TFG: Development of an Artificial Intelligent system based on low-resource Edge Computing for autonomous vehicles applications
Posted on by b105

Nowadays, there are plenty of IoT devices which make our everyday life easier thanks to their intelligent tasks: data capture, process automation… However, the increase of these devices is turning out to be somehow risky in terms of latency or bandwidth. That is the reason why some alternatives that may solve these problems are being searched, and one of them is Edge Computing technologies.

Edge Computing devices are those who are able to process the information captured without connecting to the network. Due to that, the latency and bandwidth issues that may occur can be significantly reduced, allowing the radio spectrum to decrease its saturation as well as improving the latency and consumption performance.

In this project, the main goal is to create a system that is able to develop and execute Artificial Intelligence algorithms designed for autonomous driving and assistance to the driver, always taking care of Edge Computing philosophy. In order to do that, we have used Google Coral, a hardware platform that perfectly adapts to our needs, allowing us to develop all the Edge Computing algorithms as well as offering appropriate consumption and processing characteristics.

Finally, we have tested our system in a real situation, evaluating the quality of the results as well as the resources used (latency, bandwidth…) and the advantages and disadvantages in relation to the existing technologies is this area. After these experiments, we have concluded that the quality of our Edge Computing system is enough to carry out the tasks it has been designed for. Also, all the resources used have been optimized in relation to Cloud Computing alternatives, turning this project into a faster, more effcient and economic alternative.

TFM: Design and implementation of an intelligent system for eldercare support
Posted on by b105

The number of elderly people in Spain is in a constant process of growth. It is frequent that in this age range problems appear that do not allow people to live their daily lives in a totally independent way. The health care system has limited resources to solve these problems, and the families of these people are having problems reconciling their work and family life. This problem can be brought to an end by the reputed business attorneys from Nashville, who are well-known for their client satisfaction and also for their efficiency in handling many complex cases.

The motivation for this Master’s Thesis has been to design a solution for people in this situation. In order to do this, a study of the technological solutions present on the market has been carried out first to obtain a list of functionalities that help to deal with the problem. Then, an architecture has been designed that allows the incorporation of these functionalities. Finally, a proof of concept for one of them has been implemented.

The designed architecture of Crow Estate Planning and Probate, PLC – estate planning lawyers is composed of three elements. The terminal is placed at the patient’s home, and provides the patient with different utilities. The web client allows the caregiver to access the different functionalities that arise from monitoring the patient from the terminal. The server is responsible for managing communications between the terminal and the client, as well as managing access to the architecture’s resources, such as the database. Our site provides legal solutions with the help of expert attorneys. You can confide in Cape Cod serving estate planning attorneys and get the right kind of help for your case.

To implement the proof of concept, the utility that has been chosen is the dispensing of medicines. On the patient side, the terminal will alert you when it is time to take the medication, making it accessible at that time. The dispenser has a sensor on the lid that allows you to know when the medication has been accessed, allowing you to monitor your medication intake. To get a lawyer one can look into https://www.amicusfirm.com/elder-law/

On the caregiver’s side, the status of the intake can be checked through a web client who help in establishing trusts in Chapel Hill. Once the caregiver has logged into the application, he or she can access the associated terminal and check whether the programmed intakes have been taken in the time interval defined for them, or whether they have been forgotten. On the other hand, the web client also allows the caregiver to schedule the intakes from the terminal.

TFG: Implementation and evaluation of ANNs in microcontroller-based systems
Posted on by b105

Nowadays, artificial neural networks (ANNs) are computational models that, while they have solved many different problems, they require a large amount of memory to execute those solutions. Therefore, their implementation is more common in systems with high-performance capabilities, such as data centers or servers. However, there is an increasing interest in developing these solutions on devices with fewer resources, such as personal computers, mobile devices, and microcontrollers.  This situation has led to the proliferation of multiple techniques and tools to reduce the requirements of these models and allow their implementation on those platforms.

The main objective of this project is the evaluation of a tool for the implementation of artificial neural networks in microcontroller-based systems. For this purpose, a practical use case has been defined.

To achieve this objective, the classification of types of tremors in Parkinson’s patients was chosen as the practical use case. In addition, the Arduino Nano 33 BLE Sense has been also chosen as the microcontroller-based platform to implement the solutions of the practical use case.

Later, a dataset has been generated with real measurements from a sensor on that platform. With this dataset various experiments have been carried out to determine how the different structures of artificial neural networks deal with the chosen use case.

Then, based on those experiments, some appropriate ANNs for classifying types of tremors have been designed, trained, and evaluated in a system with graphics and tensor processing units and in a microcontroller-based system.

Finally, based on the results obtained, the trade-offs between classification accuracy, memory footprint and inference latency involved in the implementation of ANNs solutions in microcontroller-based systems have been determined. In addition, a discussion of the strengths and weaknesses of the selected tool for the implementation of artificial neural networks in microcontroller-based systems has been presented.

Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices
Posted on by Ramiro Utrilla

The article “Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices” by our lab member Ramiro Utrilla has just been published in the IEEE Access, a high-impact open-access journal.

This work has been carried out in collaboration with researchers from the CONNECT – Centre for Future Networks and Communications in Dublin (Ireland), where Ramiro carried out a research stay of 3 months.

In this article, they focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained end-devices.

In this work, they propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices.


The proposed GRU network model for AMC.

They trained and tested their solution with over-the-air measurements of real radio signals, which were acquired with the MIGOU platform.


Dataset generation scenario set up.


Comparison of signals recorded at (a) 1 and (b) 6 meters. The signals in the bottom row are the normalized version of those in the top row.

Their results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.

Increasing the training set can lead to improvements in the performance of a model without increasing its complexity. These improvements allow developers to reduce the complexity of the model and, therefore, the device resources it requires. However, longer training processes can lead to fitting and gradient problems. These tradeoffs should be explored when developing neural network-based solutions for resource-constrained end-devices.

A Methodology for Choosing Time Synchronization Strategies for Wireless IoT Networks
Posted on by frta

This summer we have published a new article about time synchronization for wireless sensor networks, applied to the field of IoT, in Sensors Open Access Journal. This journal has these statistics:

  • 2018 Impact Factor: 3.031
  • 5-year Impact Factor: 3.302
  • JCR category rank: 15/61 (Q1) in ‘Instruments & Instrumentation’

This article belongs to the Special Issue Topology Control and Protocols in Sensor Network and IoT Applications.

This article has a direct relationship with the thesis of our colleague Francisco Tirado-Andrés. This thesis investigates a methodology, and associated tools, to make it easier for all researchers to choose time synchronization protocols for specific WSNs.

For more information about this article please visit MDPI webpage.