Automated optimization for broadband flat-gain antenna designs with artificial neural network

An automated optimization process for designing and optimising high-performance single microstrip antennas is presented. It consists of the successive use of two optimization methods, bottom-up optimization (BUO) and Bayesian optimization (BO), which are applied sequentially, resulting in electromagnetic (EM)-based artificial neural network modelling. The BUO method is applied for the initial design of the structure of the antennas whereas the BO approach is successively implemented to predict suitable dimensional parameters, leading to broadband, high flat-gain antennas. The optimization process is performed automatically with the combination of an electronic design automation tool and a numerical analyser. The proposed method is easy to use; it allows one to perform the design with little experience, because both structure modelling and sizing are performed automatically. To verify the power of the proposed EM-based method experimentally, two single microstrip antennas have been designed, optimised, fabricated, and measured. The first antenna has flat-gain performance (6.9–7.2 dB) in a frequency band of 8.8–10 GHz. The second has been designed to perform in the 8.7- to 10-GHz band, where it exhibits flat-gain performance with reduced fluctuation in the range of 6.7–7 dB. The experimental results are in good agreement with the numerical data

Optimization for wideband linear array antenna through bottom-up method

This paper presents an automated design methodology for electromagnetic- based (EM-based) optimization of an array antenna by applying bottom-up approach. Firstly, one single antenna is optimized then bottom-up optimization (BUO) method has been implemented by increasing the number of single antennas, sequentially. The proposed method leads to automatically find an optimal array by setting the distance between single antennas. The optimization method is performed in an automated environment with the help of an electronic design automation (EDA) tool and a numerical analyzer. The results of the final design have been compared by means of two EDA tools such as ADS and HFSS. The optimized array antenna works in the frequency band from 12.9 GHz to 14.3 GHz. It offers a linear gain performance higher than 7.5 dB. The simulations in both ADS and HFSS tools illustrate a good match in S-parameter and gain simulation output results

The Highly Miniaturised Radiation Monitor

We present the design and preliminary calibration results of a novel highly miniaturised particle radiation monitor (HMRM) for spacecraft use. The HMRM device comprises a telescopic configuration of active pixel sensors enclosed in a titanium shield, with an estimated total mass of 52 g and volume of 15 cm$^3$. The monitor is intended to provide real-time dosimetry and identification of energetic charged particles in fluxes of up to 10$^8$ cm$^{-2}$ s$^{-1}$ (omnidirectional). Achieving this capability with such a small instrument could open new prospects for radiation detection in space.Comment: 17 pages, 15 figure

String-based audiovisual fusion of behavioural events for the assessment of dimensional affect

The automatic assessment of affect is mostly based on feature-level approaches, such as distances between facial points or prosodic and spectral information when it comes to audiovisual analysis. However, it is known and intuitive that behavioural events such as smiles, head shakes or laughter and sighs also bear highly relevant information regarding a subject's affective display. Accordingly, we propose a novel string-based prediction approach to fuse such events and to predict human affect in a continuous dimensional space. Extensive analysis and evaluation has been conducted using the newly released SEMAINE database of human-to-agent communication. For a thorough understanding of the obtained results, we provide additional benchmarks by more conventional feature-level modelling, and compare these and the string-based approach to fusion of signal-based features and string-based events. Our experimental results show that the proposed string-based approach is the best performing approach for automatic prediction of Valence and Expectation dimensions, and improves prediction performance for the other dimensions when combined with at least acoustic signal-based features

Comparison of Nutrition Status and Knowledge Level of Sports Trainers and Individuals Attended with Nine-Round Fitness Sports

The aim of this study was to determine and evaluate the nutritional knowledge levels and nutritional status of the sports trainers and individuals doing nine-round fitness sports, and compare the relationship between two groups. One hundred individuals doing sports and 10 trainers were included in the study. A questionnaire about general demographic information, anthropometric measurements, nutritional habits and sporting status were applied to the participants. In addition, 1-day retrospective food consumption record was taken to evaluate the intake levels of macro and micronutrients, and The Basic Nutrition Knowledge Level for Adults (YETBID) Scale was applied to measure the nutritional knowledge of the participants. The mean energy (kcal), protein (g), fat (g) and carbohydrate (g) intakes were found to be 1930±832,6, 117,8±61,3, 88±34,5, 159±111,1 for trainers and 1465±533, 81,8±33,9, 66,6±26,4, 128±76,9 for individuals doing sports, respectively. A significant difference was found between two groups in terms of energy intakes (p=0,042) whereas, no significant difference was found between the levels of macro nutrient intakes between trainers and individuals doing sports (p>0.05). According to the YETBID scores, it was found that the total score of individuals doing sports was significantly higher than trainers’ (p=0.037). It was concluded that the level of nutritional knowledge of sports trainers and individuals doing sports did not affect the nutritional status. Furthermore, dietitians should be present at sport centers and nutrition trainings should be arranged in order to prevent improper nutrition practices

