Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Characterization of a Solar-Photovoltaic-powered Aquaponic System: A Case Study in Bogotá, Colombia

According to the Food and Agriculture Organization (FAO), the growth in the world's population will increase the challenge of food security. Therefore, a sustainable strategy is required as an option for traditional agriculture, such as aquaponic systems. In the present study, the experimental characterization of an existing aquaponic system at the Universidad de Los Andes, Colombia, is carried out. The aim is to propose a methodology to evaluate the energy viability of these systems when they are continuously power supplied by solar energy and achieve their adequate functioning by responding to the biological needs of the unit. In addition, this system is defined as a Minimum Viable Product (MVP). It is a reference for applying a roadmap to Panama according to climatic conditions. It was obtained as a result that the photovoltaic system (PV) coupled with the aquaponic system responds to the biological needs of the organisms. However, it has opportunities for improvement, such as battery capacity.
Design and Control Implementation of High Power Bidirectional DC-DC Converter for EV Applications using DSP Controller

In a battery electric vehicle (BEV), a High power density DC-DC converter must be used to reduce the size of the EV drive train. However, in high-power applications, inductors are often the largest component in dc-dc converters. The design of an inductor that uses with two different core materials, ferrite and sendust, is studied in this paper. This has been done for the 3kW DC-DC converter, the weight and size of the inductor have been compared. In comparison to the Ferrite core, it has been noted that the area product factor and weight of the Sendust core are both lowered by 22.0% and 23.0%, respectively. In addition, the generated computational delay from the DSP controller in the control loop has been taken into consideration for the design of the control algorithm in a selected microcontroller. The average current mode control (ACMC) has been implemented in the DSP microcontroller (TMS320F28335) including the delay. Also, a 3kW prototype is developed for experimental validation.
Geometric Control of uncertain quadrotor with external disturbances

This paper presents a rotation matrix based controller for quadrotors to represent its attitude globally on SO(3). The controller has been developed for a quadrotor with unknown mass and inertia matrix. The external disturbances as well as the parametric uncertainties have been estimated with an extended state observer (ESO). Mathematical proof shows that the closed loop errors are bounded and attain exponential stability as the gain of the extended state observer approaches infinity. Numerical simulation results have been provided to demonstrate the potency of the proposed controller with observer.
Secure Protocol for VANETs Communication

Vehicular ad hoc Networks (VANETs) are receiving great attention lately from academia and industries as well. So various research is going on covering different aspects of VANETs including security and safety of communication channels. One of the major challenges is preserving the privacy and security of vehicles while communicating with each other. Security becomes challenging when we have to trace the malicious nodes, in the network to punish or revoke malicious nodes, as we have to hide identity of sender during communication. In this article, we propose a secure authentication protocol for communication in VANETs that can achieve desired conditional privacy along with security during transmission of messages.
Latency-Aware Task Partitioning and Resource Allocation in Fog Networks

Smart devices generate various latency-sensitive tasks which need to be processed in real-time or near real-time. The resource-constrained smart devices leverage cloud resources for computing needs. However, the remote geographical position of cloud resources increases the latency. Fog computing provides the computational resources near the smart devices, which can meet the latency demand of many Internet of Things (IoT) applications. Fog computing is a distributed computing paradigm replicating the cloud servers near the origin of the task. However, the fog nodes are also resource-constrained. Hence, resource allocation in fog networks is challenging. Here, we propose a Lagrangian-based task offloading strategy for fog-enabled IoT networks, which partitions the tasks between fog nodes and cloud servers. Moreover, we propose the upper and lower bounds on the required cycle/bit and CPU frequency to allocate the fog node’s resources. The extensive numerical results show that the proposed strategy outperforms the existing baseline algorithms.
Phase-Change Memory using Cu2GeTe3 and Multiple Writing Technique for Neuromorphic Systems

A phase-change memory (PCM) using Cu2GeTe3 (CGT) and multiple writing technique have been developed for neuromorphic systems. First, the PCM using CGT is fabricated. Next, the single writing technique as a conventional technique is tried, but reproducible operation is required. Therefore, the multiple writing technique for reliable operation is proposed, and it is confirmed that the multiple RESET pulses contribute to it. Finally, the associative memory function by the neuromorphic system is validated.
Word Line Underdrive Technique to Increase Signal Swing and Linearity for In-Memory Compute Applications (IMC) in 65nm LSTP technology

