Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of PID and Fuzzy-PID Controllers for Agile Eye Spherical Parallel Manipulator

In this paper, the spherical three-degree-of-freedom parallel manipulator has been called Agile Eye which is designed in MCS.ADMAS as an analysis software for defining the constraints and variables. Then, forward kinematic equations of the Agile Eye spherical parallel manipulator are obtained. After that, the block diagram of ADAMS is used for controlling the Agile Eye spherical parallel manipulator with MATLAB software. The input signals of the ADAMS block are controlled torque to the lower links afterward the angle lower links and the end effector position of the ADAMS block will be obtained. Controllers PD, PID, and Fuzzy-PID are designed for the Agile Eye Spherical Parallel Manipulator. Then, the outcomes which have been acquired from the controllers are looking at and the best execution which will be presented. The system responses of the Fuzzy PID controller with a path planning as input for each lower links is computed which is includes a comparison between the reference model and the system responses. Finally, the position of the End Effector will be obtained.
Strengthening of RC Framed Structure Using Energy Dissipation Devices

A large numbers of existing buildings in India are severely deficient against earthquake forces and the number of such buildings is growing very rapidly. This paper presents a way of using energy dissipation devices for seismic strengthening of a RC framed structure. The objective was to improve the seismic performance of the building to resist the earthquake. The viscous dampers are used as an energy dissipation device in the form of single, double, inverted V, V type of dampers with different percentages of damping such as 10%, 20% and 30% to prevent building from collapse in a major earthquake and also to control the damage during earthquake. The performance of the buildings is assessed as per the procedure prescribed in ATC-40 and FEMA 356. Keywords: seismic performance, energy dissipation devices, non-linear static analysis.
On the existence of curves in $ℙ^n$ with stable normal bundle

We prove that for integers n, d, g such that n ≥ 4, g ≥ 2n and d ≥ 2g + 3n+ 1, the general (smooth) curve C in P with degree d and genus g has a stable normal bundle NC . Introduction. Let C be a smooth projective curve. It is natural to ask for which triples (n, d, g) of integers there exist smooth curves C in P of degree d and genus g with a stable normal bundle NC . For n = 3, Ellingsrud and Hirschowitz proved in [9] that there exist a lot of space smooth curves having a stable normal bundle. Here, for n ≥ 4, we will prove the following result: Theorem 1. Let n, d, g be integers with n ≥ 4, g ≥ 2n and d ≥ 2g + 3n + 1. Then the general (smooth) curve C ⊂ P of degree d and genus g has a stable normal bundle NC . As in [9], we use smoothable reducible nodal curves X having a stable normal bundle NX . Of course for n > 3 the study of stability of the normal bundle NX is more complicated than in the case n = 3. The normal bundle of a general rational curve D ⊂ P of degree d ≥ n and the normal bundle of a linearly normal elliptic curve Y ⊂ P of degree n + 1 are well known (see e.g. [15] and [5]). Therefore we use a nodal curveX whose irreducible components are linearly normal elliptic curves and rational curves. Bundles on rational and elliptic curves are rather familiar. For bundles on elliptic curves we also use a recent result obtained in [2]. To check the stability of a vector bundle on a reducible nodal curve X we use a result of [3]. We work over an algebraically closed field k with char(k) = 0. 1991 Mathematics Subject Classification: 14H10, 14H60, 14C05.
A Compact Multiband Metamaterial-Based Antenna for WLAN and WiMAX Applications

A printed multiband monopole antenna consists of four composite right/left-handed (CRLH) metamaterial unit cells is presented and discussed in this paper. Employing such CRLH metamaterial loadings have considerably reduced antenna size and increased antenna gain. The proposed antenna has a compact size of 30×25×1.6mm3 and provides multiband operation at 2.5, 4.1, 5.25 and 6.1 GHz, with reflection coefficient of better than -10dB which are within the practical wireless frequency bands. The proposed antenna has been fabricated and experimentally tested and good agreements between measured and simulated results can be observed.
PIPULS: Predicting I/O Patterns Using LSTM in Storage Systems

