Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation Of Cnossos-EU Urban Traffic Noise Model

The results of vehicle traffic measurements were used to simulate the equivalent sound level of urban noise. The Cnossos-EU model was used for this purpose. The traffic flow was measured over the period between January and December 2013. The measurements were documented at one-hour intervals throughout the entire 24 hours of the day (1:00-24:00). The results of noise simulations were compared with the equivalent sound level calculated by permanent automatic sound and traffic volume monitoring station. The results obtained for the data recorded on working days were compared. The dispersion of the equivalent sound level was described using the positional measures. The analysis consisted of the Jarque-Bera and the Shapiro-Wilk tests for the recorded and calculated data were conducted. The results indicated that in almost all cases, the distribution of the tested variable was not normal. Box plots were used to assess whether outliers data occurred in the recorded results. The analysis of the recorded and calculated data was performed for three time sub-intervals of a 24-hour interval: day, evening, and night.
Information Search as Adaptive Interaction

We use information retrieval (IR) systems to meet a broad range of information needs, from simple ones involving day-to-day decisions to complex and imprecise information needs that cannot be easily formulated as a question. In consideration of these diverse goals, search activities are commonly divided into two broad categories: lookup and exploratory. Lookup searches begin with precise search goals and end soon after reaching of the target, while exploratory searches center on learning or investigation activities with imprecise search goals. Although exploration is a prominent life activity, it is naturally challenging for users because they lack domain knowledge; at the same time, information needs are broad, complex, and subject to constant change. It is also rather difficult for IR systems to offer support for exploratory searches, not least because of the complex information needs and dynamic nature of the user. It is hard also to conceptualize exploration distinctly. In consequence, most of the popular IR systems are targeted at lookup searches only. There is a clear need for better IR systems that support a wide range of search activities. The primary objective for this thesis is to enable the design of IR systems that support exploratory and lookup searches equally well. I approached this problem by modeling information search as a rational adaptation of interactions, which aids in clear conceptualization of exploratory and lookup searches. In work building on an existing framework for examination of adaptive interaction, it is assumed that three main factors influence how we interact with search systems: the ecological structure of the environ-
Bending Magnetic-Levitation Control for Flexible Steel Plate (Fundamental Consideration on Disturbance Cancellation Control under Disturbance Conditions)

In a steel plate factory, conveyance is performed while the steel plate is in contact with a roller, and the quality deterioration of the steel plate surface is a problem. Therefore, to perform noncontact conveyance of a thin steel plate using the attraction force of electromagnets, research was conducted on the levitation stability of thin steel plates. Previously, a bending magnetic-levitation system capable of levitating using a bending thin steel plate was proposed. It was confirmed that the levitating stability is improved by bending levitation. In this report, a bending magnetic-levitation system was constructed that can input disturbances to simulate an actual usage environment, and magnetic-levitation experiments on a thin steel plate were conducted using disturbance cancellation control. The levitation target was a rectangular galvanized steel sheet (SS400) with a length of 800 mm, a width of 600 mm, and a thickness of 0.19 mm.
Bag of visual word model based on binary hashing and space pyramid

Constructing visual vocabularies in the bag of visual word (BoVW) model is a critical step, most visual vocabularies is generated either by the k-means algorithm or its improved algorithm. Visual vocabularies generated by these methods have the problem of low discriminative and long running time. For these problems, a BoVW model is proposed based on binary hashing and space pyramid. Firstly, extract the local feature points from the images. Second, learn binary hashing functions, which map the local feature points into visual words, and filter the visual words and generate the visual vocabularies whose visual word is binary hash code. Third, Combined with spatial pyramid matching model, the new BoVW model represents the image by the histogram vector of space pyramid. Finally, the BoVW model is used in image classification and retrieval to verify the effectiveness of the model. Experimental results on the common datasets show that visual vocabularies in our model has higher discriminative and expression ability. Compared with other methods, our model has higher classification accuracy and better retrieval performance.
Waveform Design for High-Resolution Automotive Radar

High spatial and Doppler resolutions are a fundamental building block of advanced automotive radar systems that will aid autonomous driving. High spatial and Doppler resolutions provide the level of detail in extracted information enabling reliability of interpretation to aid decision making for autonomous driving. This paper considers the key parameters that enable high resolution and goes on to develop a more systematic approach to vehicular waveform design. Trade-offs between radar performance and selected parameter values are examined to show the impact of key relationships.
Spatio-Temporal Broad Learning Networks for Traffic Speed Prediction *