The movement of data between memory and compute limits the system’s throughput for machine learning applications due to the Von-Neumann bottleneck. In-memory computation (IMC) has enormous potential to overcome Von-Neumann bottleneck for data-intensive applications where massive data movement occurs between memory and compute units. IMC reduces the latency and and increases the energy efficiency of the system. Mixed signal IMC requires multi-wordline activation. This paper presents the wordline (WL) underdrive and current mirroring techniques to 1) eliminate the spurious write problem and 2) enhance the system’s linearity. 20% WL underdrive increases the linearity of the proposed architecture by 36%. At FS/125°C corner to get a full swing on bitline, nearly 50% reduction in wordline voltage is required to prevent the spurious write operation.
Lateral Human-Structure Interaction in a flexible structure through Hybrid Simulation

Structures such as pedestrian bridges can present dynamic sensitivity problems when subjected to anthropic loads, the mutual influence of these two actors is known as human-structure interaction (HSI). The study of this phenomenon has been carried out through numerical models, which still fail to represent it completely, and through full-scale experimental tests that, despite being faithful, are not feasible for most educational institutions due to their high price, space requirements and large number of personnel and equipment. In this sense, it is proposed in this research to evaluate the lateral HSI in pedestrian bridges using the hybrid simulation. By substructuring the problem into a structural system, which will be the numerical substructure, and a human system, represented by three test subjects, which will be the experimental substructure, lateral HSI tests were performed using HS, with the help of an instrumented treadmill and a shaking table as a transfer system. The response of the pedestrian bridge obtained from the tests indicates that the proposed pedestrian bridge does not present serviceability problems due to the passage of a pedestrian, although it may be close to the maximum accelerations allowed by ISO 10137. It was concluded that the transfer system did a good tracking of the reference signal. In addition, the configuration and the transfer system allowed the realization of a real-time hybrid simulation.
A Duplex Method for Prediction of Concrete Strength Using Dimensionality Reduction

Concrete is a mixture of a hardened particulate material known as aggregate which is fused with water and cement. Concrete is the most used man-made material. Concrete is most commonly used on construction sites while building roads, bridges, dams, etc. Since there are different types of cement, the strength of concrete might vary. In this paper, we have used machine learning regression models like Linear Regression (LR), Lasso Regression (LaR), Ridge Regression (LR), Polynomial Regression (PR), Decision Tree (DT), K–Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting Regression (GBR), ADA Boosting (ADA), Support Vector Machine (SVM) and XG Boost (XGB) to predict the strength of concrete. This paper emphasizes on dual approach for predicting the concrete strength where the first approach is based on consideration of all the features for training and in the second approach, dimensionality reduction is performed using Principal Component Analysis (PCA) technique. In terms of results, XG Boost Regression (XGB) model with all features gave the best R2 score of 0.9206, Mean Squared Error of 20.6790, Root Mean Squared Error of 4.5474 and Mean Absolute Error of 2.8966.
The Young Cluster IC 5146

The B0 V star BD +46°3474 lies near the front surface of a dense molecular cloud and illuminates the emission/reflection nebula IC 5146. The HAeBe variable BD +46°3471 is embedded in the same cloud, about 10′ (3.5 pc) away. CCD photometry in BVRI (to V = 22) and in JHK (to about K = 16.5) has been obtained for the young clusters surrounding each of these two bright stars. Some 100 emission-Hα stars brighter than R = 20.5 have been found in the area, most of them in IC 5146. (Among these are two that have spectra resembling a high-excitation Herbig-Haro [HH] object plus a stellar continuum.) A distance of 1.2 kpc follows from the photometry of several late-type IC 5146 cluster members; the average extinction from 38 stars classified spectroscopically is AV = 3.0 ± 0.2 mag. Although optical photometry is available for 700 stars in the IC 5146 field, only about half (including all the Hα emitters) lie above the main sequence, while a substantial fraction of these are estimated to be foreground. A number of such interlopers have been identified on the basis of proper motion or abnormally low AV. The age distribution of the Hα emitters has been estimated by reference to several sets of theoretical isochrones. There is substantial disagreement, but the median age does appear to be near 1 Myr. The spectrum of +46°3474 is unexceptional except for an unusually low v sin i (10 km s-1), but +46°3471 has a complex emission plus absorption spectrum. Our interpretation of the structure of IC 5146 on the basis of optical and radio radial velocities follows a proposal by Roger & Irwin in 1982, namely, that +46°3474 formed near the near surface of the present cloud and evacuated a blister cavity out of which gas and dust are now flowing through a funnel-shaped volume in the approximate direction of the Sun. It is suggested that the IC 5146 cluster stars formed in a dense foreground section of the molecular cloud that was dissipated following the appearance of +46°3474.
A short communication on meteorological observations at Mt. Everest 6523 m

Mt. Everest is relatively inaccessible and little is known about its meteorology. On 27 April 2005, an automatic weather station was displayed on the north column (28°01′0.95″N, 86°57′48.4″E, 6523 masl) of Mt. Everest. This paper summarizes the meteorological data collected from 1 May to 22 July 2005. The measured variables included long-wave radiation, air temperature, relative humidity and baromteric pressure. The daily record indicates synoptic scale variability in the thermodynamic variables, while short-wave radiation and wind speed decrease gradually at different degree, net radiation changes little with a slight increase, and wind direction turns from north to south.
Development of software projects in thesis using an agile methodology

