Miracle-dz/UniFAR-FTUs huggingface dataset:
60GHz front-end receiver chain in 90nm CMOS technology

Operation at millimeter-wave frequency, where up to 7GHz of unlicensed bandwidth is available in the 60GHz band, provides an opportunity to meet the higher data rate demands of wireless users. Advancements in silicon technology permit one to consider exploiting the 60GHz band for commercial applications (e.g., short range, wireless HDTV transmission) for the benefit of end users. This could enable, for example, wireless streaming of uncompressed high-quality video packets of a movie in few seconds due to data rates as high as multi gigabits per second. In this thesis, the design of a receiver front-end circuit for operation in the 60GHz range in 90nm CMOS is described. The thesis includes design details of the blocks used in the receiver, including: quadrature voltage-controlled oscillator (QVCO), local oscillator (LO) buffers, divider chain, low-noise amplifier (LNA) and mixer. The QVCO predicts 56.8-64.8GHz tuning range from schematic simulations. The divider chain has 15GHz locking range at rail-to-rail (0.5V-peak) input signal. The LNA and mixer combination achieves a maximum conversion gain of 26.77dB and a noise figure of 5.88dB. The output -1dB compression point is +6.3dBm and IIP3 is -8.6dBm. The complete front-end consumes 91.7mW from 1V supply. Physical layout of the test circuit and post-layout simulations for the implementation of a test chip including the QVCO and the first stage divider are also presented. Post-layout simulations show a maximum phase noise of -97.4dBc/Hz over 55.4-61.7GHz tuning range.
A generalised SEIRD model with implicit social distancing mechanism: A Bayesian approach for the identification of the spread of COVID-19 with applications in Brazil and Rio de Janeiro state

ABSTRACT We develop a generalized Susceptible--Exposed--Infected--Removed--Dead (SEIRD) model considering social distancing measures to describe the COVID-19 spread in Brazil. We assume uncertain scenarios with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy. A Bayesian framework is proposed for the identification of model parameters and uncertainty quantification of the model outcomes. We identify through sensitivity analysis (SA) that the model parameter related to social distancing measures is one of the most influential. Different relaxation strategies of social distancing measures are then investigated to determine which are viable and less hazardous to the population. The scenario of abrupt social distancing relaxation implemented after the peak of positively diagnosed cases can prolong the epidemic. A more severe scenario occurs if a social distancing relaxation policy is implemented prior to the evidence of epidemiological control, indicating the importance of the appropriate choice of when to start the relaxation.
Position Control Performance Improvement of DTC-SVM for an Induction Motor: Application to Photovoltaic Panel Position

This paper deals with the adaptive sliding mode control DTC-SVM approach dedicated to the position control of photovoltaic panels according to the maximum sunshine position to allow a high efficiency of photovoltaic systems. The paper is devoted to the presentation of a comparison study between three DTC-SVM strategies applied to the position control of an induction motor: (i) a DTC-SVM strategy using PI controllers, (ii) a DTC-SVM strategy using PI controllers with a nonlinear compensator, and (iii) a DTC-SVM strategy using sliding mode controllers. Moreover, using estimations of machine parameters updated online, the robustness with respect to parameter variations of the induction motor is also tested. These performances are confirmed by simulation works.
An integrated obstacle detection framework for intelligent cruise control on motorways

This paper deals with the development and implementation of a purely visual obstacle detection framework for autonomous driving on motorways. Our activities are embedded in the SMART VEHICLE subproject of the ESPRIT project CLEOPATRA. The aim of SMART VEHICLE is the development of a visually controlled intelligent cruise control (ICC) for a prototype passenger car, the Mercedes-Benz research car VITA II. The vision modules are operating concurrently on a net of digital signal processors with multiple video inputs. Our obstacle detection framework bases on the application of highly adapted machine-vision elements such as robust symmetry measuring, neural net-based adaptive object recognition, real-time tracking of multiple vehicles, and inverse-perspective stereo image matching (IPM). We will show detailed results from extensive closed-loop autonomous driving on public motorways and we will present the final HPC hardware system which is part of the application computer of VITA II.
The Poly(chloro-p-xylylene)-Ag Metal-Polymer Nanocomposites Obtained by Controlled Vapor-Phase Synthesis for SERS Effect Realisation