Since more and more vehicles are used, traffic speed prediction is of great importance to ensure public safety and improve traffic management. However, it is difficult to provide an accurate traffic speed prediction since the traffic is affected by many complex factors, in which the most challenges are internal sequential correlations, spatial dependencies and external factors such like weather and holidays. To solve the problem, we propose a broad learning based method, named spatio-temporal broad learning networks (ST-BLN) to forecast the traffic speed. First, to deal with temporal dependencies and spatial dependencies of traffic speed, spatio-temporal region of support (ST-ROS) is defined and identified using historical data. More specifically, for spatial dependencies, we propose a method of slide-window correlation coefficient to find related road segments with the current one. And for temporal dependencies, we obtain temporal region of support by analyzing the cumulative distribution of correlation coefficient. Second, we extract slow features of traffic speed as feature nodes of ST-BLN which can carry major information provided by raw speed. Further, the feature nodes are combined with external factors to generate enhancement nodes. In order to prevent overfitting and eliminate the influence of noncritical information, we simultaneously add L1 regularization and L2 regularization to the solution of final connecting weights. Finally, the efficacy of the proposed method is illustrated using traffic data in Hangzhou and the superiorities are verified in comparison with five counterparts.
Comparison of principles for measuring the reflectivity values from wind turbines

The radar echo of a large wind turbine (WT) is investigated in the C band: An unmanned aerial system (UAS) is used to perform a reflectivity measurement across the flight altitude using a passive bistatic radar (PBR) constellation of a non-cooperative precipitation radar transmitter in horizontal polarization. This is done at various distances to check if far field conditions apply to derive a radar cross section (RCS). As a fully independent method, a monostatic FMCW radar is installed on ground at certain distances to the WT. The results of both methods are compared against each other regarding the applicability of the RCS.
Distributed Path Planning of Swarm Mobile Robots*

This paper is mainly to solve the path planning problem of swarm robots. If swarm robots work in the same environment at the same time, it is necessary to have appropriate planning methods to enable individuals in the group to complete their tasks. The solution is to use the artificial bee colony algorithm for the problem of robot path planning. Because the artificial bee colony algorithm has a high efficiency to solve the optimization problem, it tries to solve the problem of robot path planning in a distributed manner. The concept of organizing robots achieves efficient task of information processing and group working in the environment.
OWA Operators for the Fusion of Social Networks’ Comments with Audio-Visual Content

Social networks’ comments are rich sources of information that may be fused with audio-visual contents to improve emotional video retrieval systems. The rationale behind this fusion is that different sources can complement each other since they have different natures, formats, and origins. Although emotional information expressed in users’ comments on the Web is in accordance with the emotional audio-visual content of videos, they are not synchronized. In order to address this problem, decision-level fusion is needed when such asynchronous modalities should be fused. In this article, a new decision-level fusion approach based on Ordered Weighted Averaging (OWA) operators is proposed. In this approach, emotion is first detected based on the audio, video, and users’ comments and then, individual decisions are fused using the OWA method. The proposed method is evaluated on the music videos of the standard DEAP data set. The results of comparing the proposed method with average, product, sum, and Dempster-Shafer fusion methods show that the proposed OWA-based method outperforms other methods in different fusion settings.
Development of Methods for Optimizing Reactive Power Modes Based on Neural Network Technologies

The high cost of electric power, as well as the considerable length and branching of electrical networks, necessitate reduce electric power consumption, and losses in electrical networks. One of solutions of this problem is optimizing the reactive power mode. Reducing the reactive power factor at the point of common coupling (PCC) to the economic level established by the power system is not taking into account that in a complex network, power flows with a non-optimal arrangement of compensating devices and improper determination of their power can reach large values, that resulting in an increase in losses in the network. A program has been developed that implements prediction algorithms using neural networks, as well as optimizing the reactive power mode.
MR and PET Image Fusion Using Nonparametric Bayesian Joint Dictionary Learning

Positron emission tomography (PET) and magnetic resonance (MR) images are often used in clinical diagnosis. These images offer two different interpretations of one region of a brain; PET displays the functional activity and functional connectivity of the brain, while MR provides excellent structural information of that. Fusing both information in a single image leads to better analysis, but different sensor technologies make it difficult to devise a proper fusion scheme. Over the past few years, sparse representation (SR) has achieved great success in fusing PET and MR images. Some efforts have been made to expand SR in different feature spaces. However, these methods suffer from the main drawback, which is the absence of mapping between the two feature spaces. To address this problem, a nonparametric Bayesian technique is considered to learn dictionaries and the mapping for the two feature spaces. In the proposed method, dictionaries for two feature spaces and the mapping between them are obtained from input PET and MR images adaptively. In addition, our method automatically determines the number of dictionary atoms and the sparsity level of the coefficients. The proposed method was compared with some well-known SR-based image fusion methods visually and quantitatively.
Mobile Technology as an Enabler of Innovation in Kenya’s Micro, Small and Medium Establishments

