Cookmosegaard5809

Crop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s-1). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s-1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2±5.39 ms at an optimised linear speed of cell of 99.46 mm·s-1 and forward speed of 2 km·h-1. This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h-1), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h-1.Monitoring core body temperature (Tc) during training and competitions, especially in a hot environment, can help enhance an athlete's performance, as well as lower the risk for heat stroke. Accordingly, a noninvasive sensor that allows reliable monitoring of Tc would be highly beneficial in this context. One such novel non-invasive sensor was recently introduced onto the market (CORE, greenTEG, Rümlang, Switzerland), but, to our knowledge, a validation study of this device has not yet been reported. Therefore, the purpose of this study was to evaluate the validity and reliability of the CORE sensor. In Study I, 12 males were subjected to a low-to-moderate heat load by performing, on two separate occasions several days apart, two identical 60-min bouts of steady-state cycling in the laboratory at 19 °C and 30% relative humidity. In Study II, 13 males were subjected to moderate-to-high heat load by performing 90 min of cycling in the laboratory at 31 °C and 39% relative humidity. In both cases the core body temperatures indicated by the CORE sensor were compared to the corresponding values obtained using a rectal sensor (Trec). The first major finding was that the reliability of the CORE sensor is acceptable, since the mean bias between the two identical trials of exercise (0.02 °C) was not statistically significant. However, under both levels of heat load, the body temperature indicated by the CORE sensor did not agree well with Trec, with approximately 50% of all paired measurements differing by more than the predefined threshold for validity of ≤0.3 °C. In conclusion, the results obtained do not support the manufacturer's claim that the CORE sensor provides a valid measure of core body temperature.Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.Internet of Things (IoT) applications are becoming more integrated into our society and daily lives, although many of them can expose the user to threats against their privacy. Therefore, we find that it is crucial to address the privacy requirements of most of such applications and develop solutions that implement, as far as possible, privacy by design in order to mitigate relevant threats. While in the literature we may find innovative proposals to enhance the privacy of IoT applications, many of those only focus on the edge layer. On the other hand, privacy by design approaches are required throughout the whole system (e.g., at the cloud layer), in order to guarantee robust solutions to privacy in IoT. With this in mind, we propose an architecture that leverages the properties of blockchain, integrated with other technologies, to address security and privacy in the context of IoT applications. Selleck Linsitinib The main focus of our proposal is to enhance the privacy of the users and their data, using the anonymisation properties of blockchain to implement user-controlled privacy. We consider an IoT application with mobility for smart vehicles as our usage case, which allows us to implement and experimentally evaluate the proposed architecture and mechanisms as a proof of concept. In this application, data related to the user's identity and location needs to be shared with security and privacy. Our proposal was implemented and experimentally validated in light of fundamental privacy and security requirements, as well as its performance. We found it to be a viable approach to security and privacy in IoT environments.The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human-machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human-machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them.Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.This paper presents a technique to design strongly coupled planar arrays with very high aperture efficiency. The key innovation is that, based on an irregular 2 × 1 array, very compact medium-sized arrays of size 2 × 2, 2 × 4, and 2 × 6 are constructed with very strong and constructive mutual coupling between the elements. In this way, a maximum aperture efficiency is reached for a given footprint of the array. The occupied space of the antenna in comparison with conventional linear patch arrays is studied. A prototype 2 × 4 array operating around 5.8 GHz is designed, fabricated, built, and measured. The results show a large bandwidth of 20% and a very high aperture efficiency of 100%, which is the largest found in the literature for similarly sized arrays. These results are important in view of the future Internet of Things, where small and medium-sized arrays are planned to be mounted on numerous devices where a very limited physical area is available.In rehabilitation, the Fugl-Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia.