An intelligent humidity control system for mushroom growing house by using beam-switching antennas with artificial neural networks

An automatic humidity control system for mushroom growing house based on the free-space technique is presented. The novelty of this work is the modified free-space technique by measuring the amplitude only of transmission coefficient |S21| that reflected from mushroom by using beam-switching antenna with artificial neural networks (ANNs) as a humidity sensor to control quantity and time of water misting nozzle. In the proposed system, the antenna is designed to act as the transmitting antenna at the frequency of 2.45 GHz. Its radiation patterns can be switched to 4 directions covering all corners of mushroom growing house. The measured |S21| from each direction are converted to direct current (DC) voltage by a radio frequency (RF) detector; then are trained with ANNs in the humidity range of 60-85%. The optimized ANNs structure consists of 4 input nodes, two layers of 5 hidden nodes, and 3 output nodes. To verify the proposed system, experiments were set up in controlled humidity mushroom growing house at the humidity level of 75-80% for 120 hours. The results showed that there was slightly average standard deviation (S.D.) of humidity level 1.36. Consequently, the performance of sensor system assures that it is able to apply for humidity control in large growing house

Process Analytics from Passive Acoustic Emissions Monitoring during Fluidized Bed Pellet Coating in Pharmaceutical Manufacturing

Piezoelectric microphones were attached to a top spray fluidized bed to provide valuable process signatures. Relationships were developed between sound waves and conditions within the fluidized bed to relay critical quality and performance information. Deep learning analytics were used to extract valuable information from experimental data. Advancements in passive acoustic emissions monitoring will play a key role in optimizing pharmaceutical manufacturing pathways to ensure drug quality and performance

Design and Evaluation of a Non-Intrusive Corn Population Sensor

Specific objectives of this study were to develop, prototype, and test a corn population sensor. Both intrusive mechanical and non-intrusive capacitive techniques have been used to develop the stalk population sensors in previous research. However, neither could generate consistent performance. The mechanical method required high maintenance and resulted in significant underestimations of stalk counts. The performance of capacitive systems was limited by inadequate sensing distance, especially at low stalk moisture levels. In this research, the sensitivity of the capacitive sensor was optimized for corn stalks. This system utilized a single-sided capacitive sensor, Wien bridge oscillator, phase-locked loop, and an operational amplifier to transform stalk presence to a change in electrical potential signal. The capacitive sensor patterns were simulated using the finite element method, which provided useful conceptual information. A number of different detection element patterns were modeled and tested. The patterns examined included single-sided two-plate, interdigital, polarized interdigital, semi-interdigital, and solid ground electrode. The key parameters affecting pattern sensitivity were investigated. The most promising pattern, the solid ground electrode, was selected for further evaluation and development. The solid ground electrode detection element was incorporated into circuitry including Wien-Bridge oscillator, a phase-locked loop used as a high-speed frequency-tovoltage converter, and an operational amplifier to provide impedance matching and maximize data acquisition resolution. The operational configuration, optimum operating parameters, and associated component sizes were determined using both modeling and laboratory testing. With an acceptable signal-sided pattern and signal-to-noise ratio, this sensing system was investigated in a realistic production environment. A preliminary field test was used to evaluate the sensor system (including a protective housing and mounting system) and data acquisition system to identify problems before conducting the final field test. Stalk moisture content and harvest speed were used as treatment blocks in the final test. The influences of environmental and mechanical noise and the noise-like influence of corn leaves and weeds were also investigated. The final field test accurately simulated realistic harvesting conditions and real-time data was collected for stalk identification analysis. Post-acquisition processing, feature extraction, and principal component analysis of the extracted features were performed on the raw field data. Three sensor signal features were selected to identify stalks. A backpropagation artificial neural network technique was used to develop the pattern classification model. Numerous neural network structures were evaluated and two-layer structure with four neurons in the first layer and one neuron in the second layer was selected based on maximum prediction precision and accuracy and minimum structure complexity. This structure was then evaluated to determine the prediction accuracy at various resolution levels. Results showed that the model can predict stalk population at 99.5% accuracy when the spatial resolution is 0.025 ha. The sensor can predict stalk population with a 95% accuracy when the resolution is a 9-meter row segment (approximately 10 seconds)

Smart kitchen for Ambient Assisted Living

El envejecimiento de la población es una realidad en todos los países desarrollados. Las predicciones de crecimiento de esta población son alarmantes, planteando un reto para los servicios sociales y sanitarios. Las personas ancianas padecen diversas discapacidades que se van acentuando con la edad, siendo más propensas a sufrir accidentes domésticos, presentando problemas para realizar tareas cotidianas, etc. Esta situación conlleva a una pérdida paulatina de capacidades que en muchas ocasiones acaba con la vida autónoma de la persona. En este contexto, las Tecnologías de la Información y Comunicación (TIC) aplicadas al entorno doméstico pueden jugar un papel importante, permitiendo que las personas ancianas vivan más tiempo, de forma independiente en su propio hogar, presentando, por tanto, una alternativa a la hospitalización o institucionalización de las mismas. Este trabajo da un paso más en este sentido, presentando el diseño y desarrollo de un Ambiente Inteligente en la cocina, que ayuda a las personas ancianas y/o con discapacidad a desempeñar sus actividades de la vida diaria de una forma más fácil y sencilla. Esta tesis realiza sus principales aportaciones en dos campos: El metodológico y el tecnológico. Por un lado se presenta una metodología sistemática para extraer necesidades de colectivos específicos a fin de mejorar la información disponible por el equipo de diseño del producto, servicio o sistema. Esta metodología se basa en el estudio de la interacción Hombre-Máquina en base a los paradigmas y modelos existentes y el modelado y descripción de las capacidades del usuario en la misma utilizado el lenguaje estandarizado propuesto en la Clasificación Internacional del Funcionamiento, de la Discapacidad y de la Salud (CIF). Adicionalmente, se plantea el problema de la evaluación tecnológica, diseñando la metodología de evaluación de la tecnología con la finalidad de conocer su accesibilidad, funcionalidad y usabilidad del sistema desarrollado y aplicándola a 61 usuarios y 31 profesionales de la gerontología. Desde un punto de vista técnico, se afronta el diseño de un ambiente asistido inteligente (Ambient Assisted Living, AAL) en la cocina, planteando y definiendo la arquitectura del sistema. Esta arquitectura, basada en OSGi (Open Services Gateway initiative), oferta un sistema modular, con altas capacidades de interoperabilidad y escalabilidad. Además, se diseña e implementa una red de sensores distribuida en el entorno con el fin de obtener la mayor información posible del contexto, presentando distintos algoritmos para obtener información de alto nivel: detección de caídas o localización. Todos los dispositivos presentes en el entorno han sido modelados utilizando la taxonomía propuesta en OSGi4AmI, extendiendo la misma a los electrodomésticos más habituales de la cocina. Finalmente, se presenta el diseño e implementación de la inteligencia del sistema, que en función de la información procedente del contexto y de las capacidades del usuario da soporte a las principales actividades de la vida diaria (AVD) en la cocina

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470