Miracle-dz/UniFAR-FTUs huggingface dataset:
Zygomatic bone registration based on a modified student's mixture model method

Point Set Registration (PSR) of anatomic parts is used in different fields such as Patient Specific Implant (PSI) design in Computer Assisted Surgery (CAS) procedure. We designed a modified rigid PSR method based on student's-t mixture model. The proposed method is compared with Coherent Point Drift (CPD) registration method. Higher convergence speed and the lower error value are the advantages of the suggested algorithm in compare with CPD. In our method, the number of iterations decreases by about 69%, and the final error improvement was about 7% in comparison with CPD. The robustness of the proposed algorithm makes it beneficial to be used in the procedure of designing PSI in both unilateral and bilateral zygomatic bone defects caused by trauma or tumor ablation in the field of maxillofacial.
Vehicle Sideslip Angle Estimation based on Switched Fuzzy Model*

The vehicle sideslip angle is one of the most important state variables for vehicle motion control. Its accurate estimation is necessary for many vehicle control systems. In this paper, a novel switched Takagi–Sugeno (T-S) fuzzy observer is proposed to estimates the vehicle sideslip angle. Firstly, to approximate the nonlinear tire model well, a new piecewise affine (PWA) tire model is established. Different from the classic two-segment PWA tire model, the lateral tire force is divided into three regions, i.e. linear region, nonlinear region and saturated region in the proposed model. Substituting it into the bicycle model, the switched lateral dynamic model is obtained whose switching signal is the tire slip angle. Then, the T-S fuzzy modelling technique is applied to represent the vehicle lateral dynamics with a varying speed. Combined with the aforementioned model, a switched T-S fuzzy model is obtained to describe the lateral dynamics. Based on the measured yaw rate, a switched T-S observer is designed. Simulation results of different maneuvers based on the high- fidelity simulation software veDYNA show that the designed observer provides a fast response and has a good estimation result.
Necessary and sufficient LMI conditions for constraints satisfaction within a B-spline framework

This work presents a novel solution for the satisfaction of continuous-time constraints through the use of B-spline curves. Exploiting the B-splines functions properties (local support, convexity and positivity), we provide necessary and sufficient LMI (Linear Matrix Inequalities)-based conditions for the satisfaction of continuous-time constraints. We consider both inclusion and exclusion constraints (in relation with a predefined convex domain) and implement the latter as a MISDP (Mixed integer - Semi-definite Programming) problem. We highlight the approach for the motion planning issue of off-line trajectory generation with obstacle(s) avoidance guarantees.
Second-Order Necessary Conditions for Optimal Control Problems with Fixed Terminal Time and Free Terminal State

In this paper, we focus on piecewise continuous control strategies and derive second-order necessary conditions for optimal control problems with a fixed terminal time and free terminal state under the sole additional assumption that the state dynamics and the cost functional are two-times continuously differentiable with respect to the state variable. In analogy to nonlinear programming problems with two-times continuously differentiable objective functions, these conditions can reduce the set of control strategies which need to be investigated when searching for the optimal control strategy. This is especially true for problems with bang-bang extremals where our derived results can be shown to be consistent with those in literature for control-affine systems.
A Novel High-Resolution Imaging Method Using Reduced-Dimension Beamspace Unitary MUSIC for OFDM-MIMO Radar

