Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating instant trajectory of an indoor UAV with respect to its dynamics

The application of autonomous indoor UAVs indicates the need for a deeper examination of the trajectory generation. This is due to the fact that the same UAV dynamics are used in more confined space, greatly decreasing the space for maneuvering. The result of path planning methods usually represents a finite set of points with a certain spacing between them. To optimize the system, an effort is made to maximize these distances, while maintaining the shape of the path. To obtain a trajectory suitable for UAV control, it is necessary to define the path between these points analytically. The global trajectory (the whole flight or a significant part of it) can be generated by interpolation or regression of known points. The generation of instant trajectory takes into account the output of reactive navigation and other control systems. The outputs of these systems might provide position targets with large dynamic and directional range over short distances. In our task, we have chosen an approach to generate an instant trajectory via Hermite’s spline. With this approach we can generate an instant trajectory from the current state. During generation the transitions between points are continuous even without knowing the previous states, using only the prescribed dynamics at a given point. Therefore, we know the desired position and velocity of the UAV at all times, preventing sharp movements done by a sudden jump of desired position. The result of the task in this paper is the testing and verification of the Hermite’s spline on a real autonomous UAV, which is currently being developed in collaboration with the Institute of Robotics and Cybernetics at the Slovak University of Technology in Bratislava.
Defect Detection System Of Cloth Based On Convolutional Neural Network

A defect detection algorithm of cloth based on Neural Network by involving effective use of image processing and neural network is presented in this paper. The samples collected on the surface of the cloth are preprocessed by wavelet transforming and Otsu method, then they would be identified and classified through AlexNet. The defect information on the surface of samples is removed by filtering, and the feature is strengthened by threshold method. The image is adjusted to meet the requirement of neural network. The training data is learned by the feature detection layer, so as to achieve the detection of test data. It can detect the flaws on the cloth fast and correctly, and raise the product quality and improve production efficiency. Through the study of 400 collected samples, this method is applied to the 40 samples for testing. The success rate of the trained neural network is 99.2%, and the actual test accuracy was 93.33%, which is higher than 81.8% of Gabor method, 87.2% of MRF method and 90.4% of SE algorithm. It is considered as a suitable way for flaw detection and has a good application prospect.
The Long Pentraxin PTX3 as a Link Between Innate Immunity, Tissue Remodeling, and Cancer

The innate immune system comprises a cellular and a humoral arm. Humoral pattern recognition molecules include complement components, collectins, ficolins, and pentraxins. These molecules are involved in innate immune responses by recognizing microbial moieties and damaged tissues, activating complement, exerting opsonic activity and facilitating phagocytosis, and regulating inflammation. The long pentraxin PTX3 is a prototypic humoral pattern recognition molecule that, in addition to providing defense against infectious agents, plays several functions in tissue repair and regulation of cancer-related inflammation. Characterization of the PTX3 molecular structure and biochemical properties, and insights into its interactome and multiple roles in tissue damage and remodeling support the view that microbial and matrix recognition are evolutionarily conserved functions of humoral innate immunity molecules.
Simulated Calibrators Based Polarimetric Weather Radar External Calibration

As a potential tool for atmospheric observation, weather radar has shown good performance in providing abundant meteorological data during a short time. The key to use weather radar is that the collected data should have sufficient accuracy. To achieve this purpose, weather radar external calibration technique is proposed and applied for practical data processing, which is usually performed based on the deployed calibrators. However, because of the environment of calibration field is complicated and the scheme is difficult to operate, traditional deployed calibrator based methods have several limitations. In this paper, the novel low-cost and repeatable simulated calibrators based weather radar external calibration technology is proposed. Experimental results show that in the single-polarized mode, the proposed method can recover the reflectivity factor accurately from noisy echo data. While in the dual-polarized mode, the crosstalk and channel imbalance can be estimated and compensated successfully by the proposed method.
Automated Synthesis of Custom Networks-on-Chip for Real World Applications

