Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Energy Model for a Differential Guide Mobile Robot

Energy consumption is an important element in the autonomy of mobile robots, in any application in which the robot carries a source of finite energy. The formulation of an energy model can relate the kinematic movements of mobile robots and energy consumption. In this paper, an energy model is proposed, which takes into account the kinematic and dynamic models for a differential guide mobile robot as well as the motors, given an energy value close to the real energy consumption. The mixed energy model is tested with a typical motor energy model, and a trapezoidal velocity profile as input. The energy consumption values obtained by the mixed energy model is higher than the motor energy model, specially when the robot is in the acceleration phase. The mobile and motor parameters of the P3-DX robot are used to configure both energy models.
CrowdMeter: Congestion Level Estimation in Railway Stations Using Smartphones

We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high). Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.
Attack Detection and Identification for Automatic Generation Control Systems

Integrating today's power systems with communication infrastructure makes them vulnerable to cyber-attacks, which can disrupt their normal operation undetectable. Automatic generation control (AGC) is one of the vulnerable controllers in power grids, since it greatly depends on communication systems. This paper first shows that false data injection attacks (FDIAs) against an AGC system can be carried out stealthily with destructive outcomes. Then, it proposes an anomaly based attack detection and identification method for protecting the AGC system against cyber vulnerabilities. To detect attacks, the proposed method estimates the load frequency control system's states using an unknown input observer (UIO), and calculates the UIO's residual function. A discrepancy between the residual functions and a predefined threshold signifies an FDIA. Different identification UIOs are then used to determine the attack type, i.e., which system parameter(s) is (are) targeted by the attack. The effectiveness of the proposed method is corroborated using simulation results for a three-area power system and the IEEE 39-bus network.
Enhancing Open Data Knowledge by Extracting Tabular Data from Text Images

Open data published by public institutions are one of the most important resources available online. Using this public information, decision makers can improve the lives of citizens. Unfortunately, most of the times these open data are published as files, some of them not being easily processable such as scanned pdf files. In this paper we present an algorithm which enhances nowadays knowledge by extracting tabular data from scanned pdf documents in an efficient way. The proposed workflow consists of several distinct steps: first the pdf documents are converted into images, subsequently images are preprocessed using specific processing techniques. The final steps imply running an adaptive binarization of the images, recognizing the structure of the tables, applying Optical Character Recognition (OCR) on each cell of the detected tables and exporting them as csv. After testing the proposed method on several low quality scanned pdf documents, it turned out that our methodology performs alike dedicated OCR paid software and we have integrated this algorithm as a service in our platform that converts open data in Linked Open Data.
Investigation for Improved Power Quality in Hysteresis Current Controlled Three-Phase Four-Wire Vienna Rectifier Topologies

Improved power quality ac-dc converters (IPQCs) have been developing to provide power transfer in conformity with the ever evolving power quality standards. These include single- and three-phase unidirectional and bidirectional buck, boost, buck-boost, multilevel, and multipulse topologies. The Vienna rectifier is a unidirectional boost type IPQC that has both single- and three-phase topologies. Like most boost type IPQCs, the Vienna rectifier is amenable to input current wave shaping techniques. In this paper, the efficacy of hysteresis current control (BCC) technique to confer improved power quality in two three-phase, four-wire Vienna rectifier topologies is assessed. MATLAB simulations of the topologies with BCC have been conducted and relevant data have been recorded. The simulation results, indicating compliance of the various parameters on the ac and dc sides with the current power quality norms, are presented.
Human Activity Recognition with Convolution Neural Network Using TIAGo Robot

This paper presents a two layer convolutional neural network for performing activity recognition. We combine spatial and temporal information extracted from images acquired from RGB cameras. Spatial information are extracted from videos by splitting them into RGB channel frames and do a one frame at a time classification. Temporal information from videos are extracted by computing their optical flow. The results are combined in order to build a real time human activity recognition system. The network is tested using TIAGo robot for performing activity recognition. The accuracy of the system is 87,05 %, that is comparable with the state of the art. Also, results are obtaining in real time.
A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