Imaging of maneuvering targets has become a challenging topic recently. This study proposes an improved MUSIC-based single-snapshot imaging method for OFDM-MIMO radar. At the transmitter part, for orthogonal waveform generation, the interleaved OFDM structure is developed that each transmitter only radiates at an equidistant set of OFDM subcarriers unique to itself. At the receiver part, a novel preprocessing scheme is presented to separate multiple transmit/receive paths and compensate the reference point. Preprocessed echo matrix can fit the model of two-Dimensional (2D) MUSIC algorithm, except for the coupling between radial/cross-range in the virtual antenna array. Correspondingly, we modify the 2D ElementSpace (ES)-MUSIC. In the meantime, a 2D Reduced-Dimension BeamSpace Unitary-MUSIC (RD-BS-UMUSIC) algorithm is proposed for computational complexity reduction and estimation performance enhancement. Numerical results of a simulated target consists of limited number of scatterers and a complex target (a Boeing 737-800 plane) verify that the proposed algorithm can improve the imaging quality and reduce computational complexity effectively compared with the traditional method.
Wireless Charging in Electric Vehicles: EMI/EMC Risk Mitigation in Pacemakers by Active Coils

An innovative shielding configuration of active coils is proposed to mitigate the magnetic field around an electric vehicle (EV) with a wireless power transfer (WPT) system during battery charging. The active coil is designed to reduce the risk for patients with pacemakers or similar devices produced by the time varying magnetic field generated by the 85 kHz WPT coil currents. A numerical analysis of the magnetic field levels is carried out solving the magneto-quasi-static (MQS) equations by a FEM-based commercial tool. The calculated field which exceeds the limits of electromagnetic compatibility (EMC) and electromagnetic field (EMF) safety standards for human exposure is mitigated by active coils. The magnetic field levels in the environment and the induced peak-to-peak voltage in the pacing lead is predicted in presence and in absence of active shielding.
Evaluating Progress of a Social Venture in Wakiso District Uganda

Monitoring and evaluation (M&E) of funded projects is discussed. The goal is to determine an effective process with which IEEE Smart Village (ISV) can most easily collect the most valuable project data. The presentation begins with an operational definition of sustainable community development (SCD). The dichotomy between economic sustainability and charity is pointed out. A general model of SCD is discussed. Social Return on Investment (SRoI) is a number that is expected to reflect project efficacy. The process to determine SRoI scrutinizes a project under evaluation. The scrutiny uncovers data that becomes re-hidden in the single SRoI number. The authors of this paper took part in a SRoI process applied to an IEEE Smart Village funded non-profit, non-governmental organization, Africa Development Promise (ADP). The focus was to inform the ISV M&E process. Some salient features of the location (Wakiso District, Central Region, Uganda) are elucidated. Some detail of the sub-projects that are under valuation are given. Data collected from the SRoI process are used to determine what parts of the SRoI process are most relevant to M&E of the type of SCD projects that ISV is monitoring. An M&E process and its implementation are proposed and discussed.
Polyhedral Path-Complete Lyapunov Functions

Path-complete methods utilize a set of positive definite functions and a specially constructed graph in order to evaluate, among others, stability of switching systems. This tool is shown to be general, e.g., path-complete criteria are universal for linear switching systems and quadratic templates. In this work, we extend the approach to polyhedral Lyapunov functions, and introduce a simple parameterization that can be sufficient for stability analysis. Moreover, we indicate ways of obtaining less conservative stability criteria by partial graph extensions, all evaluated by solving Linear Programs (LPs).
Using virtual reality to boost the effectiveness of brain-computer interface applications

Noninvasive brain-computer interface (BCI) uses data from electroencephalographic (EEG) sensors to train a model using machine learning and pattern detection algorithms to recognize certain patterns. These patterns correspond to a command to an actuator like wheels of a wheelchair, keyboard or mouse. Virtual reality is used to make a virtual environment (VE) where the users can control virtual objects. BCI generated commands can be used to control the virtual objects with a suitable interface. Unity 3D software provides the interface and the VE. The noninvasive nature of input collection makes the input prone to noise and error. The user needs considerable amount of training to learn to control the wheelchair efficiently. This training is done in a VE where the user controls a virtual wheelchair. The accuracy of wheelchair control before and after training in the VE is compared. It is observed that the user can control the physical wheelchair with better accuracy after training in the VE.
A Model Reference Adaptive Continuous Sliding-Mode Control