Accurately predicting storage I/O patterns can help improving performance and endurance of flash memory SSDs. While existing studies made efforts in using machine learning to predict I/O behaviors, long latency limits wide adoption of such techniques. In this paper, we propose an efficient LSTM (Long Short-Term Memory) neural network solution to detect I/O intensities and idle periods inside storage device in real time, referred to as PIPULS (Predicting I/O Patterns Using LSTM in Storage). PIPULS is a supervised learning model that accurately and efficiently predicts I/O behaviors in SSD storage. We have built a prototype PIPULS consisting of an FPGA implementation for testing phase and a software module for training phase. The prototype PIPULS has been deployed in an NVM-express SSD platform for real-time I/O predictions. Extensive experiments have been carried out using real-world I/O traces to demonstrate the feasibility and performance of PIPULS in NVM-e SSD storage. Our experimental results show that PIPULS model can predict future I/O intensities with high accuracy (correlation coefficient = 92%). The run time latency is less than 2us. It takes 0.5MB of FPGA block RAM usage and 5% hardware resource utilization in the Xilinx VU9P FPGA implementation.
Wideband Sub-Wavelength Orbital Angular Momentum Reflectarray Antenna

A circular aperture wideband reflectarray antenna (RA) with orbital angular momentum (OAM) pattern is analyzed and designed. The proposed subwavelength element includes two circular, co-centered rings with variable diameter. While the outmost diameter remains constant, the radii of inner and outer inner ring gradually advance in the opposite directions. This design process in the proposed multi-resonance element reduces the slope of the phase-length curve and element loss. As a result, the efficiency and bandwidth of the antenna increase. Two structures with linear polarization OAM RA have been designed which the first one is a center-fed center-beam with mode (l) +1 and the second is an offset-fed offset-beam with l=+2. Results indicate the maximum gain of 21.4dBi at 10.5 GHz with 31% 1dB gain bandwidth for l=+1 OAM RA and peak gain of 18.9 dBi at 10.4 GHz with 30% 1dB gain bandwidth for l=+2 OAM RA.
Output Feedback Linearization-Adaptive Sliding Mode Control of TORA System

Translational Oscillator with a Rotational Actuator (TORA) system is known as a nonlinear benchmark with fourth-order dynamics. This system consists of an eccentric rotational proof-mass and a translational oscillator while they have nonlinear interactions together. This paper proposes an Output Feedback-Adaptive Sliding Mode Control (OFL-ASMC) for TORA as an underactuated system. The control law is constructed based on output feedback linearization and a sliding mode controller with an adaptive law. The most important characteristic of the proposed control scheme is its inherent robustness and ability to handle the nonlinear behavior of the system. The effectiveness of the designed control scheme for TORA system is verified by MATLAB/Simulink simulations.
Electropneumatic design and simulation for optimal energy and medication use in air jet medical nebulizers

Respiratory disorders cost billions of dollars for healthcare systems worldwide. Air medical jet nebulizers are class II medical devices used to treat respiratory disorders by delivering mist medication to the lungs. Being respiratory drug delivery devices, in-market existing medical nebulizers are far from operating optimally. The main limitation of the existing medical nebulizers is the excessive generation of medicine in a useless way; leading to a loss of the majority of the medicine generated, i.e., 80 to 90 % is thrown out to the environment without making use of it, while exposing the surrounding environment by the unwanted medicine. This paper proposes a new electropneumatic design for air jet medical nebulizers that involves a closed feedback loop to automatically adjust the air compression, medication delivery, and to patient flow control, simultaneously. For instance, a theoretical model is proposed and a set of simulation trials are performed in order to evaluate the model performance in power consumption and efficiency based on common respiratory diseases; The obtained results illustrate the reduction of medication use by more than 80% and reduction in the power consumption between 18% - 44% when compared to the conventional nebulizer, depending on the breathing pattern adopted. We found that a more sophisticated design of the nebulizers could overcome the ergonomic, environmental, and economical concerns facing current nebulization in clinical respiratory care.
Polar Codes With Repeating Bits and the Construction by Cluster Pairwise Error Probability