Energy crisis and carbon emission have become two seriously concerned issues universally. As a feasible solution, Energy Internet (EI) has aroused global concern once proposed. EI is a new power generation developing a vision of evolution of smart grids into the Internet. The communication infrastructure is an essential component to the implementation of EI. A scalable and permanent communication infrastructure is crucial in both construction and operation of EI. In this paper, we present an introduction and the motivation to the evolution from smart grid to EI. We also introduce a representative EI architecture, i.e., the future renewable electric energy delivery and management system. Four critical EI features are emphasized. Then, we summarize the essential requirements that EI systems have to meet. With several key supporting technologies, EI shall realize the optimal utilization of highly scalable and distributed green energy resources, so that the situation of severe energy source crisis and carbon emission can be efficiently relieved. Since an EI system might have extensively distributed consumers and devices, the guarantee of its reliability and security is extremely significant. The further specific exploration for challenges, including reliability and security, will be stated in this paper.
Mapping of Eigenvalue Performance Specifications by Real Root Classification

This paper provides a method based on real root classification (RRC) for computation of parameter space regions for LTI systems, guaranteeing certain system performance specifications described by the eigenvalue location. To this end, parameter-dependent state-space system descriptions are considered and the Lyapunov equation is utilized as the basic vehicle to derive the mapping conditions.
Shared Admittance Control for Human-Robot Co-manipulation based on Operator Intention Estimation

Collaborative robots are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In this paper, we assume the presence of an operator cooperating with a lightweight robotic arm, able to autonomously navigate its workspace, while the human co-worker physically interacts with it leading and influencing the execution of the shared task. In this scenario, we propose a human-robot co-manipulation method in which the autonomy of the robot is regulated according to the operator intentions. Specifically, the operator contact forces are assessed with respect to the autonomous motion of the robot inferring how the human motion commands diverges from the autonomous ones. This information is exploited by the system to adjust its role in the shared task, leading or following the operator and to proactively assist him during the co-manipulation. The proposed approach has been demonstrated in an industrial use case consisting of a human operator that interacts with a Kuka LBR iiwa arm to perform a cooperative manipulation task. The collected results demonstrate the effectiveness of the proposed approach.
Input Offset Estimation of CMOS Integrated Circuits in Weak Inversion

This brief studies the current mismatch of MOS transistors operating in weak inversion region. Explicit formulas are derived from the drain-current relationship of a long-channel MOS transistor. By referring the drain-current variance to the gate terminal under the small-signal conditions, the dependence of the gate–source voltage on the design parameters and sources of mismatch can be considered. This will help designers to choose the optimum sizes and the efficient $gm/I_{\text {D}}$ ratio for current offset reduction of transistors that their mismatch affects the circuit performance, significantly. In order to confirm the accuracy of the calculated drain-current variance, the hand analysis results are compared with MATLAB simulator outputs. In addition, the input offset of a conventional folded-cascode amplifier is estimated by hand analysis, and the results are compared with SPICE simulator outputs through Monte Carlo analysis.
State Space Identification Algorithm based on Multivariable Impulse Response

In this paper the definitions of multivariable discrete impulse and multivariable discrete impulse response are clearly stated and explored. From these definitions, two methods to determine the Markov parameters of a multivariable linear system from input and output data are described. Combining any of the methods to a known method to determine the state space model matrices from the Markov parameters, a practical algorithm to determine state space models from input and output data is obtained. The algorithm is then implemented and compared to a known subspace identification algorithm. The main contributions of this paper are the explicit definitions of multivariable discrete impulse and multivariable discrete impulse response, the discussion of these concepts and the application of them to solve the multivariable linear system identification problem.
Experimental Channel Measurement for Indoor Office Visible Light Communication

Channel measurement for visible light communication (VLC) is theoretically and experimentally performed in a real office platform. Six armatures composed of phosphor based white LED arrays are fed by designed analog LED drivers. Measurements are taken in a 6m x 7.8m office room which is formed as 10×13 matrix with 60 cm grid distance for both simulation and experiment studies. The link distance between LED arrays and optical receiver is kept minimum 2.5 meters for each measurement. Experimental results are generally in good agreement with simulation results, which are obtained using Barry's theoretical channel model.
Admission Control and Resource Allocation for Distributed Services in System-of-Systems: Challenges and Potential Solutions