Substrates based on the metal-polymer nanocomposites 2,3-dichloro-p-xylylene-silver (PCPX-Ag) that realize the effect of surface-enhanced Raman scattering (SERS) were developed. To obtain nanocomposites, the vapor-phase polymerization method was used (VDP), which makes it possible to control the nanocomposite microstructure. In the process of self-assembly during VDP, nanocomposite films with inclusions of silver particles were formed on the polycore substrates. Silver content varied from 2.5 to 16% vol. The possibility of using such substrates for the detection of low-molecular substances, for example 5,5′-dithiobis- (2-nitrobenzoic acid) (DTNB) analyte, by the SERS method with an enhancement factor of up to 104, was demonstrated. The dependence of the SERS spectra enhancement on the microstructure of the nanocomposite and the silver content was determined. The optical and morphological properties of nanocomposites were also investigated and their dependence on the silver content was shown. It has been demonstrated that the nanocomposite is SERS selective since when working with complex solutions in the presence of high molecular weight substances, signal enhancement was only observed for low molecular weight substances.
Zinc Chloride Supplemented during Ovarian Tissue Vitrification Improves In Vitro Follicle Development and Fertilization in Pigs

Ovarian tissue vitrification is a promising method to preserve follicles and gametes, but can be improved with mineral supplementation to the vitrification medium. The objective of this study was to determine the effects of supplementing 0.5 mg/mL zinc chloride to the media during cryopreservation of pig ovarian tissue. After thawing, the following criteria were evaluated: (1) follicular development and damage, (2) in vitro fertilization (IVF) characteristics, and (3) embryonic development. The number of damaged antral follicles (72.0 ± 3.8%) was less (p < 0.05) in ovarian tissue vitrified in media supplemented with zinc chloride compared to those not supplemented with zinc chloride (86.7 ± 4.1%). Oocytes obtained from the antral follicles on ovarian tissue vitrified in media supplemented with zinc chloride had less (p < 0.05) polyspermic penetration and higher (p < 0.05) male pronuclear formation during IVF than oocytes obtained from ovarian tissue not supplemented with zinc chloride. There were no statistical differences in embryonic development rates. Based on these results, supplementing zinc chloride during the vitrification protocol improves follicular development and subsequent IVF in pigs.
FUZZY FMEA APPLICATION TO IDENTIFICATION RISK IN-PROCESS PRODUCTION OF TOYOTA HI-ACE WIRING HARNESS PRODUCT

In product manufacture, the high failure rate problem of produce product is the number of product defects. Several types of defects have a high enough percentage. To solve this problem, we need to identify the failures and to get the assessment information of the three risk factors. Our research using the traditional FMEA method at the production of Wiring Harness products to shows the current condition of various modes of failure in those areas. This study focuses on implementing fuzzy FMEA to identify the potential risks that may occur along with the assembling of the Wiring Harness process. The fuzzy FMEA approach is preventing product and process problems before they occur, this paper is also expected to result in some mitigation effort that can be applied to improve the Wiring Harness production process. With the Fuzzy FMEA method, we have found the highest FRPN value that shows the highest defect such as damage insulation is 8.5, damage terminal is 8.5, and the damaged part is 8.5 and the highest RPN from the traditional FMEA is damage insulation (324). To solve this problem, we propose to use the fishbone diagram and give suggestions for improvements to the highest failure modes that are damaged insulation.
APPLYING DISCRETE COSINE TRANSFORM AND GREY RELATIONAL ANALYSIS TO SURFACE DEFECT DETECTION OF LEDS