Network-on-Chip (NoC), using a packetized communication model presents a scalable interconnect infrastructure for System-on-Chip (SoC) architectures that meets its Performance, Power and Area (PPA) objectives. A typical NoC consists of building blocks such as routers, resizers and Power and Clock Domain Converters (PCDC). Hand crafting a NoC that meets PPA requirements within Time-to-Market (TTM) constraints is difficult if not intractable for real world systems. In this paper, we present an automated NoC synthesis tool that generates PPA optimized, customized NoC for any system from its behavioral specification. The tool provides solution based on multiple isolated communicating trees with fixed points of inter-communication amongst them. It models a variety of requirements like deadlock-avoidance, quality of service etc. as conflicts represented in a Traffic Conflict Graph (TCG) and uses combinatorial optimization techniques to minimize the conflicts resulting in a better overall design. This is in contrast with traditional approaches which focus only on reducing communication overheads. Implementation and evaluation of the tool in production-grade designs shows that it achieves better topologies as compared to hand-crafted NoCs in only a fraction of time. Across several multi-million gate SoCs, the tool has reduced latency, buffer-size and area by 40%, 50% and 8% on average respectively w.r. t hand-crafted NoCs, while meeting user-specified performance requirements. Use of this tool has brought down the typical NoC design time from several months to less than two weeks thereby considerably reducing design effort and TTM targets.
Melanoma Skin Cancer Detection Using Deep Learning and Advanced Regularizer

Melanoma cancer Detection System is a predictive model that dynamically anticipates melanoma skin cancer by evaluating dermoscopic images with the help of deep learning. The fundamental goals behind this research are to identify skin cancer at early stages by achieving swift results With greater accuracy. The reason behind the goal signifies the problem of increment in skin cancer patients Worldwide, high medical costs and exponential increment of death risk for not starting the diagnosis at early stages which is a result of late detection. Our presented research work proposes a solution to the problem of higher medical costs behind diagnosis, lower accuracy rate in detection and portability problem of the manual detection system. In this system, dermoscopic images are classified to predict skin cancer using a multi-layered CNN approach with multiple regularization techniques named dropout and batch normalization. As a result, our system has provided an accuracy of 93.58% which is higher than most other conventional approaches.
Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?

Cognitive training efficacy is controversial. Although many recent studies indicate that cognitive training shows merit, others fail to demonstrate its efficacy. These inconsistent findings may at least partly result from differences in individuals’ ability to benefit from cognitive training in general, and from specific training types in particular. Consistent with the move toward personalized medicine, we propose using machine learning approaches to help optimize cognitive training gains.
Individual identification using code-modulated visual potentials with left-and-right balance

Electroencephalogram (EEG), measuring brain activities, is believed to be a carrier of brain activity-based biometric. Researchers have explored all kind of approaches to extracting biometric from EEG for individual identification. An emerging trend is to use code-modulated visual potentials (c-VEP) in the EEG-based method to enhance the performance of this kind of individual identification. In this study, we proposed a stimulus mode with left-and-right balance to evoke c-VEPs for individual identification. In the mode, the two panes on the screen, one in the left visual field and another in the right visual field, flashed at the same time under the control of two different m-sequences. The subjects were guided to gaze at one of the panes, one half of trials for the left, the other half for the right. The left and right c-VEPs of subjects were integrated to construct subject identity templates. State-of-the-art detection algorithms in VEP brain-computer interfaces were used to match the c-VEP templates with left-and-right balance for individual identification. We recruited 20 subjects to participate in our experiments and achieved a 92.5% correct recognition rate on their EEG datasets. Our further analysis on the mismatches revealed that the biometric of c-VEPs with left-and-right balance is stable.
Optimization of stacked di-electric bilayer at rear side of PERC solar cell for better light management

Selection of suitable metal-oxide type dielectric passivation layer enhances the open circuit voltage of the Passivated Emitter Rear Contact (PERC) solar cell to a great extent. Incorporation of Aluminium oxide (Al2O3) as passivation layer has been a common practice for the fabrication of PERC solar cell both in industry and research laboratory. However, the optically unmatched refractive index of Al2O3 does not redirect the transmitted photons (Wavelength ranging 900nm to 1100nm) through ~180µm thick c-Si p-type substrate to the solar cell. Hafnium oxide (HfO2) with optically matched RI exhibits comparable passivation property. If HfO2 can be used instead of Al2O3 increase the possibility of redirecting the transmitted photons to the solar cell again without compromising the passivation property, which may increases the solar cell performance. It has been observed that 93% transmitted photons can be reflected back to the solar cell structure for optimized double dielectric stack.
Gibbs Sampling Based Banoian Biclustering of Gene Expression Data

This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.
A Q-learning Approach for SoftECU Design in Hybrid Electric Vehicles