Signal and Noise Modeling of Microwave Transistors Using Characteristic Support Vector-based Sparse Regression

In this work, an accurate and reliable S- and Noise (N) - parameter black-box models for a microwave transistor are constructed based on the sparse regression using the Support Vector Regression Machine (SVRM) as a nonlinear extrapolator trained by the data measured at the typical bias currents belonging to only a single bias voltage in the middle region of the device operation domain of (VDS/VCE, IDS/IC, f). SVRMs are novel learning machines combining the convex optimization theory with the generalization and therefore they guarantee the global minimum and the sparse solution which can be expressed as a continuous function of the input variables using a subset of the training data so called Support Vector (SV)s. Thus magnitude and phase of each S- or N- parameter are expressed analytically valid in the wide range of device operation domain in terms of the Characteristic SVs obtained from the substantially reduced measured data. The proposed method is implemented successfully to modelling of the two LNA transistors ATF-551M4 and VMMK 1225 with their large operation domains and the comparative error-metric analysis is given in details with the counterpart method Generalized Regression Neural Network GRNN. It can be concluded that the Characteristic Support Vector based-sparse regression is an accurate and reliable method for the black-box signal and noise modelling of microwave transistors that extrapolates a reduced amount of training data consisting of the S- and N- data measured at the typical bias currents belonging to only a middle bias voltage in the form of continuous functions into the wide operation range

Использование технологии критического мышления в обучении слушателей английскому языку на примере учебной дисциплины "Чтение иноязычных текстов"

Разработан сценарий практического занятия для дисциплины "Чтение иноязычных текстов" для слушателей 2 ступени обучения (английский язык) в рамках программы повышения квалификации ППС на основе базовой модели технологии критического мышления, проведен анализ и сопоставление целей и задач базовой модели технологии критического мышления и дисциплины "Чтение иноязычных текстов"

Bi-allelic GAD1 variants cause a neonatal onset syndromic developmental and epileptic encephalopathy.

Developmental and epileptic encephalopathies are a heterogeneous group of early-onset epilepsy syndromes dramatically impairing neurodevelopment. Modern genomic technologies have revealed a number of monogenic origins and opened the door to therapeutic hopes. Here we describe a new syndromic developmental and epileptic encephalopathy caused by bi-allelic loss-of-function variants in GAD1, as presented by 11 patients from six independent consanguineous families. Seizure onset occurred in the first 2 months of life in all patients. All 10 patients, from whom early disease history was available, presented with seizure onset in the first month of life, mainly consisting of epileptic spasms or myoclonic seizures. Early EEG showed suppression-burst or pattern of burst attenuation or hypsarrhythmia if only recorded in the post-neonatal period. Eight patients had joint contractures and/or pes equinovarus. Seven patients presented a cleft palate and two also had an omphalocele, reproducing the phenotype of the knockout Gad1-/- mouse model. Four patients died before 4 years of age. GAD1 encodes the glutamate decarboxylase enzyme GAD67, a critical actor of the γ-aminobutyric acid (GABA) metabolism as it catalyses the decarboxylation of glutamic acid to form GABA. Our findings evoke a novel syndrome related to GAD67 deficiency, characterized by the unique association of developmental and epileptic encephalopathies, cleft palate, joint contractures and/or omphalocele

Low Intensity Vibrations Augment Mesenchymal Stem Cell Proliferation and Differentiation Capacity During \u3ci\u3ein vitro\u3c/i\u3e Expansion

A primary component of exercise, mechanical signals, when applied in the form of low intensity vibration (LIV), increases mesenchymal stem cell (MSC) osteogenesis and proliferation. While it is generally accepted that exercise effectively combats the deleterious effects of aging in the musculoskeletal system, how long-term exercise affects stem cell aging, which is typified by reduced proliferative and differentiative capacity, is not well explored. As a first step in understanding the effect of long-term application of mechanical signals on stem cell function, we investigated the effect of LIV during in vitro expansion of MSCs. Primary MSCs were subjected to either a control or to a twice-daily LIV regimen for up to sixty cell passages (P60) under in vitro cell expansion conditions. LIV effects were assessed at both early passage (EP) and late passage (LP). At the end of the experiment, P60 cultures exposed to LIV maintained a 28% increase of cell doubling and a 39% reduction in senescence-associated β-galactosidase activity (p \u3c 0.01) but no changes in telomere lengths and p16INK4a levels were observed. Prolonged culture-associated decreases in osteogenic and adipogenic capacity were partially protected by LIV in both EP and LP groups (p \u3c 0.05). Mass spectroscopy of late passage MSC indicated a synergistic decrease of actin and microtubule cytoskeleton-associated proteins in both control and LIV groups while LIV induced a recovery of proteins associated with oxidative reductase activity. In summary, our findings show that the application of long-term mechanical challenge (+LIV) during in vitro expansion of MSCs for sixty passages significantly alters MSC proliferation, differentiation and structure. This suggests LIV as a potential tool to investigate the role of physical activity during aging