Decision making algorithm for development strategy of information systems

The paper presents algorithm of decision making for development strategy of information systems. The process of development is planned taking into account the internal and external factors of the enterprise which affect the prospects of development of both the information system and the whole enterprise. The initial state of the system must be taken into account. The total risk is the criterion for selecting the strategy. The risk is calculated using statistical and fuzzy data of system's parameters. These data are summarized by means of the function of uncertainty. The software for the realization of the algorithm of decision making on choosing the development strategy of information system is developed and created in this paper.
Modeling and Simulation of Nonconductive Particles Trajectories in a Multifunctional Electrostatic Separator

Electrostatic separation is a technique based on the electric field forces acting on small charged or polarized particles. Several electrostatic machines have been developed over past decades, operating with different principles and treating various mixture types. The multifunctional electrostatic separator is one of the most efficient installations for the treatment of micronized materials. This paper aims to numerically simulate the dynamic behavior of micronized insulating particles in this type of separator. The numerical model of the electrostatic field calculated by COMSOL is used in a MATLAB program to simulate particle trajectories under the effect of electrical and mechanical forces. The numerical simulation implemented takes into account the impact of the particles with the rotating electrode. The intensity and direction of the forces exerted on the particles in the high-intensity electric field area determine their behavior, and thus, the quality of the separation.
Comparative Analysis and Comparison of Various AQM Algorithm for High Speed

Congestion deteriorates the network performance. In this paper various congestion control AQM algorithm are analyzed and surveyed with their shortcomings and their advantages. The main objective of the paper is to study existing AQM algorithm and develop a new AQM algorithm that gives better result than the existing algorithm. AQM are the router based mechanism for early detection of congestion in the computer network. The basic idea of AQM is to sense and detect congestion in advance and to inform the sender to reduce its sending rate, thereby reducing the number of packets sent in the network and control the congestion. There are several AQM algorithms that controls the congestion. In this paper, we have surveyed, compared and analyzed Random Early Detection (RED), Flow Random Early Detection (FRED), Stabilized Random Early Detection (SRED), Stochastic Fair Queuing (SFQ), Random Exponential Marking (REM), BLUE, Stochastic Fair BLUE (SFB) AQM algorithms. Performance parameter are tested and evaluated in NS2 simulator. After analyzing it was found that RED AQM compared with SFQ and REM achieved best result in terms of delay. SFQ had minimum average ratio and RED had max loos ratio. REM algorithm showed the best result with respect to throughput loss ratio and link utilization. Improvement: After analyzing and comparing several AQM algorithm it was found that no single algorithm can solve all the problems. Hence a research is needed to develop a new AQM algorithm that has good link utilization, is fair enough, has less loss ratio, require less space and easy for configuration.
A mixed reality framework for surgical navigation: approach and preliminary results

A mixed reality framework for surgical navigation: approach and preliminary results by Shravan Murlidaran Master’s in Robotics Engineering Worcester Polytechnic Institute The overarching purpose of this research is to understand whether Mixed Reality can enhance a surgeons manipulations skills during minimally invasive procedures. Minimally-invasive surgery (MIS) utilizes small cuts in the skin or sometimes natural orifices to deploy instruments inside a patients body, while a live video feed of the surgical site is provided by an endoscopic camera and displayed on a screen. MIS is associated with many benefits: small scars, less pain and shorter hospitalization time as compared to traditional open surgery. However, these benefits come at a cost: because surgeons have to work by looking at a monitor, and not down on their own hands, MIS disrupts their eye-hand coordination and makes even simple surgical maneuvers challenging to perform. In this study, we wish to use Mixed Reality technology to superimpose anatomical models over the surgical site and explore if it can be used to mitigate this problem.
Genetic algorithm‐based content distribution strategy for F‐RAN architectures

Fog radio access network (F‐RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm‐based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F‐RAN. First, an F‐RAN system model is presented that includes a certain number of randomly distributed fog access points (F‐APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F‐RANs is described. Third, the details of the proposed optimal genetic algorithm‐based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.
Determinants of spatial patterns of canopy tree species in a tropical evergreen forest in Gabon

QUESTIONS: We examined the spatial patterns of dominant canopy species in a tropical forest to investigate: (a) what is the niche occupancy of canopy species with respect to topographic gradients; (b) what are the dominant ecological processes that explain their distribution; (c) what are the interactions among the most prevalent canopy species; and (d) what are the interactions between canopy species adults and juveniles trees? LOCATION: Rabi permanent CTFS‐ForestGEO plot, Gabon. METHODS: We selected the four most abundant canopy species and one timber species. We used Berman's test to determine the effect of three topographic variables on the distribution of each species and univariate analysis to model the spatial pattern of each species using either an inhomogeneous Poisson process or an inhomogeneous Cox process. We also used a bivariate form of the pair correlation function (PCF) to determine the spatial interaction between species and the correlation among conspecific adult and juvenile trees. RESULTS: Four of the five species had aggregated spatial patterns while Lophira alata showed spatial randomness. Most of the variance in the local tree density was explained by within‐population dispersal processes rather than environmental factors. Bivariate PCF tests showed significant segregation between species associations. Two species exhibited aggregation at small distances between young and adult trees, while others showed either complete spatial randomness at small inter‐tree distances or segregation at large distances. CONCLUSIONS: This study showed that the spatial pattern in the majority of canopy species was aggregation. Seed dispersal limitation mainly explained the observed aggregation pattern. Habitat filtering, as evidenced by the influence of topographic variables on niche occupancy, marginally, yet significantly, explained this pattern. The different spatial patterns of the principal species permit their coexistence. Spatial segregation among adult and juvenile trees reveals a strong pattern of either species‐specific seed predation or pathogens.
HarpyGame: um jogo sério customizável com interface multimodal para reabilitação de indivíduos pós-AVE

