Miracle-dz/UniFAR-FTUs huggingface dataset:
3D-Printed Electrocardiogram Electrodes for Heart Rate Detection in Canines

This paper describes the design and fabrication of 3D-printed conductive electrodes for measuring heart rate and heart rate variability in animals. The customizable electrodes have a three-legged stool structure with rounded edges in order to provide optimized, balanced, and comfortable skin contact through the fur of the animal without needing to shave it. We explored two alternative designs for manufacturing: a flexible, insulated base structure coated with graphene for conductivity and a rigid, all-conductive, graphene-infused PLA base structure. To enable connection to standard female electrocardiogram snap connectors, we epoxied the former electrode with a metal male snap connector and the latter electrode had the standard-sized male snap connector 3D printed in one assembly. We characterized and compared the performance of these electrodes through electrochemical impedance spectroscopy and benchmarked with commercial electrodes traditionally used in veterinary clinics. Preliminary in vivo results demonstrate the feasibility of these electrodes to measure heart rate and heart rate variability in canine puppies.
VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection

Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy.
Luenberger Observers for Nonautonomous Nonlinear Systems

We show how the (initial) Luenberger methodology presented by Luenberger in “Observing the state of a linear system,” IEEE Transactions on Military Electronics, vol. 8, pp. 74–80, 1964, for linear systems can be used to design causal observers for controlled nonlinear systems. Their implementation relies on the resolution of a time-varying PDE, the solutions of which transform the dynamics into linear asymptotically stable ones. We prove the existence and injectivity (after a certain time) of such transformations, under standard observability assumptions such as differential observability or backward-distinguishability. We show on examples how this PDE can be solved and how the observability assumptions can be checked. Also, we show that similarly to the high gain framework, it is possible to use a time-independent transformation when the system is observable for any input and strongly differentially observable of order the dimension of the system.
Collaborative Filtering based Course Recommender using OWA operators

Recommendation systems guide users to choose the most appropriate items among numerous alternatives based on predicting their interests. Recently, it is seen that recommendation systems have become to be widely used in educational domain, especially in course recommender applications. The objectives of these systems is facilitating course selection process of students and reducing their stresses. The current course recommendation studies generally consider the most recent grades of the courses taken by students and ignore the case of repeating the course under the pass-fail or grade replacement options. However, retaking a course is the primary parameter giving opinion about tendency of the students to the courses. In this study, we propose a novel collaborative filtering (CF) based course recommendation system considering the case of repeating a course and students’ grades in the course for each repetition. We experiment different Ordered Weighted Averaging (OWA) operators which aggregates grades for each student’s repeated courses to enhance the recommendation quality. The normalized mean absolute error (MAE) of our approach using CF and OWA is calculated as 0,063 which is encouraging for future work.
An Improved Intentional Islanding Strategy in Distribution Network with Hierarchical Control

Intentional islanding operation can provide uninterrupted power supply for the essential load in case the main grid is failed or overhauled. This paper proposes an improved intentional islanding strategy with the hierarchical control, which can achieve a more flexible scope of islanded area regardless of the point of common coupling (PCC). The procedure of seamless transition at any point of distribution system under hierarchical control is described. During islanding operation, a power sharing method with the coordinated control of various types of distributed generations (DGs) is also proposed. This coordination is able to improve the power output curve of the master DG in islanding operation, reduces the fluctuations of total net load, and prevents the curtailment of intermittent DGs. To verify the proposed hierarchical control strategy, a demonstration application with multiple DGs is designed and implemented in a practical distribution system, which is located in Guiyang, China. The operation result of field test has shown the feasibility of the proposed method.
Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16×16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6 mW.
A Bi-level Programming Model for Distribution Network Expansion Planning with Distributed Generations and Energy Storage Systems