ABSTRACT Light-emitting diodes (LEDs), owing to their lower power requirements, higher efficiency, and longer lifetimes, are widely used in modern electronic devices. Nevertheless, tiny defects that often appear in the surface of LEDs impair not only their appearances but also their functions. This paper proposes a global approach for the automated visual inspection of tiny surface defects in non-diffused LED encapsulations. The proposed method, taking advantage of the discrete cosine transform (DCT) and grey relational analysis techniques, overcomes the difficulties of inspecting tiny defects on uneven illumination images of LEDs. We apply the grey relational analysis to the frequency components in the DCT domain, and select the large-magnitude frequency components that represent the background texture of the surface. Then, by reconstructing the frequency matrix without the selected frequency values, we eliminate not only random texture but also uneven illumination patterns and retain anomalies in the restored image. Experimental results demonstrate the effectiveness of the proposed method in inspecting tiny defects in non-diffused LED surfaces.
Network Intrusion Detection Using Class Association Rule Mining Based on Genetic Network Programming

Because of the expansion of the Internet in recent years, computer systems are exposed to an increasing number and type of security threats. How to detect network intrusions effectively becomes an important technique. This paper proposes a class association rule mining approach based on genetic network programming (GNP) for detecting network intrusions. This approach can deal with both discrete and continuous attributes in network‐related data. And it can be flexibly applied to both misuse detection and anomaly detection. Experimental results with KDD99Cup and DARPA98 database from MIT Lincoln Laboratory shows that the proposed method provides a competitive high detection rate (DR) compared to other machine learning techniques. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Synthesis of Isatin Derivatives Using Silver Nanoparticles as Green Catalyst: Study of Molecular Docking Interactions in SARS-CoV-2 3c-Like Protease and Determination of Cytotoxic Activities of the Compounds

The coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As isatin-containing compounds exhibit several remarkable biological activities, isatin derivatives were prepared to combat the global pandemic caused by SARS-CoV-2 that is gripping the world. Herein, the synthesis of novel isatin derivatives has been reported. The cytotoxic activities of the compounds were determined using cancer cell (MCF-7) and normal cell (MCF-10A and MRC-5) lines. In silico molecular docking experiments were conducted using AutoDock Vina. We have successfully predicted the binding energies and the number of hydrogen bonds present. We have also identified the residues involved in hydrogen bond formation. The target compounds were synthesized using Schiff base following cyclization and Knoevenagel condensation reactions. We have focused on the recyclable synthesis of silver nanoparticles (AgNPs) using the extracts obtained from Dipteryx odorata. The extracts were used to reduce silver ions for the production of AgNPs. The synthesized nanoparticles exhibited excellent catalytic activities during the synthesis of isatin derivatives in ethanol. The formation of the target isatin derivatives has been confirmed using the Fourier transform-infrared (FT-IR), proton nuclear magnetic resonance (1H NMR) spectroscopy, 13C NMR spectroscopy, mass spectrometry, and elemental analysis techniques. Compound 3e was found to be the most active compound when tested against the MCF-7 cancer cell line ( I C 50 = 20.5 μ M ). The activity was comparable to the activities of standard doxorubicin and other compounds. In silico molecular docking experiments were conducted to study the spike protein in SARS-CoV-2 (PDB ID: 6LU7). Compound 3c exhibited high binding ability (−9.4 kcal/mol). The inhibition ability was studied using hydroxychloroquine as a standard. Results from docking studies revealed that the inhibition ability of compound 3c was higher than the inhibition abilities exhibited by other compounds. The synthesized compound 3e is a potential antiviral drug that can be used for treating the COVID-19 disease.
Arrayed pH-responsive microvalves controlled by multiphase laminar flow