This paper deals with the problem of robust tracking for a class of scalar linear systems. To this aim, a nonlinear control law is proposed based on a Model Reference Adaptive Continuous Sliding-Mode Control (MRA Continuous-SMC) approach. Such an approach is composed of nonlinear adaptive gains that provide a rate of convergence faster than exponential and ensure convergence to zero of the tracking error. The corresponding convergence proofs are based on a Lyapunov function approach. Finally, some simulation results show the feasibility of the proposed scheme.
Decision Support System to Agronomically Optimize Crop Yield based on Nitrogen and Phosphorus

India has always been a high agricultural value country. Agriculture and its industries are one of the country's leading sources of life and economy. The prediction of crops can lead to a positive response that can help farmers improve yields and reduce losses. Natural chemicals like Nitrogen(N), Phosphorus(P), Potassium(K), pH along with soil type (clay soil, black soil, red soil, silt soil, etc.,) and average rainfall dataset are used to predict yield. This project concentrates on balancing macro nutrients (NPK) which is very important requirement for the crop growth. Different doses of these nutrients will be considered foryield prediction of various regions of Karnataka. Since, macro nutrients play major role, by varying the amount of nutrients supplied for crops and sharing this knowledge to farmers will help them to increase crop yield. This proposed work will help farmers to identify the right amount of nutrients supply to particular crops like maize, rice and wheat. Farmers are losing their yield by having lack of knowledge about nutrients requirement for particular crops, this paper concentrates on rice, maize and wheat crop for various regions of Karnataka for prediction. Right quantity of nutrients supply will help farmers to increase their yield.
A Transfer Learning Algorithm Based on CSP Regularizations of Recorded EEG for Between-Subject Classiftcation

Feature extraction and classification are the most important parts of BCI systems. The new branch of BCI studies focuses on the design of a classifier that is trained to function properly for each individual. This problem is known as Transfer Learning. In between-subject classification, due to the differences in the neural signals’ distribution of different individuals, using the common methods of feature extraction for training the classifier, does not lead to high accuracy for the test subject. As a result, in this study, we present a method for extracting features that perform well in between subjects classifications. The data that we used in this study are EEG signals recorded during five mental tasks from nine participants with motor disabilities. The proposed method is generalized from the conventional CSP approach. To this end, we proposed a combination of two CSP regularization methods. Finally, to show the efficiency of the algorithm, we applied it to the data and compared the results with those of the previously used methods. The algorithm that we suggested led us to the best accuracy (73%) in comparison to other previous methods. Therefore, this algorithm can be a useful tool in any between subject classification problem.
A 3-PRS Parallel Robot Control Based on Fuzzy-PID Controller

In this paper, a new Type-II Fuzzy-PID based approach is proposed to control a 3-PRS parallel robot. 3-PRS parallel robots are very common in industrial applications due to their high precision. In addition, according to the unique kinematic characteristics of these kinds of robots, they are capable of avoiding the singularity at the zenith angle during the tracking. The two inputs of the Fuzzy System (FS) are the error and its changes. The proposed controller applies the best control signal to the robot system by determining the PID coefficients. The simulation results confirm that a PID controller tuned by the Type-II Fuzzy system is an effective method to control these kinds of robots. The performance comparison of the Type-I and Type-II Fuzzy systems indicates the superiority of the Type-II Fuzzy system.
Convex Controller Synthesis for Robot Contact

Controlling contacts is truly challenging, and this has been a major hurdle to deploying industrial robots into unstructured/human-centric environments. More specifically, the main challenges are: (i) how to ensure stability at all times; (ii) how to satisfy task-specific performance specifications; (iii) how to achieve (i) and (ii) under environment uncertainty, robot parameters uncertainty, sensor and actuator time delays, external perturbations, etc. Here, we propose a new approach – Convex Controller Synthesis (CCS) – to tackle the above challenges based on robust control theory and convex optimization. In two physical interaction tasks – robot hand guiding and sliding on surfaces with different and unknown stiffnesses – we show that CCS controllers outperform their classical counterparts in an essential way.
Refactoring as a Technique for Transformation IMS-Queries into SQL-Queries