CYRINO, Gabriel F. HarpyGame: A Customizable Serious Game with Multimodal Interface for Upper Limb Rehabilitation after Stroke. Masters Dissertation – Faculty of Electrical Engineering – UFU. Uberlândia, 2019. Stroke is the most common disease that leads to the dexterity impairment of the upper limbs. To provide a better quality of life, patients must submit to therapeutic resources that focus on the gain of muscle activity. In fact, rehabilitation must be precocious, intensive and repetitive, which leads to the challenge of how to maintain patient motivation and dedication. In this context, serious games have emerged as an advantageous and enjoyable alternative to rehabilitation treatment, when compared to traditional therapies. However, most serious games are limited to simple 2D environments or low-quality 3D environments, which can affect patient interest and engagement. Moreover, in many of these games, it is not possible to adapt the rehabilitation tasks, according to the patient clinical stage. Thus, this work presents the development of a customizable serious game, based on Virtual Reality techniques to achieve a more natural and intuitive interface. Besides, since each patient has his own limitation, the game was developed to allow different types of input devices. In so doing, the system was designed considering four intercommunicable layers: 1) A control panel, responsible for the control and customization of the game, registration of patients and generation of comparative reports; 2) A serious game that presents an environment with realistic graphics, containing levels and adaptive tasks; 3) A database thatmaintains information such as patient profiles, game settings, goals and challenges achieved; 4) And a multimodal interface that allows the use of input devices such as Myo, Joysticks and an assisted support platform in order to provide the suitability of different patient profiles in the game. The research considered the assistance of physiotherapists to provide protocols that the game followed. Results indicated significant acceptance by the patients, implying the system’s potential use in the post-stroke rehabilitation process of the upper limbs.
Measuring plasma C4D to monitor immune complexes in lupus nephritis

Objective Because currently available assays that measure circulating immune complexes (ICx) are suboptimal, a novel assay was recently developed measuring C4d, a stable product of activation of the classical complement pathway. The present study aimed to establish the value of measuring plasma C4d levels in a longitudinal cohort of patients with severe refractory SLE who were treated with a combination therapy of rituximab with belimumab (RTX+BLM). Methods Fifteen patients with SLE who were treated with RTX+BLM in a phase 2A, open label study were included to sequentially measure plasma C4d levels and correlated to well-established markers of ICx-formation, that is, autoantibodies against double-stranded (ds) DNA, autoantibodies against C1q and proteinuria. The performance of plasma C4d measurements, C4 measurements and the ratio of C4d over C4 (C4d:C4) was evaluated. Results After establishing that on RTX+BLM treatment kinetics of C4d levels was distinct from traditional C3 and C4 levels, we found strong correlation of C4d:C4 with anti-dsDNA (R=0.76, p<0.001) and anti-C1q (R=0.65, p<0.001) autoantibody levels, which outperformed both stand-alone C4 and C4d levels. Additionally, changes in C4d:C4 over time correlated strongly with changes in proteinuria (R=0.59, p<0.001) as well as anti-dsDNA (R=0.46, p=0.003) and anti-C1q (R=0.47, p=0.002). Conclusion In patients with severe SLE, plasma C4d levels in relation to C4 levels is useful for longitudinal monitoring after RTX+BLM treatment to reflect amelioration of classical complement activation by ICx as well as proteinuria.
Enhanced Security Model for SQL Injection attacks for web Database

Information security is a critical element of business operations. The SQL Injection attack is the attack to the web application to have abused the vulnerability which is called SQL Injection. SQL injection is a technique often used to attack a website. The SQL Injection attack poses a serious threat to the security of web applications because SQL is used to operate the database in a lot of applications. The SQL Injection attack is the method that executes any SQL sentence when using arbitrarily character for the search condition or the update contents. By generating SQL sentence and changing existing command, attackers can make data public which should be hidden, and interpolate.
Video Salient Object Detection via Robust Seeds Extraction and Multi-Graphs Manifold Propagation

Video salient object detection aims at distinguishing the salient objects from the complex background and highlighting them uniformly in the spatiotemporal domain, which still suffers from the interference of the complicated dynamic background in unconstrained videos. To address this problem, we propose a novel coarse-to-fine spatiotemporal salient object detection method. Specifically, we first model a novel motion energy to exclude the motion noise by exploiting the motion magnitude and motion orientation. Then, a supervoxel-level inter-frame graph model is constructed for each pair of adjacent frames independently, and a robust graph clustering-based saliency seed generation method is proposed to produce a coarse saliency map. Furthermore, the supervoxel-level inter-frame graph model is reconstructed by considering the regional spatiotemporal consistency constraint based on the coarse saliency map. The prior information obtained from pixel clustering is also taken into account to optimize the weight of the inter-frame graph model. Finally, a multi-graphs saliency propagation method is exploited under the manifold regularization framework by fusing the motion energy and appearance feature to refine the coarse saliency map. The extensive experiments on two widely used datasets validate the effectiveness and superiority of the proposed method against 13 state-of-the-art methods in terms of PR-curves, scores of S-measure, $F_{\beta }$ , and MAE.