In spaces of degree work in higher education, the proposed software projects demand the use of so-called agile methodologies, which are based on the principles set forth in the Agile Manifesto and the disciplinary theoretical support of Software Engineering. Methodologies characterized by iterative and incremental development, supported by heuristics, of great acceptability in the dynamics of current software development due to efficient time management; when adopted in the degree works, where academic and administrative commitments of particular connotation are fulfilled, difficulties are evident in the methodological characterization, due to the business orientation precepts of universally recognized agile methodologies. In this sense, the MaTraGra methodology is conceived as a solution to this problem.
Automated Severity Detection Of Chronic Obstructive Pulmonary Disease Using Lung Sounds

Lung sound is a non-invasive diagnostic tool for assessing several respiratory disorders, such as chronic obstructive pulmonary disorder (COPD). Due to the severe implications of COPD, it is essential to distinguish different severity levels of COPD. In this paper, we have utilized 12 channel lung sound data from RespiratoryDatabase@TR to distinguish two extreme severity levels of COPD namely, COPD-0 (lesser risk) and COPD-4 (very severe level). In this study, we have proposed an efficient COPD severity classification framework using variational mode decomposition (VMD); which involves four major stages: (a) preprocessing, (b) signal decomposition using VMD and feature extraction from the decomposed modes, (c) feature selection and ranking, (d) classification using machine learning (ML) classifiers. Employing this proposed technique, we have achieved high classification measures of 97.43%, 100%,93.75% for accuracy, specificity, and sensitivity respectively, which is superior to the only existing work presented by Altan et al.
Continuous trajectory planning of a 6 DoF feeding robotic arm using a novel multi-point LSPB algorithm

The present research paper, discusses about the novel LSPB algorithm for planning a trajectory of a multi-degree of freedom robotic arm for N numbers of intermediate points. In the current research paper, a case study of a 6 DoF indigenous economical 3D printed feeding serial manipulator is taken, which is used to feed aged patients suffering from various neurological disorders. The motive of the MP-LSPB algorithm is to provide an alternative spline trajectory planning method. The spline interpolation method is made of multiple polynomials and increases the complexity of calculations along with the computational load. Apart from this spline trajectory planning method does not provide any cushioning time at the starting and at the end of the trajectory. To overcome this problem, we have come up with a generalized MP-LSPB algorithm with lesser calculations and computational complexity, which provides smoother trajectory planning along with calculated cushioning time at the starting and at the end of the trajectory. The developed algorithm is suitable for any open source microcontroller such as Arduino, Raspberry Pi, STM 32 and PIC. A homogenous trajectory planning algorithm for N numbers of intermediate points was developed, which can provide continuous angular displacement, protects mechanical joints and servo actuators from excessive vibrations, infinite acceleration and instantaneous velocity, and safely deliver the food to the patient, without wasting it. After deploying the MP-LSPB algorithm, the robot was tested successfully over 30 patients for 25 feeding cycles.
Large scale hydrothermal optimal power flow problems based on interior point nonlinear programming

This paper presents an interior point algorithm for hydrothermal optimal power flow problems (HTOPF) which is derived from the perturbed KKT conditions of the primal problem. Moreover, the algorithm is extended successfully to solve approximate HTOPF problems (A-HTOPF) to find a suboptimal solution with much less execution time. For large scale systems, A-HTOPF can reduce CPU time by half and can guarantee more than 99% accuracy in most cases. By properly exchanging rows and columns of a correction equation, the reduced equation with novel 4T/spl times/4T block diagonal submatrices can be derived, where T is the number of time interval. Its topological structure is identical to that of the nodal admittance matrix, thus enabling an efficient algorithm. Numerical tests have been executed on six test systems of up to 1047 buses for HTOPF over 72 time intervals. The computational burden of the maximum test system is equal to that of 75384 (1047/spl times/72) bus OPF problem. The simulation results have verified that the proposed algorithm possesses good convergence property within reasonable execution time, and hence, the algorithm is quite promising for large scale applications.
Predicting Insurance Churn to Reduce Clawback

Many insurance companies still depend on agents for marketing and selling their products. The compensation for these agents is based mainly on commissions and hence such agents try to sell as many policies as possible. In addition to increasing the quantity of policies sold, agents may try to recommend policies with high commissions even if other policies are more suitable for the client. Furthermore, agents may at times ill-advise customers to change their policies in order to gain a new commission even if such a change does not benefit the customer or the insurance company. This last type of transaction is called internal insurance churn or policy churn since the customer remains with the insurance company but they make a policy change. We use Machine Learning techniques to predict policy churn prior to payment of the commission to the agent. This avoids the process of having the agent repay the commission (a process termed clawback) in such cases. We illustrate how the proposed approach can provide significant savings to the insurance company.
University Class Schedule Assignment by a Tabu Search Algorithm

