Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel RPI Set Computation Method for Discrete-time LPV Systems with Bounded Uncertainties

Set invariance plays a fundamental role in the analysis and design of linear systems. This paper proposes a novel method for constructing robust positively invariant (RPI) sets for discrete-time linear parameter varying (LPV) systems. Starting from the stability assumption in the absence of disturbances, we aim to construct the RPI sets for parametric uncertain system. The existence condition of a common quadratic Lyapunov function for all vertices of the polytopic system is relaxed in the present study. Thus the proposed method enlarges the application field of RPI sets to LPV systems. A family of approximations of minimal robust positively invariant(mRPI) sets are obtained by using a shrinking procedure. Finally, the effect of scheduling variables on the size of the mRPI set is analyzed to obtain more accurate set characterization of the uncertain LPV system. A numerical example is used to illustrate the effectiveness of the proposed method.
Multiclass Vessel Detection From High Resolution Optical Satellite Images Based On Deep Neural Networks

One of the core components of remote sensing based maritime surveillance applications is vessel detection. It helps to prevent and investigate different unlawful actions as well as environmental hazards present at sea. Growing constellation of very high resolution (VHR) optical satellite sensors are able to frequently cover large areas with spatial resolution of up to 0.3m per pixel, which is sufficient to detect and distinguish different types of vessels. This paper presents a novel method for automatic multiclass vessel detection with use of deep convolutional neural networks (DCNN) and principle component analysis (PCA). The described approach provides reasonable performance and therefore is potentially suitable for near real time (NRT) applications.
Sentinel based malicious relay detection in wireless IoT networks

Increased device connectivity and information sharing in wireless IoT networks increases the risk of cyber attack by malicious nodes. In this paper, we present an effective and practical scheme for detecting data integrity and selective forwarding attacks launched by malicious relays in wireless IoT networks. The proposed scheme exploits the broadcast nature of wireless transmission and provides a sentinel based approach to intrusion detection. Our detection scheme assumes a general noise model for the network where different wireless links may have different packet error probability (PEP). Further, our detection scheme is effective even in scenarios where different wireless links in the network employ distinct modulation and coding schemes at the physical layer. This detection scheme has application in practical wireless IoT networks, such as those based on the recently introduced IEEE 802.11ah standard.
Sb3+ Doping-Induced Triplet Self-Trapped Excitons Emission in Lead-Free Cs2SnCl6 Nanocrystals.

Doped halide perovskite nanocrystals (NCs) have opened new opportunities for the emerging optical and optoelectronic applications. Here, we describe a hot-injection synthesis of all-inorganic lead-free Cs2SnCl6 and Sb3+ doped Cs2SnCl6 NCs. Cs2SnCl6 NCs present a blue emission peak at 438 nm, whereas a new broad-band emission peak appears at 615 nm for the Sb3+ doped NCs. A comparative structural and spectral characterizations of Sb3+ doped Cs2SnCl6 NCs with micrometer-sized undoped and Sb3+ doped crystals show that the formation of broadband orange emission is originted from triplet self-trapped excitons, attributed to the 3Pn - 1S0 transitions ( n = 0, 1, 2). Our results in Sb3+ doped Cs2SnCl6 materials provides insights into the machanisms of doping-induced emission centers, and it extends the existing knowledge of optical properties of doped halide NCs for further studies.
Performance Comparison of Different Inverter Stages CS-VCO in 0.18μm CMOS Technology

This paper presents a comparative study of two different topologies of Current Starved Oscillator (CS-VCO) i.e., (3-stage & 5-stage) on the basis of phase noise, power dissipation and centre frequency of oscillation parameters. With the observation, it is measured that the power dissipation can be scaled down to 28.53% for 3-stage w.r. t. 5-stage CS-VCO topology. In such manner, a design of optimal and robust current starved-VCO is achieved. The performance of circuit is simulated on 0.18$\mu$m CMOS Technology.
Air Quality Monitoring Using IoT: A Survey

The increase in industrial activities and the rapid urbanization of human populations had a terrible effect on global air quality. Thousands of factories and billions of vehicles release enormous amounts of pollutants into the air every day; dangerously affecting human health. Many epidemiological studies pointed out the responsibility of air pollution in many health issues, the reason why monitoring air quality became an obligation to prevent or limit these issues. Conventional systems based on measuring stations are expensive and offer low data granularity. As a result, researchers are increasingly targeting IoT-based systems. However, elaborating a new system for air quality monitoring requires an awareness of the state of the art and the mastery of a certain amount of specific knowledge (pollutants, their health effects, the sensing equipment, the IoT possible configuration,.). This paper aims to answer these necessities by reviewing the existing works on air quality monitoring using IoT with the focus on lasted trends and challenges.
Identification of A Novel Class of Benzofuran Oxoacetic Acid-Derived Ligands that Selectively Activate Cellular EPAC1

