Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Time-Updating Sparse Covariance-Based Spectral Estimation

Recently, the time-updating <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-norm sparse covariance-based estimator (<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-SPICE) was developed for online spectral estimation of stationary signals. In this work, this development is furthered to deal with non-stationary signals. By introducing a weighting matrix defined by a forgetting factor, the generalized least absolute shrinkage and selection operator (LASSO) is generalized, in order to allow for changes in the spectral content. As shown here, the resulting LASSO formulation can be solved in a simple manner using cyclic minimization, enabling recursive estimation for non-stationary signals. The proposed generalized time-updating <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-SPICE offers the same benefits as the original estimator, including being computationally efficient at constant computational and storage cost, but also allows for substantial improvements when dealing with non-stationary signals. The performance of the method is evaluated using both stationary and non-stationary signals, indicating the preferable performance of the generalized formulation as compared to the original time-updating SPICE algorithm.
True Random Generators and Hidden Transfer of Keys

Most industrial automatic systems are remotely controlled, so the problem of protecting control information transmitted through an open channel is relevant. The use of cryptographic systems for security involves the ability to fast exchange system keys. In this paper, a new family of the true random key generators is proposed. The source of entropy for such random devices is the phenomenon of jittering in electronic circuits. Generators are built in the form of cascade schemes based on adders. We give the architectural solution for the synthesis of the generator that based on transition dynamics of such an appliance. The procedure for the hidden transfer of keys using those generators is discussed.
A 5Gb/s $2\times 2$ MIMO Real-time Visible Light Communication System based on silicon substrate LEDs

In this paper, we propose and experimentally demonstrate a high-speed $2\times 2$ MIMO real-time VLC system based on 64QAM-Discrete Multi-Tone (64QAM-DMT) modulation. The system was achieved by utilizing field programmable gate arrays (FPGAs) and silicon substrate LEDs. The effect of attenuation of the attenuator in transmitter and receiver, the aperture diameter of the received signal, code rate and different modulation order have been investigated in our experiment. The experimental results show that our real-time VLC system can transmit over 2m distance for an overall 5Gb/s successfully.
Control Systems with Limitations on Controls and their Derivatives

The paper deals with control problems for linear systems with taking into account control resources limitations. The use of modal synthesis under given restriction is not possible. The class of systems for which the stabilization problem (tracking) with respect to output variables has a solution is extended. Control inputs are considered in the form of discontinuous functions or linear functions with saturation. Both cases ensure compliance with the restrictions on controls, and in the second case also restrictions on their derivatives. The problem of dynamic compensation of disturbances of a given class is solved. The problem of output variables stabilizing is also solved as application to non-minimal phase linear systems. The basis of the developed algorithms is the block principle in the control synthesis.
Blockchain Based Traceability System in Food Supply Chain

Traceability system is a system that is able to record product movements along the supply chain and trace them back. In the food supply chain, traceability systems are considered important to ensure the safety of a product. There are two data architectures commonly used in traceability systems namely one-step-forward and one-step-back and aggregated information model architecture. In one-stepforward and one-step-back architectures, tracing process takes a long time and does not guarantee the integrity of the data. In aggregated architecture, data is integrated but rely on third parties as data managers. This research explores the possibility of another type of architecture, which is the blockchain based system. Blockchain based system allows an integrated system to occur without third party by distributing all data to each member of the supply chain involved. Exploration is done by making the FoodTrail Blockchain design using four blockchain system abstraction layers. FoodTrail Blockchain records and tracks the transfer and transformation of food products in the supply chain. Transfer and transformation are stored in the blockchain and can be traced using depth first search algorithm. The design was then implemented in a prototype using the Hyperledger Sawtooth framework. The prototype that are built are evaluated based on aspects that must be considered in the traceability system (breadth, depth, precision, and access) and blockchain (distributed, verified, and immutable). The evaluation shows that FoodTrail Blockchain fulfilled the distributed, verified, and immutable aspect. The system has low performance in transaction handling related to breadth and depth aspect due to limited server capacity and complicated process. However the system has advantages related to access and precision aspect, because all transaction is verified, immutable, and stored locally in every node.
Development and Experiments of the Passive Buoyancy Balance System for Sea-Whale 2000 AUV

The Sea-Whale 2000 Autonomous Underwater Vehicle (Sea-Whale 2000 AUV), developed by the Shenyang Institute of Automation, CAS, is an instrument platform designed to glide to depths of 2000 meters for the observation of environmental parameters of deep sea environment, which endurance can reach 1500 kilometers at the speed of 0.5m/s. In order to ensure that the platform can keep neutral buoyancy under different depth and reduce power consumption as much as possible, we design a set of passive buoyancy balance system. Through the bench and sea experiment, the passive buoyancy balance system can reliable applied to the Sea-Whale 2000 AUV system.
Multitemporal InSAR Coherence Change Analysis: Application to Volcanic Eruption Monitoring

