The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wild animals is a difficult task due to the difficulties of tracking and classifying their actions. Nowadays, technology allows designing low-cost systems that make these tasks easier to carry out, and some of these systems produce good results; however, none of them obtains a high-accuracy classification because of the lack of information. Doñana National Park is a very rich environment with various endangered animal species. Thereby, this park requires a more accurate and efficient system of monitoring to act quickly against animal behaviors that may endanger certain species. In this letter, we propose a hierarchical, wireless sensor network installed in this park, to collect information about animals' behaviors using intelligent devices placed on them which contain a neural network implementation to classify their behavior based on sensory information. Once a behavior is detected, the network redirects this information to an external database for further treatment. This solution reduces power consumption and facilitates animals' behavior monitoring for biologists.
Neuromorphic engineering tries to mimic biology in information processing. Address-Event Representation (AER) is a neuromorphic communication protocol for spiking neurons between different layers. AER bio-inspired image sensors are called “retina”. This kind of sensors measurees visual information not based on frames from real life and generates corresponding events. In this paper we provide an alternative, based on cheap FPGA, to these image sensors that takes images provided by an analog video source (video composite signal), digitalizes it and generates AER streams for testing purposes. This design was initially developed for Xilinx Spartan FPGA. In this paper we present a comparison study between synthesis of this design for Xilinx Spartan, Virtex FPGA and Achronix asynchronous FPGA, measuring the maximum performance reached in each case.
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.
Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls' risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
Abnormal foot postures can be measured during the march by plantar pressures in both dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine learning classifier inside a low-power embedded system in order to obtain information from the user’s gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power consumption and the model effectiveness. The machine learning classifier is trained using an acquired dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over 99% and the power consumption tests obtains a battery autonomy over 25 days.
Augmented reality and virtual reality technologies are increasing in popularity. Augmented reality has thrived to date mainly on mobile applications, with games like Pokémon Go or the new Google Maps utility as some of its ambassadors. On the other hand, virtual reality has been popularized mainly thanks to the videogame industry and cheaper devices. However, what was initially a failure in the industrial field is resurfacing in recent years thanks to the technological improvements in devices and processing hardware. In this work, an in-depth study of the different fields in which augmented and virtual reality have been used has been carried out. This study focuses on conducting a thorough scoping review focused on these new technologies, where the evolution of each of them during the last years in the most important categories and in the countries most involved in these technologies will be analyzed. Finally, we will analyze the future trend of these technologies and the areas in which it is necessary to investigate to further integrate these technologies into society.
In stereo-vision processing, the image-matching step is essential for results, although it involves a very high computational cost. Moreover, the more information is processed, the more time is spent by the matching algorithm, and the more inefficient it is. Spike-based processing is a relatively new approach that implements processing methods by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system can solve much more complex problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy for visual information processing based on the neuro-inspired address-event-representation (AER) is currently achieving very high performance. The aim of this work was to study the viability of a matching mechanism in stereo-vision systems, using AER codification and its implementation in a field-programmable gate array (FPGA). Some studies have been done before in an AER system with monitored data using a computer; however, this kind of mechanism has not been implemented directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied and implemented, with other restrictions, in order to achieve good results in a real-time scenario. The results and conclusions are shown, and the viability of its implementation is proven.
Image processing in digital computer systems usually considers the visual information as a sequence of frames. These frames are from photographs that capture reality for a short period of time. They are renewed and transmitted at a rate of 25–30 frames per second, in a typical real-time scenario. Each of these frames needs to be filtered and processed in order to detect a feature on it. This processing is usually based on very expensive operations in terms of resource consumption (processor resources and processing time) for an efficient real-time application. In contrast, neuro-inspired systems, which work in a manner similar to the nervous system, may resolve those and others more complex problems, such as visual recognition in real-time. The spike-based philosophy for visual information processing based on the neuro-inspired Address-Event-Representation (AER) is achieving nowadays very high performances. Address-Event-Representation (AER) is a neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge numbers of neurons located on different chips. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) convert conventional frame-based video stream in the computer into AER and inject it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. A set of software methods for converting digital frames into AER format are present in the literature. In this work we study the low level performance lacks of these methods monitoring internal performance hardware counters for an Intel Core 2 Quad. We discuss the results obtained and we propose improvements for those software methods that did not achieve real-time properties.