Refactoring is defined as special techniques for restructuring an existing body of a code and its internal structure without changing its external functionality. In other words one can say that refactoring is recoding of the source code. Refactoring approach can be used for complex transformation of the application programs from PL/1 or COBOL (with invocations to IMS databases) to C or Java with invocations to some modern database like Oracle or MYSQL. Since an enormous lines of PL/1 code are working now (some of them with poor documentation) and are needed to be transformed on modern platforms therefore automated translation should be done (as intensive as possible). Some ideas about decision of such transformation will be done in future.
Leaf Based Trees Identification Using Convolutional Neural Network

There are huge numbers of species of trees present on the earth. Only those who study plants can easily identify them but for a common people it’s very difficult to distinguish among them. Machine learning is an important technology of all time. Deep learning is basically a subset of a machine learning that automatically extracts best features, learns it and provides best results. In this paper we have proposed one of the deep learning’s approach model Convolutional Neural Network (CNN) that automatically classify different trees (Mango, Neem, Hibiscus, Guava, Lemon, Ashoka, Curry, Sheesham, Cachia, Henna) on the basis of their leaves and achieved the test accuracy of 99.40%. Experimental results were performed on our own local dataset of leaves, which consists of 10,000 images of leaves, one thousand for each class of trees. Results show that the proposed system provides an effective classification model for the identification of trees on the basis of their leaves.
Enhanced State Feedback Control of T-S Fuzzy Systems with Time-delays

In this paper, the issue of designing an enhanced controller is exploited for time-delayed nonlinear systems on the basis of T-S fuzzy models. At present, a large number of existing design methods of fuzzy controller independent on membership functions, which are doubtlessly conservative. To overcome the aforementioned shortage, this paper first establishes a novel Lyapunov-Krasovskii functional containing both delay-product-type terms and membership functions. Then, a membership-function-dependent stability criterion is developed by using Wirtinger-based integral inequality and an extended reciprocally convex matrix inequality. Subsequently, an improved state feedback fuzzy controller is designed. Finally, the merits and the availability of the obtained stability criterion and fuzzy controller designed method are verified by two numerical examples.
Research on Software Defined Spectrum Sharing Cognitive Multi-Beam Satellite Communication System

The increasing number of users of satellite communication system needs more spectrum and bandwidth resources. In the future, multi-beam broadband satellite system must have greater flexibility and be able to adjust dynamically according to the change of traffic flow. Dynamic spectrum access is an effective method of spectrum sharing, which can realize the effective utilization of spectrum resources. Cognitive radio spectrum sharing technology is an effective way to improve spectrum efficiency and realize the effective utilization of spectrum resources. This paper studies software-defined spectrum-sharing cognitive radio multi-beam broadband satellite network system. The technology achieves effective management of satellite resources, improves the utilization of satellite resources and improves the performance of multi-beam satellite communication system. The principle of software-defined spectrum-sharing cognitive multi-beam broadband satellite network system is discussed. The spectrum utilization efficiency and throughput are analyzed.
Limitation of lightning overvoltages in electrical substation 220 kV due to back flashover by installing surge arresters

Lightning strikes on transmission overhead lines are one of the most frequent reasons for outages of power supply. Overvoltages due to back flashover can endangered the equipment in the substations in the cases of flashover near the substation. This paper presents model study of the limitation of lightning overvoltages in electrical substation 220 kV by installing surge arresters on various places. The substation, towers and overhead lines were modeled by using ATP-EMTP software. The results of the model show improvement of the lightning performance of the overhead line 220 kV and decrease overvoltages level in the substation. The results from this paper could be useful for reduction the outages and improve the design of the power system.
PolyPy: A Web-Platform for Generating Quasi-Random Python Code and Gaining Insights on Student Learning

