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Effective pest management is crucial for enhancing agricultural productivity, especially for crops such as sugarcane and wheat that are highly vulnerable to pest infestations. Traditional pest management methods depend heavily on manual…

This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a…

Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems…

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images.…

Automation of objects labeling in aerial imagery is a computer vision task with numerous practical applications. Fields like energy exploration require an automated method to process a continuous stream of imagery on a daily basis. In this…

This paper solves and analyzes a trajectory optimization problem to deflect Earth-crossing objects (ECOs) employing continuous thrust obtained using a laser ablative system. The optimal control is determined for various initial ECO-Earth…

Sensor networks are currently an active research area mainly due to the potential of their applications. In this paper we investigate the use of Wireless Sensor Networks (WSN) for air pollution monitoring in Mauritius. With the fast growing…

In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learning leverages unlabeled samples to augment the available…

To have the greatest impact, public health initiatives must be made using evidence-based decision-making. Machine learning Algorithms are created to gather, store, process, and analyse data to provide knowledge and guide decisions. A…

The demand for improved efficiency and accuracy in vaccine safety assessments is increasing. Here, we explore the application of computer vision technologies to automate the monitoring of experimental mice for potential side effects after…

We present the design and prototype of an open source, optical fingerprint reader, called RaspiReader, using ubiquitous components. RaspiReader, a low-cost and easy to assemble reader, provides the fingerprint research community a seamless…

Levitated optomechanical systems are rapidly becoming leading tools for precision sensing of forces and accelerations acting on particles in the femtogram to nanogram mass range. These systems enable a high level of control over the…

This paper shows a novel method to precisely measure the laser power using an optomechanical system. By measuring a mirror displacement caused by the reflection of an amplitude modulated laser beam, the number of photons in the incident…

Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector. In previous works, SPI is usually used for capturing object images or performing…

With the tremendous advances of the wireless devices technology, securing wireless sensor networks became more and more a vital but also a challenging task. In this paper we propose an integrated strategy that is meant to discover malicious…

Wide field-of-view imaging of fast processes in a microscope requires high light intensities motivating the use of lasers as light sources. However, due to their long spatial coherence length lasers are inappropriate for such applications…

Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient celestial events in the images is very important for such study as it allows…

Automatic camera-assisted monitoring of insects for abundance estimations is crucial to understand and counteract ongoing insect decline. In this paper, we present two datasets of nocturnal insects, especially moths as a subset of…

This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolution bin. It is shown how sensing accuracy, in terms of both…

Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency. They do so by using asynchronous spike-based data flow,…