Cyclic AMP promotes EPAC1 and EPAC2 activation through direct binding to a specific cyclic nucleotide-binding domain (CNBD) within each protein, leading to activation of Rap GTPases, which control multiple cell responses, including cell proliferation, adhesion, morphology, exocytosis, and gene expression. As a result, it has become apparent that directed activation of EPAC1 and EPAC2 with synthetic agonists may also be useful for the future treatment of diabetes and cardiovascular diseases. To identify new EPAC agonists we have developed a fluorescent-based, ultra-high-throughput screening (uHTS) assay that measures the displacement of binding of the fluorescent cAMP analogue, 8-NBD-cAMP to the EPAC1 CNBD. Triage of the output of an approximately 350,000 compound screens using this assay identified a benzofuran oxaloacetic acid EPAC1 binder (SY000) that displayed moderate potency using orthogonal assays (competition binding and microscale thermophoresis). We next generated a limited library of 91 analogues of SY000 and identified SY009, with modifications to the benzofuran ring associated with a 10-fold increase in potency towards EPAC1 over SY000 in binding assays. In vitro EPAC1 activity assays confirmed the agonist potential of these molecules in comparison with the known EPAC1 non-cyclic nucleotide (NCN) partial agonist, I942. Rap1 GTPase activation assays further demonstrated that SY009 selectively activates EPAC1 over EPAC2 in cells. SY009 therefore represents a novel class of NCN EPAC1 activators that selectively activate EPAC1 in cellulae.
A UAS Platform for Assessing Spectral, Structural, and Thermal Patterns of Arctic Tundra Vegetation

As climate continues to warm, detailed geospatial spectral, structural, and thermal information, related to water, carbon, and energy cycling, are required for modeling the future state of the Arctic biome. To address these needs we have developed a cost-effective, multi-sensor UAS-based remote sensing platform for acquiring high resolution spectral, structural, and thermal measurements of Arctic vegetation. We successfully deployed this remote sensing platform in three sites along an elevation gradient near Nome, Alaska in summer 2017. Corresponding workflows have been further developed for assessing spectral, structural, and thermal patterns of arctic tundra biomes from the collected datasets. Our results demonstrate that the UAS platform can successfully (1) map heterogenous vegetation composition and (2) assess corresponding spectral, structural, and thermal patterns of Arctic tundra biomes. This study not only presents a novel and innovative approach for collecting high resolution spectral, structural and thermal information for characterizing Arctic tundra biome patterns, but also a basis for informing the modeling of climate feedbacks between the biosphere and the atmosphere in response to ongoing global change.
Domain Adaptation for Semantic Segmentation Using Convolutional Neural Networks

Semantic segmentation is an important analysis task for the investigation of aerial imagery. Recently, the arise of con-volutional neural networks has increased the performance of computer vision methods considerably. But the success of deep learning applications mostly relies on the availability of sufficiently large training datasets. However, the manual annotation of images is time consuming and needs human effort. To reduce the necessary amount of training data it is possible to fine-tune a model which is pre-trained on a different larger dataset. But usually orthophotos are affected by weather and sensor dependent light conditions. Additionally, such images are composed of imbalanced classes which leads to poor pixel-wise classification results for sparsely represented labels. In this paper we propose a convolutional neural network based domain adaptation method for semantic segmentation. The encoder-decoder structure uses adaptation modules and an alternately training procedure to adapt the network to the target domain. We employ the large ISPRS Potsdam dataset as source domain to train a base model and adapt it using very few samples. We compared our method to the common fine-tuning approach and evaluated the results for a decreasing number of training samples. We observed an improvement of the average overall prediction accuracy but especially for the sparsely represented vehicle class.
MEMS based piezoresistive pressure sensor design of circular membrane with annularly grooves and center mass for measurements of low pressure

The MEMS based piezoresistive pressure sensor presented in this paper utilizes a novel circular bossed diaphragm structure with annular grooves for low pressure measurements. An attempt has been made to alleviate the trade-off between sensitivity and linearity hence obtaining an improved sensor performance . The performance parameters such as stress and membrane deflection have been analysed using the Finite element model (FEM). The novel structure is characterised by annular grooves which provide a considerably high value of sensitivity of 22.84 mv/kPa and a central mass which partially stiffens the membrane to provide reduced deflection and hence non-linearity. The proposed sensor possesses advantages like micro dimension, low nonlinearity, high sensitivity and a stable dynamic behaviour. The proposed structure hence proves to be ideal for low pressure applications below 5KPa.
A Neural-Network-Based Real-end Collision Prediction Mechanism for Smart Cities

