IoT Enabled Smart Fertilization and Irrigation Aid for Agricultural Purposes

Soil is of great importance to agriculture, especially the moisture and nutrients in the soil are the essential ingredients for growing plants and crops. Therefore, benefits and importance of a soil moisture and nutrient monitoring system in modern agriculture and gardening is undeniable. It can also be an interesting feature of an intelligent home or smart agriculture system using the internet of things (IoT) technology. This paper presents an IoT application in Arduino platform aiming to monitor the change in soil moisture and Nitrogen (N), Phosphorus (P), Potassium (K) (NPK) value for an indoor plant using moisture sensors and optical transducers. Other functionalities and important features of this prototype include online data display infographic as user feedback, level-based nutrient classification for enabling proper type of fertilizer selection, hardware and e-mail notification of moisture and nutrients' easily accessible and user-friendly smartphone app

Demo: Results of 'iCaveats', a Project on the Integration of Architectures and Components for Embedded Vision

iCaveats is a Project on the integration of components and architectures for embedded vision in transport and security applications. A compact and efficient implementation of autonomous vision systems is difficult to be accomplished by using the conventional image processing chain. In this project we have targeted alternative approaches, that exploit the inherent parallelism in the visual stimulus, and hierarchical multilevel optimization. A set of demos showcase the advances at sensor level, in adapted architectures for signal processing and in power management and energy harvesting.Ministerio de Economia, Industria y Competitividad de España (MINECO) y el Fondo Europeo de Desarrollo de las Regiones (FEDER)-‘iCaveats’ TEC2015-66878-C3-1-R, TEC2015-66878-C3-2-R y TEC2015-66878-C3-3-RJunta de Andalucía-‘SmartCIS3D’ TIC 2338-2013FEDER- 2016-2019, ED431G/08 y 2017-2020, ED431C 2017/69Agencia Ejecutiva Europea de Investigación (EU-REA)-‘Achieve’ H2020 MSCAITN 2017 N° 76586

Crop Production Modeling System for Diverse Physiographical Areas in Nueva Vizcaya

The wide range of environmental factors affecting cropping is very difficult to ascertain most especially with the absence of climate monitoring equipment and dissemination of an up-to-date weather data forecasting Due to climate change irregular weather patterns cause major disruptions in agricultural activities and heavy damage to crop yields There is limited data available to anticipate and adapt to climatic changes due to insufficiency of monitoring systems The proposed system entitled Crop Production Modeling System then integrates the use of available state-of-the art climate sensing and monitoring system to gather and interpret data and establish current pattern of weather Portable and unique Field Monitoring Systems FMS installed in strategic locations of the different municipalities of Nueva Vizcaya will be utilize to effectively monitor variations in the weather patterns These weather patterns will be used as a tool to determine the optimal cropping season In addition the system provides different graphical presentations as reports readable and understandable to the users especially to the agricultural sectors Moreover the said system will be accessible and portable to the users in all cases because its internet dependability The present system can be customized to address not only agriculture concerns but also health and safety and disaster and risk management The system has the potential for up scaling and adoption by other provinces or municipalities due to its very promising capabilitie

Pixel-wise parameter adaptation for single-exposure extension of the image dynamic range

High dynamic range imaging is central in application fields like surveillance, intelligent transportation and advanced driving assistance systems. In some scenarios, methods for dynamic range extension based on multiple captures have shown limitations in apprehending the dynamics of the scene. Artifacts appear that can put at risk the correct segmentation of objects in the image. We have developed several techniques for the on-chip implementation of single-exposure extension of the dynamic range. We work on the upper extreme of the range, i. e. administering the available full-well capacity. Parameters are adapted pixel-wise in order to accommodate a high intra-scene range of illuminations.Ministerio de Economía (MINECO) TEC2015-66878-C3-1-RJunta de Andalucía P12-TIC 233

Pixel-wise parameter adaptation for single-exposure extension of the image dynamic range

High dynamic range imaging is central in application fields like surveillance, intelligent transportation and advanced driving assistance systems. In some scenarios, methods for dynamic range extension based on multiple captures have shown limitations in apprehending the dynamics of the scene. Artifacts appear that can put at risk the correct segmentation of objects in the image. We have developed several techniques for the on-chip implementation of single-exposure extension of the dynamic range. We work on the upper extreme of the range, i. e. administering the available full-well capacity. Parameters are adapted pixel-wise in order to accommodate a high intra-scene range of illuminationsPeer reviewe