A polar code with repeating bits (RB polar code), where a part of frozen bits only repeat their preceding information bits, is proposed. In the code construction, we propose cluster pairwise error probability (CPEP) to measure the competitiveness of the correct path against the error paths and apply CPEP as the optimization criterion for the code construction, aiming to reduce the elimination probability of the correct path in a successive cancellation list (SCL) decoder. In the simulations, the constructed RB polar codes are combined with the cyclic redundancy check (CRC) code and outperform their counterparts of CRC-concatenated polar codes over various codeword lengths, code rates, and puncturing patterns.
Thermal performance and reliability of high temperature SiC power module with direct-cooled stacked Si3N4 substrates

This work is concerned with the thermal performance and reliability of the SiC power module with direct-cooled stacked Si3N4 substrates for high temperature applications. First, finite element thermal and thermo-mechanical simulations have been carried out on the half bridge switch-like module accommodating 4 SiC diodes of 20 A-3.3 kV. The simulation results can be used to justify the selections of direct-cooled stacked Si3N4 substrates and high temperature die attachments in terms of good thermal performance and thermo-mechanical reliability. Then the corresponding half bridge switch-like module samples have been prepared using high temperature Pb5Sn, Zn5Al0.1Ge and sintered Ag joints to attach SiC diodes on commercially available stacked Si3N4 substrates for transient thermal and thermal cycling tests. The test results cannot compare the thermal performance and thermo-mechanical reliability of the different high temperature die attachments, but reveal unusually high contribution and rapid increase of the thermal resistance from the brazed AgCuInTi joint in the commercial stacked Si3N4 substrates. Further work is needed and ongoing for providing better understanding, while the results obtained so far reveal that new stacked substrates especially new brazing or bonding technology for producing the stacked substrates are required for the relevant SiC power module applications.
Campus Energy usage modelling for a South African Academic Institution

When considering buildings of academic institutions, various drivers often impact the building’s energy consumption. This paper evaluates the influence of average temperature, work days, non-work days, and monthly active hours towards the energy usage of selected campus buildings. Various existing models are utilized to evaluate the sensitivity and influence of each of these drivers on the buildings energy usage. These models include: linear two variable and multivariable regression, exponential regression and polynomial regression. The dataset selected consists of energy data from selected campus buildings of a South African University, during the 2017 academic year. The buildings were clustered into three categories, namely libraries, student centers and laboratories. The results of this study revealed that the models developed using polynomial regression produced coefficient of determination (R2) values ranging from 0.48 to 0.9 in the case of active monthly hours, 0.32–0.70 in the case of average temperature and 0.21–0.8 in the case of workdays and non-workdays, which were the highest model accuracies when compared to those of other models. Analysis of the results also revealed that certain drivers such as active monthly hours had a greater correlation to the building energy usage over other drivers. The final model developed (A linear multivariable regression model) achieved an R2 value of: 0.9 (student centers), 0.85 (libraries) and 0.89 (laboratories), indicating the models high accuracy in predicting the dependent variable (energy consumption) when using all the drivers indicated as independent variables to the model.
A Solution for Interoperability in Crisis Management

To effectively manage a crisis, is necessary the participation of multiple agencies related to protection and public safety, which allow actions in accordance with the demands of environment and requirements of all those affected. Interoperability is the key to comprehensive and comprehensive Crisis Management (CM), that allow to face any type of disaster, at any time or place. For this, it is necessary the permanent exchange of information that enables all the agencies involved, coordinate its operations and collaborate to manage the situation in the best way possible. This article describes the approach to interoperability in CM carried out by Secure European Common Information Space for the Interoperability of First Responders and Police (SECTOR) European Project, which has one of its main objectives, the development of an interoperability platform that allows the agencies involved in the management of a crisis, to achieve a joint, coordinated and collaborative response. The platform has a core, a Common Information Space (CIS), which manage as a single storage entity, all information of the Information Systems (ISs) connected to the platform, regardless of the model of data and computer applications used by each one of them.
Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.
Evaluation of the application of bovine, ovine and caprine SNP chips to dromedary genotyping