A software electronic control unit (SoftECU) implements simplified versions of the algorithms coded in a corresponding real ECU. In this paper a Q-learning approach for the design of SoftECUs is proposed. The training phase of the SoftECU is based on the mismatch between the commands provided by the real ECU and those coming from the SoftECU model. The technique is applied to the case of an energy management ECU for hybrid electric vehicles. The effectiveness of the SoftECU is validated by considering the new European driving cycle and the worldwide harmonized light vehicles test procedure.
Controllability Function as Time of Motion: An Extension of the Set of Controls in the Two-Dimensional Case

For the two dimensional canonical system, a family of bounded finite-time stabilizing positional controls is extended in terms of a certain parameter. We use Korobov's controllability function, which is a Lyapunov-type function. We focus on the case when the value of the controllability function at the initial position is exactly the time of motion from the initial point to the origin. As one of the consequences of such an extension, we ascertain that to each bounded positional control there could correspond one, two or three different times of motion from a given position to the origin.
SLPI Inhibits ATP-Mediated Maturation of IL-1β in Human Monocytic Leukocytes: A Novel Function of an Old Player

Interleukin-1β (IL-1β) is a potent, pro-inflammatory cytokine of the innate immune system that plays an essential role in host defense against infection. However, elevated circulating levels of IL-1β can cause life-threatening systemic inflammation. Hence, mechanisms controlling IL-1β maturation and release are of outstanding clinical interest. Secretory leukocyte protease inhibitor (SLPI), in addition to its well-described anti-protease function, controls the expression of several pro-inflammatory cytokines on the transcriptional level. In the present study, we tested the potential involvement of SLPI in the control of ATP-induced, inflammasome-dependent IL-1β maturation and release. We demonstrated that SLPI dose-dependently inhibits the ATP-mediated inflammasome activation and IL-1β release in human monocytic cells, without affecting the induction of pro-IL-1β mRNA by LPS. In contrast, the ATP-independent IL-1β release induced by the pore forming bacterial toxin nigericin is not impaired, and SLPI does not directly modulate the ion channel function of the human P2X7 receptor heterologously expressed in Xenopus laevis oocytes. In human monocytic U937 cells, however, SLPI efficiently inhibits ATP-induced ion-currents. Using specific inhibitors and siRNA, we demonstrate that SLPI activates the calcium-independent phospholipase A2β (iPLA2β) and leads to the release of a low molecular mass factor that mediates the inhibition of IL-1β release. Signaling involves nicotinic acetylcholine receptor subunits α7, α9, α10, and Src kinase activation and results in an inhibition of ATP-induced caspase-1 activation. In conclusion, we propose a novel anti-inflammatory mechanism induced by SLPI, which inhibits the ATP-dependent maturation and secretion of IL-1β. This novel signaling pathway might lead to development of therapies that are urgently needed for the prevention and treatment of systemic inflammation.
Perception of Verticality and Vestibular Disorders of Balance and Falls

Objective: To review current knowledge of the perception of verticality, its normal function and disorders. This is based on an integrative graviceptive input from the vertical semicircular canals and the otolith organs. Methods: The special focus is on human psychophysics, neurophysiological and imaging data on the adjustments of subjective visual vertical (SVV) and the subjective postural vertical. Furthermore, examples of mathematical modeling of specific vestibular cell functions for orientation in space in rodents and in patients are briefly presented. Results: Pathological tilts of the SVV in the roll plane are most sensitive and frequent clinical vestibular signs of unilateral lesions extending from the labyrinths via the brainstem and thalamus to the parieto-insular vestibular cortex. Due to crossings of ascending graviceptive fibers, peripheral vestibular and pontomedullary lesions cause ipsilateral tilts of the SVV; ponto-mesencephalic lesions cause contralateral tilts. In contrast, SVV tilts, which are measured in unilateral vestibular lesions at thalamic and cortical levels, have two different characteristic features: (i) they may be ipsi- or contralateral, and (ii) they are smaller than those found in lower brainstem or peripheral lesions. Motor signs such as head tilt and body lateropulsion, components of ocular tilt reaction, are typical for vestibular lesions of the peripheral vestibular organ and the pontomedullary brainstem (vestibular nucleus). They are less frequent in midbrain lesions (interstitial nucleus of Cajal) and rare in cortical lesions. Isolated body lateropulsion is chiefly found in caudal lateral medullary brainstem lesions. Vestibular function in the roll plane and its disorders can be mathematically modeled by an attractor model of angular head velocity cell and head direction cell function. Disorders manifesting with misperception of the body vertical are the pusher syndrome, the progressive supranuclear palsy, or the normal pressure hydrocephalus; they may affect roll and/or pitch plane. Conclusion: Clinical determinations of the SVV are easy and reliable. They indicate acute unilateral vestibular dysfunctions, the causative lesion of which extends from labyrinth to cortex. They allow precise topographical diagnosis of side and level in unilateral brainstem or peripheral vestibular disorders. SVV tilts may coincide with or differ from the perception of body vertical, e.g., in isolated body lateropulsion.
Quantum Public Key Distribution using Randomized Glauber States