This Innovative Practice Full paper introduces PolyPy, a freely available web-platform that allows instructors to automate the creation of many different coding question instances from a single template. Interactive web platforms offer a way of engaging students with practice material while also providing them with real-time feedback. However, many of these platforms depend on manually pre-setting a static pool of questions and answers, which limits 1) the number of unique coding challenges presented to students, 2) the ability of instructors to dynamically customize challenges, and 3) the ability to recognize and adapt to individual student needs. PolyPy bypasses these limitations by providing a flexible and easy-to-use framework that lets instructors define question templates. These templates contain segments that can exhibit one of many pre-set values; we refer to these segments as “randomizable elements. ” The question instances are generated (from the templates) on-the-fly every time a student requests a new question via the student interface. To verify the correctness of students’ answers, PolyPy interprets these question instances and compares their output to the students’ submissions in real-time. All students’ attempts, both correct and incorrect, are stored and can be accessed by instructors; this information can be used to gain insight on the strengths and weaknesses of student comprehension. This paper presents the PolyPy system architecture and describes the student and instructor interfaces. It reports promising preliminary results of students’ use of PolyPy along with feedback obtained regarding their learning experience.
Performing SLAM using Low-Cost Sensors for Autonomous Navigation in household environments

In this work, we present a way of performing Simultaneous Localization And Mapping on a mobile robot using an array of ultrasonic sensors as a low cost alternative to laser rangefinders(LIDAR) and RGB-D Cameras. The mobile robot captures information around it using ultrasonic sensor ring, and creates a map with position data obtained from the encoders of motors. The robot runs using ROS on a Intel NUC. The algorithm has been evaluated and tested for household environments and good mapping estimations were obtained.
Decentralized Estimator-based Secondary Voltage and Reactive Power Control of an Islanded Microgrid

A decentralized secondary control approach is proposed to compensate voltage deviations and to improve reactive power sharing accuracy in islanded microgrids. A state observer according to Kalman filter theory is designed based on a reduced order small-signal model to estimate reactive power and system voltages through local measurements without using communication system. An optimal tuning algorithm is used to tune PI controller in voltage control loop and a regularly tuned PI controller is used for compensating reactive power error. The effectiveness of the proposed control strategy is verified by the simulation of a microgrid test system in SIMULINK/ Simscape toolbox.
A Randomized Block Subgradient Approach to Distributed Big Data Optimization

This paper introduces a novel distributed algorithm over static directed graphs for solving big data convex optimization problems in which the dimension of the decision variable can be extremely high and the objective function can be nonsmooth. In the proposed algorithm nodes in the network update and communicate only blocks of their current solution estimate rather than the entire vector. The algorithm consists of two main steps: a consensus step and a subgradient update on a single block of the optimization variable (which is then broadcast to neighbors). Agents are shown to asymptotically achieve consensus by studying a block-wise consensus protocol over random graphs. Then convergence to the optimal cost is proven in expected value by exploiting the consensus of agents estimates and randomness of the algorithm. Finally, as a numerical example, a distributed linear classification problem is solved by means of the proposed algorithm.
Attitude Observation for Second Order Attitude Kinematics

This paper addresses the problem of estimating the attitude and angular velocity of a rigid object by exploiting its second order kinematic model. The approach is particularly useful in cases where angular velocity measurements are not available and the attitude and angular velocity of an object need to be estimated from accelerometers and magnetometers. We propose a novel sensor modality that uses multiple accelerometers to measure the angular acceleration of an object as well as using magnetometers to measure partial attitude. We extend the approach of equivariant observer design to second order attitude kinematics by demonstrating that the special Euclidean group acts as a symmetry group on the system considered. The observer design is based on the lifted kinematics and we prove almost global asymptotic stability and local uniform exponential stability of the estimation error. The performance of the observer is demonstrated in simulation.