The interface between mobile phone technologies and innovation remains largely under-researched. Attempting to bridge this knowledge gap, this cross-sectional study of Kenyan enterprises examines the role of mobile phone technologies in innovation. The study applies probit estimators to a comprehensive dataset of 24, 164 micro, small and medium establishments in Kenya. The study reveals that; (1) mobile technologies are positively and significantly correlated with product and marketing innovation but are insignificantly correlated with process innovation. (2) The predicted probabilities of product, process and marketing innovation increase with the increase in the ICT index. (3) The effect of age and size on the propensity of innovation is concave: older and larger establishments tend to innovate more than relatively younger and smaller ones, but the relationships get reversed after a certain threshold. (3) The predicted probability of product, process and marketing innovation increase with increases in R&D, training, credit and foreign trade.
Flexible and Efficient Blockchain-Based ABE Scheme With Multi-Authority for Medical on Demand in Telemedicine System

Telemedicine offers a medical-on-demand (MoD) service from a distance. This technology is designed to overcome distance barriers and improve the process of accessing medical services in distant rural communities. With the development of cloud computing, the MoD services in the telemedicine system are provided by the Cloud Service Provider (CSP). This CSP connects the patient and the medical staff in different places with both convenience and fidelity. Meanwhile, the outsourcing healthcare data on public cloud platforms bring some new challenges on the security. Although attribute-based encryption (ABE) algorithm realizes flexible and fine-grained access control, a large number of patients subscribe or unsubscribe the different medical services frequently in the cloud, which takes a huge cost for membership management. In this paper, an ABE scheme is presented to achieve the dynamic authentication and authorization with higher flexibility and efficiency for the MoD services in telemedicine system. On the one hand, when the patient alters his ordered service, it requires no updating on the parameters for those whose statuses remain unchanged. We construct an independent-update key policy ABE scheme in the distributed telemedicine system that aims to updates patient’s keys separately, and there are multiple authorities to manage this system altogether which is more similar to the real situation. On the other hand, by using blockchain and distributed database technologies, the private healthcare data stored in public cloud is protected in integrity, which avoids the misdiagnosis accident from the inaccurate electronic health records distorted by a malicious user or authority from the inner cloud. Finally, we analyze the collusion attack in multiple authorities and formally prove the security of this protocol in a standard model. After comparing and simulating, the results of this work show a better performance.
Iterative Nonlinear Detection and Decoding in Multi-User Massive MIMO

In this paper, we propose a new candidates list re-calculating to improve performance of iterative nonlinear detection and decoding in Multi-User (MU) Massive Multiple Input, Multiple Output (MIMO) system. The proposed nonlinear iterative detector includes a new algorithm of users (UEs) sorting before QR decomposition (QRD) and a new sorting-reduced (SR) K-best method. If MIMO detector is based on a candidates list updates, the performance can be improved by the candidates list re-calculating or using a priori information in the list generation. This is natural, because the quality of the candidates list is likely to be improved by using the decoder output as a priori information. We analyze the convergence of combining the detection algorithms with the soft low-density parity-check (LDPC) decoder. Simulation results are presented in 5G QuaDRiGa channel with QAM64 modulation in 48 × 64 MIMO system and compared with other state-of-art approaches.
A Multi-Dimensional Resource Scheduling Strategy Based on Multilateral Complementarity

The diversification of computing resources and the increasing complexity of resource demand from applications in terms of type, granularity, and quantity call for more efficiency in resource scheduling. To meet this challenge, this paper proposes a resource description model based on a quantized polygon. It explores the theoretical basis for the multilateral complementarity strategy (MCS), analyzes the basic mechanisms and application architecture of the MCS, and, finally, proposes the multi-dimensional quantized polygon (MQP) algorithm—a multi-dimensional resource scheduling algorithm based on multilateral complementarity (MC-MDRS algorithm). The experiments show that the multi-dimensional resource scheduling strategy based on the MQP, when implemented in an environment in which multi-dimensional ( $3\leq {d}\leq6$ ) computing resources are provisioned, can effectively respond to the requests for various computing resources by granular services and facilitate the deployment of granular application services arriving in batches. The analysis of the experimental results indicates that the MQP algorithm outperforms other multi-dimensional resource scheduling algorithms by 2%–5% in node utilization.
On demand vison-based indoor localization