State-of-the-art Quantum Key Distribution (QKD) is based on the uncertainty principle of qubits on quantum measurements and is theoretically proven to be unconditionally secure. Over the past three decades, QKD has been explored with single photons as the information carrier. More recently, attention has shifted towards using weak coherent laser pulses as the information carrier. In this paper, we propose a novel quantum key distribution mechanism over a pure optical channel using randomized Glauber states. The proposed mechanism closely resembles a quantum mechanical implementation of the public key envelope idea. The core idea can be described in five steps as follows: 1. A user (Bob) generates a Glauber state as a quantum public key envelope (QPKE) by randomly modulating a secret phase φr, known only to Bob, and transmits it over an optical channel to the other user (Alice). 2. Alice modulates a key phase φk into the QPKE based on a random key and selected modulation scheme and returns it to Bob. 3. For the returning QPKE, Bob derandomizes it with his private key or the phase -φr and then 4. passes it to a coherent receiver to measure the key phase φk. 5. For better security, differential phase-shift keying (DPSK) technique with a reference list is applied to extract keys. For the proposed solution, we explore physical countermeasures to provide path authentication and to avoid man-in-the-middle attacks. Other attack vectors can also be effectively mitigated by leveraging the QPKE, the uncertainty principle and the DPSK modulation technique.
Role of Src and Cortactin in Pemphigus Skin Blistering

Autoantibodies against desmoglein (Dsg) 1 and Dsg3 primarily cause blister formation in the autoimmune disease pemphigus vulgaris (PV). Src was proposed to contribute to loss of keratinocyte cohesion. However, the role and underlying mechanisms are unclear and were studied here. In keratinocytes, cell cohesion in response to autoantibodies was reduced in Src-dependent manner by two patient-derived PV-IgG fractions as well as by AK23 but not by a third PV-IgG fraction, although Src was activated by all autoantibodies. Loss of cell cohesion was progredient in a timeframe of 24 h and AK23, similar to PV-IgG, interfered with reconstitution of cell cohesion after Ca2+-switch, indicating that the autoantibodies also interfered with desmosome assembly. Dsg3 co-localized along cell contacts and interacted with the Src substrate cortactin. In keratinocytes isolated from cortactin-deficient mice, cell adhesion was impaired and Src-mediated inhibition of AK23-induced loss of cell cohesion for 24 h was significantly reduced compared to wild-type (wt) cells. Similarly, AK23 impaired reconstitution of cell adhesion was Src-dependent only in the presence of cortactin. Likewise, Src inhibition significantly reduced AK23-induced skin blistering in wt but not cortactin-deficient mice. These data suggest that the Src-mediated long-term effects of AK23 on loss of cell cohesion and skin blistering are dependent on cortactin-mediated desmosome assembly. However, in human epidermis PV-IgG-induced skin blistering and ultrastructural alterations of desmosomes were not affected by Src inhibition, indicating that Src may not be critical for skin blistering in intact human skin, at least when high levels of autoantibodies targeting Dsg1 are present.
Ridge Polynomial Neural Network with Error Feedback for Recursive Multi-step Forecast Strategy: A Case Study of Carbon Dioxide Emissions Forecasting

Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NN s with error feedbacks appears when there is a need for recursive multi-step forecast strategy because the observed values must be known in order to calculate network errors. This paper proposes to use the last calculated error after finishing training NNs with error feedbacks because the observed values are unknown. This last calculated error is used as a fixed value when producing forecasts using recursive multi-step forecast strategy. For that, this paper investigated this simple solution with a NN with error feedback called the ridge polynomial neural network with error feedback (RPNN-EF). Carbon dioxide emissions for three countries in the organization of the petroleum exporting countries (OPEC) were used in this investigation. The forecasting accuracy of RPNN-EF was compared with seven forecasting methods. According to the obtained results, on average, the proposed solution produces reasonable forecasts compared to the seven forecasting methods. Therefore, this solution can be suggested for NNs with error feedbacks for recursive multi-step forecast strategy.
Key Advances in Pervasive Edge Computing for Industrial Internet of Things in 5G and Beyond

This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC—distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.