In chemical and biological assays, the synchronized control of multiple valves is very important. Currently there is no control scheme for the use of a large number of valves without adopting bulky peripheral control devices such as pumps or electrical equipment. Therefore, we propose a simple pH-responsive microvalve array system and a new multiplexing technology that, through its reliance on multiphase laminar flow, facilitates the operation of arrayed multiple microvalves. For the fabrication of the microvalve arrays, we have used pH-responsive hydrogel microspheres produced by a microfluidic chip. By incorporating these microspheres inside an arrayed microvalve polydimethylsiloxane (PDMS) platform, we produced pH-responsive arrayed microvalves with ‘open’ and ‘close’ operations carried out by the multiphase laminar streams of pH solutions. The constructed arrayed microvalves were adequately controlled by the multiphase pH buffer; the pressure of the pH-buffer solutions controlled the widths of the sample flow. Based on the simplicity and reliability of the operating principles, this new arrayed-valve system will decrease the need for complicated peripheral lines and will remove the need for many solenoid valves, which are currently used for the individual control of arrayed valves.
Adaptive Levitation Control Based on Feedback Linearization

This study mainly addresses the adaptive control for the levitation system in presence of mass variation. Firstly, considering the nonlinear of levitation system, the feedback linearization is applied to achieve the global stabilization. Due to the additions of luggage and passengers, the variation of mass degrades the dynamic performance of levitation system, it is necessary to estimate and cancel the effect of mass variation. So an algorithm to estimate the mass of levitation system is proposed and integrated to adjust the parameters on line. The results of simulations show that excellent closed-loop performance is provided in the presence of mass variation.
A new power amplifier topology with series biasing and power combining of transistors

A power amplifier topology was demonstrated in a microwave monolithic integrated circuit (MMIC) implementation with GaAs MESFETs. This topology overcomes several limitations of the traditional approach of paralleling of power transistor unit cells. In the new topology, unit cells are both parallel and series combined. The benefits include higher input and output impedances, broadband power matched interstage networks, and high voltage biasing at reduced DC current. Measured results on a MMIC and a hybrid power amplifier implemented with this technique are presented. <<ETX>>
Voltage controller with recurrent neural networks

An electric power system requires voltage and reactive power control (VQ control) to avoid voltage collapse. The conventional VQ control, however, does not meet this requirement because of approximated control. The authors propose a new algorithm for VQ control using recurrent neural networks which have the ability to treat system dynamics. Firstly, they propose the learning algorithm for dynamics and inverse dynamics of the controlled target. Secondly, they apply this algorithm to the VQ control. The authors call this controller 'neuro VQC'. Finally, the usefulness of the neuro VQC is shown in comparison with the conventional VQ controller. <<ETX>>
The design of erbium-doped fiber amplifiers

An accurate model for the erbium-doped fiber amplifier is presented. The model is used to design the index profile of the doped fiber, optimizing with regard to efficiency for inline- and preamplifiers as well as for power booster amplifiers. The predicted pump efficiencies (maximum gain to pump power ratios) are in agreement with experimental results presented in the literature. The choice of codopant is shown to be very significant for the pump efficiency when pumping in the 0.98 mu m. The pump efficiency in the 0.98- mu m pump band is shown to be twice the pump efficiency in the 1.48- mu m pump band. >
Active control for enhancing vortex induced vibration of a circular cylinder based on deep reinforcement learning

In the current paper, the active flow control for enhancing vortex induced vibration (VIV) of a circular cylinder, which can be potentially applied in ocean energy harvesting, is achieved by an artificial neural network (ANN) trained through deep reinforcement learning (DRL). The flow past a circular cylinder with and without jet control located on the cylinder is numerically investigated using OpenFOAM, and the ANN is applied to learn an active flow control strategy through experimenting with different mass flow rates of the jets. According to our results, the jets on the cylinder are able to dramatically destabilize the periodic shedding of the cylinder wake, which leads to a much larger VIV and work capability of the cylinder. Through controlling the flow rate of the jets based on the observation of the instantaneous flow field, the ANN successfully increases the drag by 30.78%, and the magnitude of the fluctuation of the drag and lift coefficient by 785.71% and 139.62%, respectively, while the energy consumption of the jets is almost negligible. Furthermore, the net energy output by VIV with jet control increases by 357.63% (case of water) compared with the uncontrolled situation. The results demonstrate that the performance of the active jet control strategy established by DRL for enhancing VIV is outstanding and promising for realizing the transformation from the ocean energy to electrical energy. Therefore, it is encouraged to perform further investigations on VIV enhancement using active flow control based on DRL.
Improving the volatility of the optimal weights of the Markowitz model