Interferometric Synthetic Aperture Radar (InSAR) coherence time series provides potential information used for temporal change monitoring of regions of interest on the Earth surface. This paper addresses the change analysis of multitemporal InSAR coherence images for volcanic eruption monitoring using Change Detection Matrix (CDM) approach. The Piton de la Fournaise volcano (French island La Réunion) with four eruptions in 2018 was selected as a case study. From InSAR image time series (ITS) included 28 Sentinel-1 images acquired during 2018 over the study area, sorted by acquisition date, 27 coherence images were computed between each two adjacent dates. Changes on the ground related to volcanic eruptions were analyzed through the obtained coherence ITS using CDM framework. The preliminary results on lava extent mapping have demonstrated the relevancy of this study.
What could the baseline measurements predict about decision-making performance in serious games set in the financial context

This paper sets out to investigate how the basal activation of the parasympathetic and sympathetic nervous systems may affect and predict the decision-making performance of players in serious games. In order to investigate the basal activation of both branches of the autonomic nervous system, pupil diameter and heart rate were recorded during baseline and analyzed in regards to performance scores in the serious game. It was found that the balance between the parasympathetic and sympathetic activation was responsible for beneficial decision-making performance, while lower sympathetic activation was found to be associated with the higher level reached in the game. It is suggested that the balance between the basal activation of both branches of the autonomic nervous system recorded during the baseline may predict the decision-making performance of players on the subsequent tasks in serious games.
Research on Movement Performance of a Vector Propulsion Autonomous Underwater Vehicle

The vector propulsion device can increase the maneuverability, stability and anti-disturbance capability of underwater vehicles under any operating condition. This paper presents design and optimization of a vector propulsion Autonomous Underwater Vehicle (AUV). we have discussed the influence of two primary shape parameters about the AUV and deduced the resistance of the translational degrees of freedom based on Computational Fluid Dynamics (CFD) simulation. Movement control principles and the system composing of the AUV were proposed. The variation features of the thrust and the hydrodynamic characteristics were simulated under different sailing velocity and different propeller rotational speeds. Then, focusing on the mutual influence between the thrusters and the forward speed, thrust performance of the vector thrusters was analyzed. Simulation results show that the performance of the vector thrusters has a strong dependence on the both the main thrusters and the longitudinal speed.
Doppler Difference Aided Long Baseline Navigation for a Fast-Moving Underwater Vehicle

Underwater navigation that relies solely on dead reckoning suffers from unbounded position error growth. In traditional positioning method based on TDOA, usual interference from multipath reflections and background noise lead to the choice of some fault correlation peaks and low SNR. In the paper, the combination of LBL and Doppler difference of an AUV will be expected to enable survey accuracy at any water depth where the position of an underwater acoustic transmitter equipped on the moving platform can be determined. And based on pattern time delay shift coding pulse, frequency estimation of Doppler shift in LBL method could be described with Doppler effects from AUV moving. Thus, with the intersection of two contours of Doppler difference, the positioning solution of AUV and the relative trial results could be finished and verified.
Managing Industrial Communication Delays with Software-Defined Networking

Recent technological advances have fostered the development of complex industrial cyber-physical systems with communication delay requirements. The consequences of delay requirement violation in such systems may become increasingly severe. In this paper, we propose a contract-based fault-resilient methodology which aims at managing the communication delays of network flows in industries. With this objective, we present a lightweight mechanism to estimate end-to-end communication delays in the network where the clocks of the switches are not synchronized. The mechanism aims at providing high level of accuracy with little communication overhead. We then propose a contract-based framework using software-defined networking (SDN) where the components are associated with delay contracts and a resilience manager. The proposed resilience management framework contains: (1) contracts which state requirements about components' behaviors, (2) observers which are responsible to detect contract failure (fault), (3) monitors to detect events such as run-time changes in the delay requirements and link failure, (4) control logic to take suitable decisions based on the type of the fault, (5) resilience manager to decide response strategies containing the best course of action as per the control logic decision. Finally, we present a delay-aware path finding algorithm which is used to route/reroute the network flows to provide resilience in the case of faults and, to adapt to the changes in the network state. Performance of the proposed framework is evaluated with the Ryu SDN controller and Mininet network emulator.
Model-Based Controller Using Quasi-Velocities for Some Vehicles