Rear-end collisions is a serious issue in smart cities with Intelligent Transportation System, which is one of the main causes of casualties. With the development of Internet of Vehicle, many researches have been conducted to solve this problem. Parametric based methods are first proposed to send warnings before rear-end collisions occur. Deep learning methods are more suitable for this issue, which have better adaptive capability to different environments. However, the proposed deep learning based methods still have some limitations and perform not well in real scenes. In this paper, a neural-network-based mechanism is proposed to predict rear-end collisions. The experimental results show that the proposed mechanism performs better than existing schemes and can effectively help drivers to avoid dangers.
Decentralized Formation Control of a Swarm of Quadrotor Helicopters

We propose a decentralized hybrid swarm control mechanism for quadrotor helicopters. The mechanism includes formation, rotation, tracking, and inter-agent collision avoidance capabilities. We provide stability analysis of the proposed rule assuming that each quadrotor has a single-integrator dynamics on the formation and tracking level. Then we integrate the formation level control inputs to obtain the desired position of the quadrotors and design the low level individual quadrotor controllers to track these positions. Since the output of each decentralized swarm controller is a desired location for a member, the quadrotors are required to have position controllers in addition to attitude controllers. Both simulation and real world experiments show that a group of quadrotor agents successfully achieves desired formation, rotation, and tracking predefined way-points without any collisions.
Image Recognition and Positioning Control for Cable-Suspended Parallel Robots

The main goals of this paper are focused on the implement visual feedback controller for cable-suspended robot (CSR), especially for pick-and-place operation. This study will propose a novel algorithm which is combined CSR coordinate transformation with image recognition to manipulate the suspended gripper to the desire position for assigned material handling tasks. Two webcams are introduced to sense the orientation of the end-effector, one is calculated the horizontal planar coordinate of the target, and the other is measured the vertical height for the target. Accordingly, the two-dimensional spatial image can be converted into 3-D image coordinates by using such approach. Based on the sensing information, the robots can convert the target position to the required cable lengths and then driving the servomotor to retract (or release) the cable lengths to manipulate the suspended gripper to the desired position. Several experiments will be carried out to verify the proposed methodology. The widespread variety of themes that are deliberated in this study can be applied to cranes, aerostats, and others large-scale manufacturing usages that need cable-driven robots.
Placement of Distributed Generation in Radial Distribution System by Considering Uncertainty of Load Demand

The scope of the present study is to state the probabilistic load flow (PLF) for the distribution system. The PLF is extremely important in case of uncertain input variables. In real time the electric load demand and output power of wind and solar operated DGs are uncertain parameters. The uncertain input parameters give rise to uncertain output. To handle uncertain parameters the PLF provides accurate results while deterministic load flow (DLF) is not capable of providing satisfactory results. Therefore, the present paper aims to identify the buses for placement of Distributed Generation (DG) in the distribution by evaluating loss sensitivity factor with the help of PLF by considering the uncertainty of electric load demand. In the present paper, the electric load demand is considered as a normal probability distribution function (PDF). The three-point estimate method (PEM) based method is applied to get the solution of optimal probabilistic load flow (OPLF). The candidate buses are determined for placement of DG unit in distribution by PLF.
The Influence of the Phase Angle Between the Rotor Magnetic Axis and the Stator Winding Current Vector on the Synchronous Reluctance Motor Efficiency

Modern transport technologies require the use of highly efficient AC motors. One of these machines are synchronous reluctance motors (SRM). When designing a SRM, it is necessary to determine the main parameters with reliable accuracy. The article presents a refined equivalent circuit of a synchronous reluctance motor, which allows calculating the motor torque and stator current with high accuracy, a method for determining the longitudinal and transverse resistance when solving the problem using the finite element method is proposed. To evaluate the proposed methods, the article provides a comparative analysis of the calculated design parameters of the SRM and experimental results.
Relative Gain Array and Singular Value Analysis to Improve the Control in a Biomass Pyrolysis Process

The pairing of actuators and controlled variables for process control is non-trivial due to couplings among process quantities and further disturbing effects. Here, we consider the product gas stage in a biomass pyrolysis process. Based on a dynamic model of the latter, several analyses, namely Relative Gain Array (RGA), Dynamic Relative Gain Array (DRGA) and Singular Value Analysis (SVA) are performed to solve the pairing problem. In particular, we emphasize the practical benefit from DRGA’s results by showing that challenges, like non-measurable disturbances or time delays, can be mitigated by simple changes in an implemented control concept.
A Distributed Optimization Method with Unknown Cost Function in Multi-Agent Systems via a Learning-Based Method