Many indoor localization systems are based on earth-magnetic or radio field strength fingerprinting which is considering its low computing power requirement is very well-suited method for mobile phone applications. However, certain parts of navigation areas might not cover with any radio signal which makes the localization very difficult and imprecise. In this case the mobile phone camera can be used as a temporary localization sensor. Since the mobile phone camera is usually not stereo or depth sensing camera and since the image processing capabilities of the mobile phone is limited, a specific algorithm needs to be developed. The algorithm is expected to work without using any special markers, using only existing features of the surrounding environment, recognizing them, using a database to lookup their position and using a Perspective-n-Point algorithm to determine the user's relative position. The markers can be any specific feature of the building or any logo, sign or anything visually specific to the given location. This article summarizes the development, tuning and testing of a mobile phone application using marker-less vision-based localization algorithm.
Comparative Experiments of Moving Horizon Estimation based SLAM in Indoor Environment

In this paper, we describe accuracy verification of simultaneous localization and mapping (SLAM) in a crowded area where measured data often becomes insufficient. To realize autonomous locomotion of robots in an unknown environment, they need to create an environment map while estimating self-pose with measured data. When the robot works in an area where many people exist, however, the measured data is frequently occluded by them and become insufficient to achieve the SLAM. Applying moving horizon estimation to SLAM, when the measured data is insufficient, the evaluation of the motion model becomes more dominant than that of the matching error of the measured data. To verify the effectiveness of SLAM based on MHE, we conduct experiments in the crowded area to compare the result with that of SLAM based on extended Kalman filter. Also, we applied sequential quadratic programming to the optimization to reduce the computational complexity of the proposed method. It was confirmed that the computational complexity was reduced by about 88 % compared to the trust region method.
EKF for Wind Speed Estimation and Sensor Fault Detection Using Pitot Tube Measurements

The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS) measurements. For this purpose, Extended Kalman Filter (EKF) was designed, and as state variables, the wind velocity components and ADS pitot scale factor are considered. A sensor fault detection algorithm based on EKF innovation process was developed. The results were obtained for noise increment and bias conditions. Estimation errors, normalized innovations and fault detection statistics were obtained.
Framework-supported mechanism of testing algorithms for assessing memory and detecting disorientation from IoT sensors

Assessing memory in daily life can be useful for both early detection and tracking the symptoms of neurodegenerative disease such as Alzheimer’s disease. One of these symptoms is disorientation, which can cause domestic accidents. With the avenue of Internet of Things (IoT), houses can collect big data in real-time for being processed and acquiring knowledge between both edge computing (local processing for avoiding communication overload) and the cloud computing conforming the fog computing. What sensors are needed and how to collect the information is something that requires actually preparing prototypes of this setup in a lab. As an alternative, this work proposes a cheaper method which is based on the use of realistic 3D simulations of the scenarios to consider. Using these scenarios, different algorithms for assessing memory can be compared and evaluate how well they detect disorientation of a person living in a house by means of tracking the movement of a person. The contribution of this paper is a case study where researchers can design the scenarios, including the activity of the daily living of the individuals, the array of sensors to be deployed, and the disorientation detection algorithms.
A Novel Trajectory Planning Method for Automated Vehicles Under Parameter Decision Framework

Decision and control in all stack scenarios comprise a key issue in the design of automated vehicle control systems. Thus, in higher level, automated vehicles, the decision and the form of the decision should be able to adapt to diverse, changeable, and complex scenarios, which increase the complexity of trajectory planning. In this paper, a parameter decision framework in which the decision is described with key parameters, rather than specific behaviors, such as lane-changing or car-following, is considered. Under this framework, a novel trajectory planning method is proposed to implement behavior with integrated longitudinal and lateral control, in which a nonlinear motion control model is established. The nonlinear model predictive control (NMPC) method with terminal constraints without a predefined path form is applied, which presents more flexibility for changeable decisions. Both the trajectory planning controller and the overall framework are verified by simulation. The results show the validity of the controller and the framework.
Phasor Data Concentrator Functionalities to Support a Multi-Agent Control System in Active Distribution Networks

One of the goals of the Smart Grid paradigm is to fulfil consumers' demands and, at the same time, avoiding network contingencies and infrastructure overloads. In this scenario, the management of active demand based on Multi-Agent System (MAS), thanks to its flexibility and autonomy, could allow increasing the energy efficiency and reducing the cost of energy. Nevertheless, the Agents can define an effective strategy only if they are supported by a suitable measurement system providing accurate information about the state of the network (e.g. voltage profile). In this context, the paper presents a management proposal exploiting the synergy between the functionalities implemented in the Phasor Data Concentrator (PDC) specific for distribution grids and the MAS strategy to meet the load needs and to improve the voltage control in modern distribution networks.