Abstract The main practical problems that are faced by portfolio optimisation under the Markowitz model are (i) its lower out-of-sample performance than the naive rule, (ii) the resulting asset weights with extreme values, and (iii) the high sensitivity of those asset weights to small changes in the data. In this study, we aim to overcome these problems by using a computation method that shifts the smaller eigenvalues of the covariance matrix to the space that houses the eigenvalue spectrum of a random matrix. We evaluate this new method using a rolling sample approach. We obtain portfolios that show both more stable asset weights and better performance than the rule. We expect that this new computation method will be extended to several problems in portfolio management, thereby improving their consistency and performance.
Perceptive exploration of layout designs using an interactive genetic algorithm

Layout design optimization has a significant impact in the design and use of many engineering products and systems, such as the subdivision of a ship, the layout of facilities in a plant or further still the assembly of parts of a mechanism. The search for an optimal layout configuration is a critical and complex task due to the increasing demands of designers working on varied projects. A layout optimization process is generally divided into different steps: the description, formulation and solving of the problem and the final decision. This process consists in writing an optimization problem that transform designer’s requirements into variables, constraints and objectives. Then, an optimization algorithm has to be used in order to search for optimal solutions that fit with product’s specifications. This paper focuses on the last step which consists, for the designer, in making a choice on the solutions generated by the optimization algorithm. This choice is made according to the global performances of the designs and also the personal judgment of the designer. This judgment is based on the expertise of the designer and the subjective requirements that could not be integrated on the formulation of the problem. This paper proposes a perceptive exploration method, based on an Interactive Genetic Algorithm (IGA), used to explore designs, taking into account the subjective evaluation of the designer. The objective of this method is to select an ideal solution that realizes the best trade-off between the quantitative and qualitative performance criteria. This interactive process is tested on an industrial layout application which deals with the search for an optimal layout of facilities in a shelter.Copyright © 2012 by ASME
Performance optimization in switched reluctance motor drives with online commutation angle control

The problem of performance optimization in current controlled switched reluctance motor (SRM) drives is investigated. Two controllers are proposed that determine the optimal turn-on and turn-off angles, respectively, for improving motor efficiency and torque ripple. The suggested controllers are simple, do not affect the complexity of the drive, and are easily implemented since the knowledge of torque-angle-current characteristics or magnetization curves is not required. The proposed control scheme is demonstrated on a prototype experimental system.
Capacity of Shared Left Turn Lanes—Comparative Analysis

This paper compares the capacities of shared left turn lanes obtained by four methods for varying lane configurations, through and left turn volumes, and traffic signal timings. The 1997 Highway Capacity Manual (HCM), Canadian, SIDRA, and Levinson methods were analyzed for left turn volumes ranging up to 250 vehicles per lane per hour (vph) for both 60- and 90-s cycles, assuming 50% effective green time per cycle. Scenarios were tested assuming both equal and unequal volume. More than 700 individual computations were performed. The methods provide generally consistent patterns for each scenario tested. The 1997 HCM method (unlike the 1994 HCM method) consistently provided higher capacities than the other three methods for single-lane approaches. The SIDRA method consistently provided the lowest capacities on multilane approaches where left turn volumes exceed 100–150 vph. The research suggests further field tests and analyses to see if modifications in left turn equivalency factors for single-approach lan.