A novel optimization model for distribution network expansion considering the construction of distributed generation (DG) and energy storage system (ESS) simultaneously is proposed in this paper. This proposed model consists of the upper-level sub-problem involving the long-term investment decisions and the lower-level sub-problem involving the short-term operation strategies. Several typical day scenarios are designed to deal with the uncertainties of load demand and DGs including wind generation together with photovoltaic unit. A bilevel programming method combing the improved genetic algorithm (IGA) with the mixed integer linear programming (MILP) is applied in the solution of the proposed optimization model. Finally, case studies are carried out on the 16-bus system as benchmark to achieve a variety of planning scheme options.
Design of Novel Metaheuristic Techniques for Clustering

One of the major drawbacks of clustering techniques utilizing a predetermined number of clusters is that it does not guarantee convergence to the global optimum. Evolutionary algorithms are one of the most powerful general search techniques that can guarantee, if well designed, to converge to the global optimum. In this paper, a novel metaheuristic framework capable of identifying the optimum number of clusters will be proposed. In the proposed framework, an edge-recombination crossover operator, along with a redesigned chromosome architecture, is implemented to help in obtaining the optimum number of clusters. A genetic algorithm (GA)-based technique, an artificial immune system centralized on clonal selection algorithm, and immuno-GA will be designed and implemented. The proposed techniques will be tested on different real-world data sets with various sizes and dimensions. The obtained results are then used in a comparative study between all the proposed techniques to determine and select the best solution. Further investigation for the effectiveness of the results and the verification of the statistical validity for the number of clusters is carried out using Mann–Whitney–Wilcoxon rank-sum test.
An advanced maximum power point capturing technique with supercapacitor for PV system

In this paper, the characteristic curves considering complete Current-Voltage (I-V) and Power-Voltage (P-V) relations for PV system are presented. Based on the I-V characteristic curve, a novel Maximum Power Point Capturing (MPPC) technique is proposed to overcome the steady-state oscillation and slow dynamic response problems existing in the conventional Maximum Power Point Tracking (MPPT) technique. In order to further mitigate the fluctuations for PV system, an appropriate supercapacitor (superC) is selected to be connected in parallel with the PV system. The simulation results demonstrate the designed MPPC-superC structure has a better performance than the conventional MPPT method and MPPC method in both steady-state and dynamic responses.
Cooperative Control of Energy Storage for Transient Stability Enhancement

The increasing adoption of inertia-less power sources raises concerns with respect to the ability of the power grid of the future in resisting large disturbances. In this paper, we propose a method that leverages fast-acting energy storage to stabilize generators when subject to contingencies. This is done by a cooperative control protocol capable of coordinating the control actions of neighboring control agents. The goals of this controller is achieving rotor speed synchronism between nearby generators and limiting the rotor angular frequency deviation from nominal values. The distributed protocol is tested using IEEE 39-bus test system implemented in MATLAB/Simulink. The results show that the designed controller can increase the critical clearing time of the system when compared to classical power system stabilizers and a parametric feedback linearization method that leverages energy storage systems.
Unbalanced Frequency Dependent Load Flow for Microgrids

This work presents an unbalanced three-phase frequency dependent load flow for microgrids at distribution level considering the insertion of renewable energy sources, distributed generation and frequency dependent loads. To assess the frequency influence on microgrids, the IEEE 34 Bus test feeder was employed. Further, simulations were held for both traditional and frequency dependent load flow approaches. The results indicated that the frequency dependency interaction accounted by the proposed methodology provides a more realistic scenario leading to a better-conditioned microgrid operation, which is fundamental for islanded system studies when compared to the traditional approach, which provides a more pessimistic perspective.
The Minimum Specific on-Resistance of Semi-SJ Device

