ALBERT

Description: This paper presents microfabrication process-driven design of a multi-degree of freedom (multi-DoF) non-resonant electrostatic microelectromechanical systems (MEMS) gyroscope by considering the design constraints of commercially available low-cost and widely-used silicon-on-insulator multi-user MEMS processes (SOIMUMPs), with silicon as a structural material. The proposed design consists of a 3-DoF drive mode oscillator with the concept of addition of a collider mass which transmits energy from the drive mass to the passive sense mass. In the sense direction, 2-DoF sense mode oscillator is used to achieve dynamically-amplified displacement in the sense mass. A detailed analytical model for the dynamic response of MEMS gyroscope is presented and performance characteristics are validated through finite element method (FEM)-based simulations. The effect of operating air pressure and temperature variations on the air damping and resulting dynamic response is analyzed. The thermal stability of the design and corresponding effect on the mechanical and capacitive sensitivity, for an operating temperature range of −40 °C to 100 °C, is presented. The results showed that the proposed design is thermally stable, robust to environmental variations, and process tolerances with a wide operational bandwidth and high sensitivity. Moreover, a system-level model of the proposed gyroscope and its integration with the sensor electronics is presented to estimate the voltage sensitivity under the constraints of the readout electronic circuit.

Description: Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p 〈 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Description: Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study’s objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.

Description: Climate change scenarios predict that an extended period of drought is a real threat to food security, emphasizing the need for new crops that tolerate these conditions. Quinoa is the best option because it has the potential to grow under water deficit conditions. There is considerable variation in drought tolerance in quinoa genotypes, and the selection of drought-tolerant quinoa germplasms is of great interest. The main goal of this work is to evaluate the crop yield and characterize the physiology of 20 quinoa genotypes grown under water deficit in a wirehouse. The experiment was a complete randomized design (CRD) factorial with three replications. Seedling growth, i.e., fresh weight (FW), dry weight (DW), root length (RL), shoot length (SL), relative growth rate of root length (RGR-RL), shoot length (RGR-SL), and physiological performance, i.e., chlorophyll content (a and b), carotenoid, leaf phenolic content, leaf proline content, membrane stability index (MSI), and leaf K+ accumulation were evaluated in a hydroponic culture under different water-deficit levels developed by PEG 6000 doses (w/v) of 0% (control), 0.3%, and 0.6%. Yield attributes were evaluated in a pot at three different soil moisture levels, as determined by soil gravimetric water holding capacity (WHC) of 100 (control), 50% WHC (50 % drought stress) and 25% WHC (75% stress). In both experiments, under the water stress condition, the growth (hydroponic study) and yield traits (pot study) were significantly reduced compared to control treatments. On the drought tolerance index (DTI) based on seed yield, genotype 16 followed by 10, 1, 4, 5, 7, and 12 could be considered drought-tolerant genotypes that produced maximum grain yield and improved physiological characteristics under severe water stress conditions in hydroponic culture. In both studies, genotypes 3, 8, 13, and 20 performed poorly and were considered drought-sensitive genotypes with the lowest DTI values under water-stressed conditions. All the studied agronomic traits (grain yield, root and shoot length, shoot fresh and dry weights) and physiological traits (leaf phenolic, proline content, carotenoid, K+ accumulation, membrane stability index, and relative water content) were firmly inter-correlated and strongly correlated with DTI. They can be regarded as screening criteria, employing a large set of quinoa genotypes in a breeding program.

Description: Different soil amendments are applied to improve soil properties and to achieve higher crop yield under drought conditions. The objective of the study was to investigate the role of biochar for the improvement of wheat (Triticum aestivum L.) growth and soil biochemical properties under drought conditions. A pot experiment with a completely randomized design was arranged with four replications in a wire house. Drought was imposed on two critical growth stages (tillering and grain filling) and biochar was applied to the soil 10 days before sowing at two different rates (28 g kg−1 and 38 g kg−1). Soil samples were collected to determine the soil properties including soil respiration and enzymatic parameters after crop harvesting. Results showed that water stress negatively affects all biochemical properties of the soil, while biochar amendments positively improved these properties. Application of biochar at 38 g kg−1 provided significantly higher mineral nutrients, Bray P (18.72%), exchangeable-K (7.44%), soil carbon (11.86%), nitrogen mineralization (16.35%), and soil respiration (6.37%) as a result of increased microbial activities in comparison with the 28 g kg−1 rate.