This paper deals with the problem of trajectory tracking control for some vehicles (underwater vehicles, and indoor airships). The approach uses a velocity transformation which results from the inertia matrix decomposition. Next, a model-based non-adaptive nonlinear tracking controller in terms of the Generalized Velocity Components (GVC) is proposed. An important property of the algorithm is that the control gains are closely related to the dynamics of the vehicle (especially dynamical couplings). The general algorithm was given for a 6 DOF vehicle and tested using simulation. The results obtained for a full airship model have shown that the control scheme guarantees satisfactory performance.
IVAA: Intelligent Vehicle Accident Analysis System

Intelligent Vehicle Accident Analysis (IVAA) system provides an artificial intelligence as a service (AIaaS) for building a system that can automatically evaluate vehicle parts' damage from the accident. Particularly, IVAA can recognize the damaged vehicle part and the severity level facilitating the insurance company's claiming process. There are four main elements in the system which support four stakeholders in an insurance company: insurance experts, data scientists, operators and field employees. Insurance experts utilize the data labeling tool to label damaged parts of a vehicle in a given image as a training data building process. Data scientists iterate to the deep learning model building process for continuous model update. Operators monitor the visualization system for daily statistics related to the number of accidents based on locations. Field employees use LINE Official integration to take a photo the damaged on the vehicle at the accident site and retrieve the repair estimation. IVAA is built on the docker image which can scale-in or scale-out the system depend on utilization efficiently. We deploy the Faster Region-based convolutional neural network, along with residual Inception network to localize the damage region and classify into 5 damage levels for a vehicle. The accuracy of the localization is 93.28 percents and the accuracy of the classification is 98.47 percents.
Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network

Object detection and classification in the water enhances not only the application of the autonomous underwater vehicle(AUV) but also localization of the AUV. Object detection and classification using sonar images are challenging problems due to low resolution and low signal-to-noise ratio. In this paper, we propose shape classification method using multi-view sonar images for AUV. To train multi-view of sonar images, we used network which is connected in parallel with convolutional neural network(CNN). We used Alex-net for the basic CNN model. The extracted features by the CNN are collected through the pooling layer and connected to the fully connected layer to classify the shape. To overcome the lack of training data, sonar simulator was used to generate data set. As a result, 6 shape are well classified and also shows possibility for the recognition of the real sonar images acquired in water tank.
Cerebrospinal fluid pulse pressure and craniospinal dynamics : a theoretical, clinical and experimental study

textabstractSince the introduction of continuous recording of intracranial pressure (ICP) in neurosurgical practice (Guillaume and Janny, 1951; Lundberg, 1960) this method has greatly contributed to clinical research in the field of intracranial hypertension. Numerous publications have enriched the literature on ICP and five International Symposia (1972, 1974, 1976, 1979 and 1982) on the subject have been held. The knowledge gained from these investigations has considerably benefited the management of patients suffering from raised ICP. However, in spite of the enthousiasm among those who practise ICP monitoring, the method has not yet gained universal acceptance as a routine procedure with regard to the various aspects of care of the individual patient: diagnosis, surveillance, treatment and prognosis. The question of the clinical value of ICP monitoring is, of course, closely related to the question as to what extent ICP truly reflects central nervous system function. In our view the answer to this question must be sought somewhere between the two extremes expressed in the statements of Stern (1963) "that ICP should be given a subsidiary role in the assessment of the fluid-mass dynamics in neurological disease" and of Langfitt (1969) "that uncontrolled, increased ICP is one of the most common causes of neurological death". This dilemma is clinically illustrated by the common experience that high levels of ICP may be sustained by normal subjects without evidence of neurological impairment (Evans et al., 1951), whereas patients with large space-occupying lesions may have clinical signs of brainstem distortion without significant elevation of ICP. Much of this controversy results from the two-fold meaning of increased ICP. On the one hand it may be regarded as a symptom indicating the presence of a space-occupying process, of whatever nature, which can no longer be compensated for. On the other hand intracranial hypertension may be a pathogenetic factor causing brain dysfunction per se. The latter aspect in particular is still controversial; for instance, it is not yet known for certain which level of ICP is so critical that it requires treatment. The interaction between ICP and brain function is apparently so complex that no simple deductions can be made from the level of ICP alone. It is therefore necessary to consider many other aspects of ICP that 'matter' (Jennett, 1976) such as: the cause and rate of development of intracranial hypertension, the presence of pressure waves, brain shift, and of pressure gradients, the cerebral perfusion pressure, and the craniospinal volume-pressure relationships
Parametric Synthesis of Modal Control with Output Feedback for Descent Module Attitude Stabilization