The minimum specific ON-resistance <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ \mathrm{\scriptscriptstyle ON},\textsf {sp}}$ </tex-math></inline-formula> of the semisuperjunction (semi-SJ) device is realized in this paper. Based on the similar SJ optimization method in our previous works, the minimum <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ \mathrm{\scriptscriptstyle ON},\textsf {sp}}$ </tex-math></inline-formula> of the semi-SJ is searched by optimizing both the semi-SJ and the N-buffer regions. The proposed design formulas give the optimized doping concentration <inline-formula> <tex-math notation="LaTeX">$\text{N}_{\textsf {SJ}}$ </tex-math></inline-formula> of the semi-SJ region, doping concentration <inline-formula> <tex-math notation="LaTeX">$\text{N}_{\textsf {B}}$ </tex-math></inline-formula>, and length <inline-formula> <tex-math notation="LaTeX">$\text{L}_{\textsf {B}}$ </tex-math></inline-formula> of the N-buffer region, respectively. As examples, 1000- and 1500-V semi-SJ devices are designed with the formulas. The calculated results are in a good agreement with the simulations. The comparisons between the simulations and the existing experiments demonstrate that the optimization in this paper realizes lower <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ \mathrm{\scriptscriptstyle ON},\textsf {sp}}$ </tex-math></inline-formula> under the same breakdown voltage <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {B}}$ </tex-math></inline-formula> even with a shorter SJ length.
Decentralized Coordinated Control of AC/DC Power Interface Via Adaptive Inverse Theory

With the large-scale access of new energy sources, the fluctuation and intermittence of new energy output are becoming more and more serious. Microgrid provides a new technical approach for large-scale application of renewable energy grid connected power generation. Because of the popularity of distributed power supply growth and DC load, single AC microgrid or DC microgrid are gradually unable to meet the requirements of power supply, and AC/DC hybrid microgrid integrated AC microgrid and DC microgrid advantages, has become one of the development trend of microgrid. The AC/DC power interface, which is composed of multiple parallel AC/DC bidirectional converters(BC), is very important to maintain the dynamic balance of power in the hybrid microgrid. This paper establishes a multi BCs mathematical model, and then put forward the decentralized coordination control strategy based on the theory of adaptive inverse control, realized in the maintenance of AC and DC bus voltage constant, the precise distribution of each converter power according to the rated capacity. Finally, the correctness and feasibility of the proposed control strategy are verified by MATLAB simulation platform.
Capacity Planning Model with CSP and Battery

Thermal storage in Concentrating Solar Power plants (CSP) and battery storage are still relatively expensive options to support renewable energy generation, but may offer significant flexibility benefits. This paper discusses a new formulation for embedding all three typical sub-components of a CSP plant (namely, solar field, storage, and power block in a solar tower configuration) in a long-term expansion planning model so that these are co-optimized. The proposed formulation can be accommodated in standard linear/mixed-integer linear programming (LP/MIP) models. A set of two illustrative case studies is used to demonstrate how selection of CSP capacity may be optimized using the proposed formulation, and how it may be influenced by factors such as: its ability to provide spinning reserve, presence of competing options such as a combination of PV and battery storage, and policy targets on renewables.
Using Personality Traits Information from Social Media for Music Recommendation

Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content, the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.
On Symmetry Properties of Intrinsic Interference in FBMC-OQAM Systems

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is a modulation technique which is widely considered as a strong contender to replace cyclic prefixed orthogonal frequency division multiplexing (CP-OFDM), for the future 5th generation (5G) of mobile wireless communication networks. FBMC-OQAM exhibits good spectral confinement properties, high spectral efficiency and immunity to synchronization errors, mainly due to its prototype filter’s localization properties. But these desirable traits come with the loss of orthogonality, rendering it only orthogonal for real symbols with imaginary interference appearing in both the frequency and time domains. In this work, we prove a set of symmetry properties of this interference for a general class of FMBC-OQAM systems, and use these relationships to derive autocorrelation matrices for interference from neighboring symbols and also for noise contributions. These matrices find application in receiver design and also in channel estimation. The derived symmetry properties are also demonstrated through numerical examples for various standard prototype filter designs.
An Experimental Testbed for Managing BAN Services at the Network Edge