Historically, the University class scheduling problem has been difficult to solve efficiently, due to the long time it takes to find satisfactory solutions. This work seeks to automate the process that solves the following problem: assign to each mathematics class of an academic period of the Universidad Tecnológica de Panamá (Task) a professor from the Department of Exact Sciences (Resource) of said institution. In an academic period, the Department of Exact Sciences has approximately 205 subjects and 71 professors. Due to the huge space of possible solutions, a local search has been chosen, in which iteratively tries to improve the solution through slight changes. The effective implementation of this search requires solving two great challenges. First, define an objective function that allows considering the most important qualities of an assignment, and at the same time control its computational complexity, allowing multiple iterations to be carried out in a reasonable time. Second, toast to the search for a structure. The problem will start from a solution obtained by means of a greedy algorithm, where for each subject, a teacher is assigned so that the lowest value of the objective function is recorded. Subsequently, the search is provided with a Tabu Search metaheuristic that allows it to escape local optima and better control its path. The results obtained show evidence that the metaheuristic works, providing a fast feasible solution that includes the desired characteristics and that serve as a starting point for the career coordinator.
A Machine Learning-Based Approach for Improving TCP Congestion Detection Mechanism in IoTs

TCP provides suboptimal performance when it comes to wireless or mobile networks. End-to-end connectivity with reliability is a big challenge in IoTs that have restricted memory and processor resources. Mainly, TCP was prepared for only wired networks and its performance will be ruined if we applied it on wireless and ad-hoc networks. IoTs have several issues related to TCP that need to be addressed and have been addressed in past. This paper addresses multiple issues that IoT enables an application to face during data transmissions with mobile nodes. Many researchers have proposed approaches based on certain algorithms and machine-learning techniques that have been summarized in this paper. A new algorithm has also been proposed that focuses on the differentiation of the data loss as congestion loss or random loss in a TCP-driven network transmission using an unsupervised machine learning approach. The proposed algorithm is both memory and computation efficient. It is self-evolving and adaptive as well.
GEM: A Generalized Memristor Device Modeling Framework Based on Neural Network for Transient Circuit Simulation

Conventional physics-based memristor device modeling methods highly rely on human expertise, which results in a long development period. To address the aforementioned challenges, we propose a new generalized memristor (GEM) device modeling framework based on the artificial neural network (ANN) technique, which has a minimum dependency on the underlying physics, resulting in a fast turn-around development time for customized memristor devices. GEM framework models the switching and conducting behaviors of the memristor devices separately, avoiding the signal-dependence issue in the prior time-series data modeling method. The result of the GEM framework is a compact model that supports general-purpose circuit simulators. Experimental results show that our compact model achieves a ratio of root-mean-square error to peak-to-peak (RMSE/PP) of 3.6% compared to the physics-based device model. Performance analysis of memristor-based logic and memristor crossbar circuits are conducted to demonstrate the effectiveness of our proposed GEM framework for the design and analysis of memristor-based circuits.
Miniaturized Reflective Cross-Polarization Converter for K-Band Applications

A cross-polarization converter with a new epsilon-shaped unit cell is presented in this work. The structure of the unit cell consists of an arc and a rectangular strip resonator protruded from the center of the arc. When the ratio of the length of rectangular strip (b) to the width of the arc (a) is 5:1 then, the proposed structure exhibits cross polarization conversion from 17.83 - 26.85 GHz (K-band operation) with a polarization conversion ratio (PCR) >95%. Theoretical analysis is provided in this study to justify the simulation results. The incident wave along the y-axis is decomposed into two orthogonal components (u and v) to show that the phase difference (|(ϕvv − ϕuu)|) of ±180° exists between the u and v components due to the asymmetric structure of the polarizer. The reflective cross-polarizer maintains stable polarization conversion up to 40◦ angle of incidence. The proposed polarizer covering K-band and it is suitable for satellite communication applications.
Software-driven Debug Framework for Embedded RISC-V, that Transparently Emulates the Industry Standard Debug Framework

Hardware support for external debug is essential for development and maintenance of embedded softwares. However, external debug module and debug interface are often removed in the production, for optimization. In this paper, we propose a software-driven debug framework for embedded RISC-V system. Our framework transparently emulates the debug hardware, by utilizing one external interrupt and 1.04 KB of memory on the target. The overhead is negligible, compared to the removable debug module and IO pads for the debug interface, which occupy as much as 12.7% and 60.6% area of CPU subsystem, respectively.