This paper presents a distributed optimization algorithm to solve the unconstrained (not necessarily convex) optimization problems in a multi-agent system. The algorithm does not require any explicit expressions on the local cost functions, which means gradient information is not available. The only required information is the local measurements of the local cost function from each agent. The idea of the algorithm is to search for a better optimal point until no better points can be found. Each step-update is based on an estimation of the distance between the possible better points from the current point. The estimation is generated from a learning model, which is trained by the historical data of the step-updates. This algorithm does not require any assumptions on the convexity and differentiability of the local and/or global functions except that the solution exists and is finite. Convergence property of the proposed algorithm is carefully studied. Based on the idea of exploitation and exploration, it is shown that the algorithm is able to escape from the local optimum to find the best known optimum. A numerical example is provided to verify the performance of the proposed algorithm.
Aeolus – 1 Year After Launch

ESA deployed the first Doppler Wind lidar in space within its Earth Explorer Mission Aeolus. The objective of Aeolus is to provide tropospheric and lower stratospheric wind profiles globally for the improvement of weather forecasts on short and medium term. Spin-off products are profiles of atmospheric backscatter and extinction coefficients and lidar ratio that can input for air quality models. After the successful launch in late August 2018 and following commissioning phase, the Calibration and Validation phase started in Decemeber 2018. The talk will recap the highlights of these phases and present the status of the mission.
A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Due to the significant use of Arabic language in social media networks, the demand for Arabic sentiment analysis has increased rapidly. Although, numerous sentiment analysis techniques enable people to obtain valuable insights from the opinions shared on social media. However, these techniques are still in their infancy, and the Arabic sentiment analysis domain lacks a compressive survey. Therefore, this study focused on the various characteristics, State-of-the-Art, and the level of sentiment analysis along with the natural language processing applied in the Arabic sentiment analysis. Furthermore, this study also discussed the sentiment analysis of the modern standards and the dialects of Arabic languages along with various machine learning processes and a few popular algorithms. Moreover, this study adds values by critical analysis of two case studies, which displayed an extensive set of the various research communities in this field of sentiment analysis. Finally, open research challenges are investigated, with a focus on the shortage of lexicons; availability; use of Dialect Arabic (DA); lack of corpora and datasets; right to left reading and compound phrases and idioms.
On the Error Performance of Relay-Assisted Vehicular Communication Systems

The paper investigates a dual-hop relay-assisted vehicle-to-vehicle communication system where the fading effects in wireless channels are assumed to have double Rayleigh fading distribution, which is worse than Rayleigh fading. In the considered system, source and destination nodes are equipped with single antenna while the relay has multiple antennas with employing maximum ratio combining method to obtain the maximum signal-to-noise-ratio at the relay. In the analysis of the system, bit error probability is obtained for decode-and-forward and amplify-and-forward relaying protocols. The analytical results are validated by Monte Carlo simulations providing the performance comparison for both relaying protocols.
Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images

The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications such as the interpretation of natural and man-made processes on the Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial Vehicle (UAV) images. The training phase of the CSVM is based on convolutional layers that learn the kernel weights via a set of linear Support Vector Machines (SVMs). This paper proposes the Multi-scale Convolutional Support Vector Machine (MCSVM) network, that is an ensemble of CSVM classifiers which process patches of different spatial sizes and can deal with multi-class classification problems. The experiments are carried out on the EuroSAT Sentinel-2 dataset and the results are compared to the one obtained with recent transfer learning approaches based on pre-trained Convolutional Neural Networks (CNNs).
Regression Models between Active Sensor-Measured NDVI and UAV-Acquired Multispectral Images with Positioning Uncertainty

Nowadays, it is frequent to monitor large crop areas using high-precision active sensors for measuring NDVI or multispectral cameras mounted on UAVs. However, the NDVI calculations using multispectral images differ from the readings of an active sensor for the same object or surface. What is more, there is a difference between the NDVI values of two multispectral images taken with different lighting conditions or height over the same object. In this paper, we propose new models to estimate NDVI using aerial images from a multispectral camera with values comparable to those of a portable active sensor. For this, we propose a methodology where three lambertian reflection surfaces are chosen and characterized with a hyperspectral camera. These surfaces appear in the aerial images and are used as control points of three NDVI values in the range of 0 to 1. Then, a linear model and an exponential model, derived from the original NDVI calculation expression, are proposed and evaluated, using the spectral information of the pixels inside a region equivalent to the active sensor reflection zone and the NDVI measurements of the same sensor. After the conditioning of the data, the parameters of the models are obtained by calculating the minimum squared errors. In addition, we have done a set of tests to verify the variations of the parameters versus the positioning uncertainty, different lighting conditions and different heights in the range of 15 to 40 m. The results show that the parameters of the two proposed models vary with the height, maintaining the absolute differences of NDVI close to 0.01, which is equivalent to the resolution of the active sensor; the smallest differences occur for the linear model in the interpolation interval, while the exponential model has a high accuracy near the upper limit of NDVI.