Ignacio Martinez-Alpiste

Ignacio Martinez-Alpiste
University of the West of Scotland | UWS · School of Computing

PhD

About

11
Publications
1,585
Reads
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65
Citations
Education
September 2017 - February 2021
University of the West of Scotland
Field of study
  • Doctor of Computer Science and Artificial Intelligence
September 2012 - June 2017
University of Murcia
Field of study
  • Computer Science

Publications

Publications (11)
Article
This study proposes a novel illumination-aware image fusion technique and a Convolutional Neural Network (CNN) called BlendNet to significantly enhance the robustness and real-time performance of small human objects detection from Unmanned Aerial Vehicles (UAVs) in harsh and adverse operation environments. The proposed solution is particular useful...
Article
Full-text available
Telescopic cranes are powerful lifting facilities employed in construction, transportation, manufacturing and other industries. Since the ground workforce cannot be aware of their surrounding environment during the current crane operations in busy and complex sites, accidents and even fatalities are not avoidable. Hence, deploying an automatic and...
Article
Full-text available
Machine learning algorithms based on convolutional neural networks (CNNs) have recently been explored in a myriad of object detection applications. Nonetheless, many devices with limited computation resources and strict power consumption constraints are not suitable to run such algorithms designed for high-performance computers. Hence, a novel smar...
Article
Full-text available
This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements...
Article
Unmanned Aerial Vehicles (UAVs) are promising technologies within many different application scenarios including human detection in search and rescue and surveillance use cases, which have received considerable attention worldwide. However, adverse conditions, such as varying altitude, overhead camera placement, changing illumination and moving pla...
Article
Many people go missing in the wild every year. In this paper, the Search and Rescue (SAR) mission is conducted using a novel system comprising an Unmanned Aerial Vehicle (UAV) coupled with real-time machine-learning-based object detection system embedded on a smartphone. Human detection from UAV in the wilderness is a challenging task, because of m...
Conference Paper
To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world condition...
Article
https://www.comsoc.org/publications/ctn/5g-can-shape-mission-critical-healthcare-services Seamless connectivity for patients, first responders and health care professionals including medical centres is becoming an important aspect of the quality of emergency medical care. 5G networks and related technologies can significantly improve “care on the g...
Conference Paper
Human Search and Rescue (SAR) tasks are mission-critical and take place in the wild, and thus solutions require timely and accurate human detection on a highly portable platform. This paper proposes a novel lightweight and practical SAR system that meets those demanding requirements by running optimised machine learning in a smartphone, interoperab...
Article
Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innova...
Conference Paper
Object detection systems mounted on Unmanned Aerial Vehicles (UAVs) have gained momentum in recent years in light of the widespread use cases enabled by such systems in public safety and other areas. Machine learning has emerged as an enabler for improving the performance of object detection. However, there is little existing work that has studied...

Network

Cited By

Projects

Projects (4)
Project
5G-INDUCE targets the development of an open, ETSI NFV compatible, 5G orchestration platform for the deployment of advanced 5G NetApps. The platform’s unique features provide the capability to the NetApp developers to define and modify the application requirements, while the underlay intelligent OSS can expose the network capabilities to the end users on the application level without revealing any infrastructure related information. This process enables an application-oriented network management and optimization approach that is in line with the operator’s role as manager of its own facilities, while it offers the development framework environment to any developer and service provider through which tailored made applications can be designed and deployed, for the benefit of vertical industries and without any indirect dependency through a cloud provider.
Archived project
SliceNet intends to meet the challenging requirements from the management and control planes of network slicing across multiple administrative domains, facilitating early and smooth adoption of 5G slices for verticals to achieve their demanding use cases, and managing the QoE for slice services. SliceNet will follow a layered architectural approach to allow the creation of a modular, extensible and scalable framework.
Project
This research project is motivated by the requirements from Intebloc and their desired of deploying a real-time human detection to make significant improvements to the health and safety of the workforce during the lifting operation. The project provides a new solution to increase the visibility of a crane operator using Artificial Intelligence and will address the challenges in providing real-time machine learning-based human detection from top-view on a mobile platform (small-form PC) in complex operational environments. The system will be optimised for detecting people in complex operational areas from a distance up to 50 meters between the camera and the object.