Multi-probe Enabled Over-the-air Calibration of Millimeter-wave Antenna Array: Concept and Experimental Validation

Millimeter wave (mmWave) antenna array systems with high-gain beam-steerablecapability play a key role in fulfilling the high data-rate demands of the fifth generation (5G)and beyond wireless technologies. Rigorous array calibration is essential to ensure theirradiation performance fulfills the standard requirements before massive rollout. These testswill exclusively transition to over-the-air (OTA) testing approaches with antennas included,due to the lack of antenna connectors and their compact and highly integrated designsin emerging mmWave radio systems. This has posed huge challenges on measurementand calibration of mmWave antenna arrays, due to the more demanding requirement onsystem complexity, implementation cost, measurement time, and measurement uncertainty.In this work, a multi-probe framework for phased array calibration is introduced, aimingto achieve objectives including measurement range reduction, measurement efficiencyimprovement and measurement accuracy enhancement compared with the conventionalsingle-probe method. The basic principle, capabilities, limitations, and design of multi-probe configuration are detailed for each measurement objective. Moreover, extensivemeasurement results were presented to validate the effectiveness and robustness of theproposed multi-probe based array calibration algorithms for each measu

Development of gas sensor based on fractal substrate structures

Gas sensor plays a key role in many applications with sensitivity being a critical performance characteristic. Increasing the surface area of gas sensing material is one approach that can increase sensitivity. Fractal geometries, which have the large specific surface area and special fractal dimension, have previously been successfully used in the design of macrostructure and microstructure of gas sensors to improve their performance. In this article, the influence of geometrical structure of the substrate on the gas sensor performance has been investigated. Two fractal structures (Koch snowflake and Menger sponge) and one traditional structure (Cylinder) were fabricated by 3-D printing and coated in Ag-doped multiwalled carbon nanotube (Ag:MWCNT)-based sensing materials. The fabricated sensors were tested with nitrogen dioxide at different temperatures and humidity. Experimental results show that the sensitivity of gas sensors with fractal structures is increased more than twice that of those with traditional geometrical structures

Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment

The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer’s experience

Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose

In this paper, the relationship between typical circuit structures of gas sensorcircuits and their output noise is analyzed. By using averaged segmenting periodical graphand improved histogram estimation methods, we estimated their noise power spectra andoptimal probability distribution functions (pdf). The results were confirmed throughexperiment studies

Research on a Visual Electronic Nose System Based on Spatial Heterodyne Spectrometer

Light absorption gas sensing technology has the characteristics of massive parallelism, cross-sensitivity and extensive responsiveness, which make it suitable for the sensing task of an electronic nose (e-nose). With the performance of hyperspectral resolution, spatial heterodyne spectrometer (SHS) can present absorption spectra of the gas in the form of a two dimensional (2D) interferogram which facilitates the analysis of gases with mature image processing techniques. Therefore, a visual e-nose system based on SHS was proposed. Firstly, a theoretical model of the visual e-nose system was constructed and its visual maps were obtained by an experiment. Then the local binary pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) were used for feature extraction. Finally, classification algorithms based on distance similarity (Correlation coefficient (CC); Euclidean distance to centroids (EDC)) were chosen to carry on pattern recognition analysis to verify the feasibility of the visual e-nose system

Circuit and noise analysis of odorant gas sensors in an E-nose

Abstract: In this paper, the relationship between typical circuit structures of gas sensor circuits and their output noise is analyzed. By using averaged segmenting periodical graph and improved histogram estimation methods, we estimated their noise power spectra and optimal probability distribution functions (pdf). The results were confirmed through experiment studies

Block-based versus text-based programming environments on novice student learning outcomes: a meta-analysis study

Background and Context: The use of block-based programming environments is purported to be a good way to gently introduce novice computer programmers to computer programming. A small, but growing body of research examines the differences between block-based and text-based programming environments. Objective: Thus, the purpose of this study was to examine the overall effect of block-based versus text-based programming environments on both cognitive and affective student learning outcomes. Method: Five academic databases were searched to identify literature meeting our inclusion criteria and resulted in 13 publications with 52 effect size comparisons on both cognitive and affective outcomes. Findings: We found small effect size (g = 0.245; p = .137; with a 95% confidence interval of −0.078 to 0.567) in favor of block-based programming environments on cognitive outcomes, and a trivial effect size (g = 0.195, p = .429; with a 95% confidence interval of −0.289 to 0.678) on affective outcomes. Both effect size calculations were statistically insignificant using random effects models. The effect sizes were examined for moderating effects by education level, learning environment, and study duration. Some evidence of publication bias was detected in these data. Implications: More research is needed to examine the utility and efficacy of block-based programming environments for novice programmers. Future studies should account for hybrid programming environments using novel research methods

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data