The authors offer a new approach to synthesize output feedback modal control basing on zeroing the columns of state feedback controller analytic matrix corresponding to unmeasured components of state vector. This approach is used to synthesize the independent on stochastic parameters of aerodynamics and the Earth's atmosphere analytic controller of descent module attitude stabilization during its movement in Earth's atmosphere. Further, the authors compare the algorithm obtained to regular onboard algorithm currently applied on a descent module of transport manned spacecraft “Soyuz”. The comparative analysis showed that the new algorithm allows improving descent module stabilization precision without increasing the jet engines fuel rate.
Pre-Trained Bidirectional Temporal Representation for Crowd Flows Prediction in Regular Region

Deep learning-based methods for predicting spatial-temporal data such as crowd flows need to consider both spatial dependency and temporal dependency. Previous research mainly focused on modeling spatial dependency, whereas studies on temporal dependency are few. Existing finite deep learning-based methods for temporal dependency modeling can be divided into RNN-based methods and domain knowledge-based methods. However, RNN-based methods are hard to learn very long-term temporal dependency, and domain knowledge-based methods cannot model temporal dependency automatically, depending on data pre-processing based on prior knowledge. In view of the problem, crowd flows prediction in regular gridded regions are studied and a model called Pre-trained Bidirectional Temporal Representation (PBTR) based on Transformer encoder is proposed capable of modeling very long-term temporal dependency automatically. PBTR is simple, scalable, and can be combined with any other spatial component. Furthermore, we introduce Crowd Flows Prediction based on PBTR (CPPBTR) to form a Transformer based encoder-decoder framework. There are two decode stages in the proposed model. At decoder-stage 1, ‘draft’ sequence is generated. At decoder-stage 2, each timestep of the ‘draft’ sequence is masked and fed into PBTR to predict the refined flow for each masked position. Experiment results demonstrate that our method outperforms RNN-based methods and domain knowledge-based methods.
An FSS Based Multiband MIMO System Incorporating 3D Antennas for WLAN/WiMAX/5G Cellular and 5G Wi-Fi Applications

A compact 3-dimensional dual-element, multiband antenna for Multi-Input-Multi-Output (MIMO) applications is proposed in this paper. Folding technique along with various impedance matching structures are employed for miniaturization and multiband operation while maintaining the desired S-parameter performance. A grid of Frequency Selective Surface (FSS) patch based decoupling structure is introduced to achieve measured isolation of more than 30 dB between antenna ports. In addition to this decoupling structure, a meandered line based defected ground plane configuration helps to retain the multiband response while maintaining the optimum isolation performance. This 3D-MIMO antenna is fabricated and measured for its performance in different bands of WLAN, WiMAX, 5G cellular and 5G Wi-Fi along with different MIMO performance parameters. The reported design is a suitable option for multiband MIMO applications for future wireless technologies.
Optimization Techniques for the Design of Identification Procedures for the Electro-Chemical Dynamics of High-Temperature Fuel Cells

This paper presents a methodology to design an optimal open-loop control sequence for the electro-chemical subsystem of a high-temperature fuel cell. This control input helps to improve the parameter identifiability and simultaneously acts on both a nominal and disturbed system model. For this purpose, a quadratic distance measure between the respective output variables is maximized with the help of Pontryagin's Maximum Principle. To compute the trajectory for the manipulated variable in terms of a reasonably smooth (respectively, continuous) electric current signal, the system is augmented by an additional state, which represents a linear low-pass filter for the physical input. Furthermore, the parameters of the dynamic system under consideration can then be identified reliably with the help of this optimal control signal. Due to nonlinear couplings between some of the system parameters, the subsequent identification is performed by a two-stage procedure. As a result, the identified parameters reconstruct the nominal system dynamics with high accuracy, while the advantages of this methodological approach are verified in comparison with naive heuristics.
Virtual Assistant Using Lstm Networks In Indonesian

Researches into the development of virtual assistants that are human-like today were an active research that has been developed over the past few decades. Currently the generative model virtual assistant is a few steps towards artificial general intelligence, the smart computer with complex feelings and characteristics. One method for building the generative model virtual assistant is by using the LSTM Networks. This method has been popular for building English virtual assistant. Our aim is to test whether this method could be applied in Indonesian or not. We used the dataset from many movie subtitles in Indonesian languages. The LSTM variant that we used was the sequence-to-sequence model embedded with word2vec. The results show that the model could answer appropriately to the simple questions such as greetings. Although the models were failed to answer some complex questions, the results still give potential works on the future.