In this article we investigate how to support Intraor On-Body Area Network (BAN) applications with strict delay requirements using a network edge architecture. By using an SDN/NFV approach integrated with a mobile system it is possible to transmit a multimedia stream generated inside the human body to a mobile device, which then relays the information to the cloud for further processing. Therefore, we propose an edge approach to reduce overheads, thus making the support of time-constrained medical applications feasible. The proposed architecture has been implemented in a real testbed and the performance of the system were assessed to prove its feasibility.
Dynamic Switching of Two Degree-of-Freedom Control for Belt-Driven Servomechanism

This paper proposes a new two degree-of-freedom control structure such that the robustness control of a belt-driven servomechanism is adequately addressed. In conventional design approaches, a trade-off between robust stability and robust tracking performance is unavoidable because the control engineer must take different frequency regions into consideration. In this paper, a frequency-dependent switching control structure is proposed, where the feedback connection to the external-loop controller is dynamically switched between the outputs of the controlled plant and its nominal model. The disturbance attenuation at lower frequencies by the double-loop control, as well as the robust stability at higher frequencies through the reference feedforward control, can be achieved at one fixed two degree-of-freedom control structure. The feasibility of the proposed approach is verified by theoretical analysis and experimental results.
Transient Stability Study Incorporating Weather Effects on Conductors

Transient stability study is essential in determining stability limits of a power system. Classical transient stability study of Single Machine Infinite Bus (SMIB) system neglects line conductor resistance and therefore does not encapsulate realistic scenario of power system operation. Moreover, the line conductor resistances which are classically considered to be fixed during power flow analysis also do not represent realistic operation. In this paper, the weather dependence of the resistance of line conductors are taken into account to perform transient stability analysis on a SMIB system. Different conductors were considered in the study with varying lengths to generate better understanding of transient stability under realistic environmental conditions. Simulations carried out show the importance of incorporating weather effects of conductors in power system transient stability study.
Adaptive Collision Avoidance Using Road Friction Information

Technical development with the goal of achieving zero accidents and zero fatalities is ongoing. The autonomous emergency braking systems that debuted in the late 2000s have proven their value regarding improved safety. However, the technology still presents many challenges because it is not easy to ensure that the system will operate as intended in any environment and at any time. Any system that is unaware of its environment is prone to be excessively conservative, which could adversely affect the efficacy of said system. Situation awareness is a key to resolving this problem. The present study suggests the use of warning braking to gain an awareness of the level of road friction, which is one of the major uncertainties faced on the road. During warning braking, the tire-road maximum friction coefficient is estimated in real time, and a threat assessment is performed adaptively based on the friction information. Because warning braking is momentary and applied with limited dynamics due to issues related to human factors, this study discusses the major considerations and requirements for the key parameters related to warning braking. The performance of the suggested adaptive collision avoidance scheme is verified by means of simulation and experiments.
Correlation of Defect Luminescence and Recombination in Multicrystalline Silicon

Correlations between defect-related luminescence (DRL) and recombination mechanisms of multicrystalline silicon wafers are investigated by hyperspectral photoluminescence (PL) imaging at cryogenic temperatures (∼80 K) and by PL-based techniques for charge carrier lifetime at room temperature. This unique combination of measurement techniques is used to spectrally compare the DRL in n-type and p-type wafers and to investigate the DRL as a function of block height in a p-type block. Further, the dependence of DRL on interstitial and precipitated metallic impurities has been investigated by comparison of simulated concentration profiles of interstitial and precipitated iron with the spatial distribution of DRL. Our results indicate that the origins of the dislocation-related emission lines (D-lines) are independent of the doping type and suggest that the spectral shape, rather, is determined by the dominating recombination mechanism in the material. In regions with high structural defect density, we observe increased intensities of the D-lines D1–D4 in the DRL spectrum. In regions with a high concentration of either iron or other metallic precipitates, we observe reduced emission intensities of D3 and D4. It is, thus, likely that precipitates of either iron or other impurities partly supress the D3 and D4 emission intensities.