The lack of SNP arrays for less traditional livestock species has led researchers to explore the possibility of applying the use of commercial SNPs already detected in other related livestock species. For example, the medium density bovine and ovine SNP chips have been successfully used to detect polymorphic SNPs in several other species. The dromedary camel belongs to the Camelidae family and represents an important source for production of milk, transportation and meat in several countries. Until now, no SNP chip has been developed for the dromedary and genome information is still incomplete when compared to other livestock species. In this study, 8 to 17 dromedary samples were genotyped using 3 different ruminant species SNP chips: the Bovine 777K SNP BeadChip, the Ovine 600K SNP BeadChip, and the Caprine 50K SNP BeadChip. The quality of hybridization was assessed considering the average value between the 10%GC and the 50%GC (averageGC) scores and the call rate. Only SNPs with averageGC >0.70 and call rate=1.0 were considered. A total of 29,900 Bovine and 14,179 Ovine SNPs were considered successfully genotyped while none of the Caprine SNPs were qualified for further analysis. Among these SNPs, only 27,585 for the bovine and only 88 for the ovine were polymorphic in our dromedary dataset. These SNPs could represent the first step towards the development of a SNP chip useful for this camelid species after appropriate validations.
A Neuromorphic-Hardware Oriented Bio-Plausible Online-Learning Spiking Neural Network Model

Neuromorphic hardware inspired by the brain has attracted much attention for its advanced information processing concept. However, implementing online learning in the neuromorphic chip is still challenging. In this paper, we present a bio-plausible online-learning spiking neural network (SNN) model for hardware implementation. The SNN consists of an input layer, an excitatory layer, and an inhibitory layer. To save resource cost and accelerate information processing speed during hardware implementation, online learning based on the spiking neural model is realized by trace-based spiking-timing-dependent plasticity (STDP). Neuron and synapse activities are digitalized, and decay behaviors of neuron and synapse parameters are realized by the bit-shift operation. After learning training set from the Modified National Institute of Standards and Technology (MNIST), the spiking neural model successfully recognizes the digits from the MNIST test set, showing the feasibility and capability of the model. The recognition accuracy increases significantly from 90.0% to 94.5% with the number of the excitatory/inhibitory neurons rising from 400 to 3,500, which provides a guide to make a trade-off between the recognition accuracy and the resource cost during hardware implementation. Encouragingly, compared to its corresponding floating-point model, the proposed model reduces the hardware resources and power consumption by 40.7% and 36.3%, respectively (under 55-nm CMOS process).
Cognitive Smart Cities and Deep Learning: A Classification Framework

Deep learning constitute a promising alternative when it comes to providing algorithms that process a big quantity of data and input variables coming from cognitive cities. But the use of deep learning technologies in the cognitive cities domain started only a few years ago; in that regard, there are still many issues that need to be addressed and not enough literature has been developed. This work provides a novel classification framework of cognitive cities using deep learning algorithms that is based in current surveys for known domains such as machine learning and smart cities, but adds the perspective of efficiency, sustainability and resilience that cognitive cities aim to solve.
Diversity and abundance of aerobic anoxygenic phototrophic bacteria in two cyanobacterial bloom-forming lakes in China

Aerobic anoxygenic phototrophic (AAP) bacteria are widely distributed in marine and freshwater ecosystems. The aims of this study were to investigate the diversity and abundance of AAP bacteria in cyano- bacterial bloom-forming eutrophic lakes and to study the association of AAP bacteria with the bloom- forming cyanobacteria. Analysis of pufM gene (the light-reaction center gene) clone libraries indicated that in eutrophic lakes (Lake Taihu and Lake Chaohu, China) with cyanobacterium Microcystis blooms, the AAP bacteria were related to members of Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. In Lake Taihu and Lake Chaohu, Alphaproteobacteria accounted for 81.5% and 75.0% of Microcystis- associated AAP bacteria, respectively, and 84.6% and 72.5% of free-living AAP bacteria, respectively. The predominance of Alphaproteobacteria in the two lakes was different from the previously reported predominance of Betaproteobacteria in freshwater lakes. Quantitative real-time PCR analysis indicated that in Lake Taihu and Lake Chaohu, AAP bacteria represented an important part of the bacterial community associated with Microcystis, and the abundance of Microcystis-associated AAP bacteria (18.3% and 11.7%, respectively) was higher than that of free-living AAP bacteria (5.1% and 7.9%, respectively). The abundance of AAP bacteria in the two bloom-forming lakes was higher than the previously reported level in other eutrophic freshwater bodies.
Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is light-weight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. We further present a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a 384x to 1581x speedup in execution time over the state-of-the-art design.