System-of-Systems (SoS) are a paradigm that has been proposed to cope with the evolving new services in large scale systems that not only need extensive computing resources, but also may have extra-functional properties like stringent time requirements. In SoS, a complex task is typically divided into sub-tasks that have a lower complexity and can be performed by multiple distributed systems working independently from each other. However, the coordination between these systems is of paramount importance to achieve the required compound complex task. Admission control and resource allocation are two essential mechanisms needed to coordinate the distributed tasks among the systems. However, performing admission control and resource allocation in such distributed systems is challenging, because of the distributed nature of SoS. In this paper, the proposed SoS system architecture is described, then the process of admission control and resource allocation is discussed, while highlighting the potential challenges associated with this process and its solutions.
Arthroscopic Superior Capsule Reconstruction for Irreparable Rotator Cuff Tear and Axillary Nerve Palsy Associated with Anterior Dislocation of the Shoulder

Introduction: This report describes a case of irreparable massive rotator cuff tear and axillary nerve palsy associated with shoulder dislocation successfully treated by arthroscopic superior capsule reconstruction (ASCR), with a favorable post-operative outcome. Case Report: A 76-year-old man, injured from a fall while walking, presented to another hospital with right shoulder pain and a limited range of motion (ROM) 3 days after the injury. Given a diagnosis of right shoulder dislocation, he received manual reduction followed by immobilization with a sling. He continued to experience difficulty in performing active ROM exercises of the shoulder and underwent magnetic resonance imaging, which revealed an irreparable extensive rotator cuff tear involving the supraspinatus and infraspinatus muscles. He was then referred to our hospital 2 months after the injury. Examination revealed atrophy of the supraspinatus and infraspinatus muscles, atrophy of the deltoid muscle and hypoesthesia, likely due to axillary nerve palsy, and a marked limitation of active ROM with flexion, abduction and lateral rotation angles of 10°each. ASCR was considered for treating the irreparable rotator cuff tear. Since the technique is not indicated for patients with deltoid paralysis, the operation was delayed until signs of improved axillary nerve palsy were observed at 6 months after the injury. The patient started passive ROM training the day after the operation while wearing a shoulder abduction orthosis for 3 weeks, followed by immobilization with a sling for 2 weeks. Thereafter, he started active exercise. The axillary nerve palsy was almost completely resolved 3 months after the operation. He achieved a ROM comparable to that of the unaffected side at 1 year after operation. He has had an uneventful post-operative course for 2 years after operation. Conclusion: We encountered a rare, difficult-to-treat case of irreparable extensive rotator cuff tear with axillary nerve palsy after a shoulder dislocation. ASCR, although not indicated for patients with deltoid muscle dysfunction, provided a favorable post-operative outcome when performed after confirmation of signs of improved palsy.
Detecting and Analyzing Privacy Leaks in Tweets

Social network platforms are changing the way people interact not just with each other but also with companies and institutions. In sharing information on these platforms, users often underestimate potential consequences, especially when such information discloses personal information. For such reason, actionable privacy awareness and protection mechanisms are becoming of paramount importance. In this paper we propose an approach to assess the privacy content of the social posts with the goal of: protecting the users from inadvertently disclosing sensitive information, and rising awareness about privacy in online behavior. We adopt a machine learning approach based on a crowd-sourced definition of privacy that can assess whether messages are disclosing sensitive information. Our approach can automatically detect messages carrying sensitive information, so to warn users before sharing a post, and provides a set of analysis to rise users awareness about online behavior related to privacy disclosure.
A Blockchain based Access Control Scheme

: Recent years have witnessed the trend of increasingly relying on remote and distributed infrastructures. This increased the number of reported incidents of security and privacy breaches, mainly due to the loss of data control. Towards these challenges, we propose a new access control scheme based on emerging blockchain infrastructures. Our approach relies on the use of smart auditable contracts deployed in blockchain infrastruc-tures. Thus, it offers transparent and controlled access to outsourced data, such that malicious entities cannot process data without data owners’ authorization. In fact, the effectiveness of the authentication relies on the blockchain intrinsic properties. Moreover, an implementation of the proposed solution based on Ethereum Blockchain is presented to show the applicability of our scheme in real-world scenarios.
Autonomic disturbances in diabetes: Assessment and anaesthetic implications