HRTF individualization using deep learning

The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques. HRTF individualization is paramount for accurate binaural rendering, which is used in XR technologies, tools for the visually impaired, and many other applications. The rising availability of public HRTF data currently allows experimentation with different input data formats and various computational models. Accordingly, three research directions are investigated here: (1) extraction of predictors from user data; (2) unsupervised learning of HRTFs based on autoencoder networks; and (3) synthesis of HRTFs from anthropometric data using deep multilayer perceptrons and principal component analysis. While none of the aforementioned investigations has shown outstanding results to date, the knowledge acquired throughout the development and troubleshooting phases highlights areas of improvement which are expected to pave the way to more accurate models for HRTF individualization

Automatic Capture and Classification of Frog Calls

Global frog populations are threatened by an increasing number of environmental threats such as habitat loss, disease, and pollution. Traditionally, in-person acoustic surveys of frogs have measured population loss and conservation outcomes among these visually cryptic species. However, these methods rely heavily on trained individuals and time-consuming field work. We propose an end-to-end workflow for the automatic recording, presence-absence identification, and web page visualization of frog calls by their species. The workflow encompasses recording of frog calls via custom Raspberry Pis, data-pushing to Jetstream cloud computer, and species classification by three different machine learning models: Random Forest, Convolutional Neural Network, and Recursive Neural Network

Remote Collection of Physiological Data in a Virtual Reality Study

Recent pandemic related events have effectively put a stop to most in-lab data collection which has a profound negative impact on many research fields. Online and remote data collection, without the need to travel to a laboratory, starts to be used as a valuable alternative in some scenarios. This approach does not only help to resume some research activities, it also has an enormous potential to change how research is conducted in future. With the use of our biometric sensing system for Virtual Reality (emteqGO), we designed a VR experience autonomously guiding participants through the study. The combination of hardware posted to participants, alongside software solutions handling the setup, data collection, quality assurance and upload for immediate access enables a fully remote, unsupervised approach to data collection. While this approach might be the only feasible solution for some researchers, it has also laid the groundwork for possible future direction of research where remote data collection isa new way to enhance access to participants who typically would not travel to the laboratories. In designing these solutions, we found that for unsupervised remote data collection to work effectively, setup procedures must be easy to follow to obtain high quality data and the entire process must be highly robust, reliable, and built with a high degree of redundancy. Post-pandemic, there are many benefits of an ongoing use of remote research paradigms. These include ameliorating the diversity problem afflicting current research by widening the participant pool, improved research quality by collecting data in more naturalistic environments, and improving protocol standardisation using virtual reality

Inefficiencies in the Cache Hierarchy: A Sensitivity Study of Cacheline Size with Mobile Workloads

With the rising number of cores in mobile devices, the cache hierarchy in mobile application processors gets deeper, and the cache size gets bigger. However, the cacheline size remained relatively constant over the last decade in mobile application processors. In this work, we investigate whether the cacheline size in mobile application processors is due for a refresh, by looking at inefficiencies in the cache hierarchy which tend to be exacerbated when increasing the cacheline size: false sharing and cacheline utilization. Firstly, we look at false sharing, which is more likely to arise at larger cacheline sizes and can severely impact performance. False sharing occurs when non-shared data structures, mapped onto the same cacheline, are being accessed by threads running on different cores, causing avoidable invalidations and subsequent misses. False sharing has been found in various places such as scientific workloads and real applications. We find that whilst increasing the cacheline size does increase false sharing, it still is negligible when compared to known cases of false sharing in scientific workloads, due to the limited level of thread-level parallelism in mobile workloads. Secondly, we look at cacheline utilization which measures the number of bytes in a cacheline actually used by the processor. This effect has been investigated under various names for a multitude of server and desktop applications. As a low cacheline utilization implies that very little of the fetched cachelines was used by the processor, this causes waste in bandwidth and energy in moving data across the memory hierarchy. The energy cost associated with data movements is much higher compared to logic operations, increasing the need for cache efficiency, especially in the case of an energy-constrained platform like a mobile device. We find that the cacheline utilization of mobile workloads is low in general, decreasing when increasing the cacheline size. When increasing the cacheline size from 64 bytes to 128 bytes, the number of misses will be reduced by 10%-30%, depending on the workload. However, because of the low cacheline utilization, this more than doubles the amount of unused traffic to the L1 caches. Using the cacheline utilization as a metric in this way, illustrates an important point. If a change in cacheline size would only be assessed on its local effects, we find that this change in cacheline size will only have advantages as the miss rate decreases. However, at system level, this change will increase the stress on the bus and increase the amount of wasted energy due to unused traffic. Using cacheline utilization as a metric underscores the need for system-level research when changing characteristics of the cache hierarchy