Diabetes mellitus is the most common medical condition and with increased awareness of heath and related issues, several patients are getting diagnosed with diabetes. The poor control of sugar and long-standing status of disease affects the autonomic system of body. The autonomic nervous system innervates cardiovascular, gastrointestinal, and genitourinary system, thus affecting important functions of the body. The cardiovascular system involvement can manifest as mild arrhythmias to sudden death. Our search for this review included PubMed, Google Search and End Note X6 version and the key words used for the search were autonomic neuropathy, diabetes, anesthesia, tests and implications. This review aims to highlight the dysfunction of autonomic system due to diabetes and its clinical presentations. The various modalities to diagnose the involvement of different systems are mentioned. An estimated 25% of diabetic patients will require surgery. It has been already established that mortality rates in diabetic patients are higher than in nondiabetic patients. Hence, complete workup is needed prior to any surgery. Diabetic autonomic neuropathy and its implications may sometimes be disastrous and further increase the incidence of in hospital morbidity and mortality. Overall, complete knowledge of diabetes and its varied effects with anaesthetic implications and careful perioperative management is the key guiding factor for a successful outcome.
A multi‐GPU protein database search model with hybrid alignment manner on distributed GPU clusters

In computational biology, technologies of protein database search become more and more indispensable for researchers. As the database size rapidly grows, the search complexity is improving, leading to an excessively long runtime. In this paper, we provide a novel parallel protein database search model based on distributed GPU clusters. The proposed model targets clusters composed of personal computers equipped with graphic processing unit (GPU) cards and can employ computing power of GPUs and CPUs in the cluster nodes. The workload distribution strategy is designed to take the performance of each node into account, which enables the model to work well with inhomogeneous cluster nodes with different hardware configurations. A hybrid alignment approach, an extension to our previous work, is used in our model to make global and local alignments done concurrently. The parallel program is realized with compute unified device architecture (CUDA) parallel framework and Microsoft message passing interface (MS‐MPI). In the experiment, the model is tested on a small cluster composed of three personal computers. The results show that the proposed model can achieve a speedup up to 159.89 times over the serial counterpart when searching the Swiss‐Prot database.
Reengineering of Corporate Management in the Innovative Context and Professional Competencies of a Reengineer

The reengineering approach helps to create a system of efficient management for flexible companies within the changing world and innovative growth. The real value is created and captured by people who are able to detect the new customers' needs or to discover new paths for market evolution. In order to assess a product or service as new and attractive, to motivate or communicate, humans use nominalization on the basis of verbal evaluations, presented in the language. The process description based on the identification of values passes through structuring activities and algorithms towards the nominalization of functions and roles as positions and semantic entities, that make sense of activity. Functional operations reflect not only tasks that are verbally fixed, but also actions as a phase of uncertainty and multi-factor continuous choice, decision-making and outcome evaluation. Digital twins are a vivid example of a tool that provides a huge volume of data, but does not reduce uncertainty, that requires interpretation and reflection to produce a meaning. The concept of reengineering in the digital economy relies both on data collection and their intellectual analysis, and on the synthesis of conclusions based on linguistic nominalization.
A Flexible Framework for Rogue Access Point Detection

The society’s requirement for constant connectivity, leads to the need for an increasing number of available Wi-Fi Access Points (APs). These can be located almost everywhere: schools, coffee shops, shopping malls, airports, trains, buses. This proliferation raises problems of trustworthiness and cost-effective difficulties for verifying such security. In order to address these issues, it is necessary to detect effectively Rogue Access Points (RAPs). There are open source solutions and others developed within enterprises for commercial purposes. Relative to the latter, it has become obvious that they are not accessible to everyone due to their high costs, and the former do not address all the types of RAPs. In this paper, we research these solutions and do a thorough survey study of the most commonly used and recent Wi-Fi type of attacks. Based on this knowledge we developed a solution to detect RAPs, which covers the most commonly known attacks. This proposed solution, is a modular framework composed of Scanners, Detectors and Actuators, which are responsible for scanning for available APs, apply a set of heuristics to detect them and apply a countermeasure mechanism.
Delayless Controllers for Triangular Decoupling with Simultaneous Disturbance Rejection of General Neutral Time Delay Systems

The problem of Triangular decoupling with simultaneous Disturbance Rejection (TDDR) is studied for the class of general neutral multi delay systems with measurable disturbances via delayless controllers. The delayless controllers are of the measurement output feedback type with compensation of the measurable disturbances. The cases of dynamic and static controllers are studied separately. For both cases, the necessary and sufficient conditions for the problem to have a solution are established and the general form of the delayless controller matrices solving the problem are derived.