Abstract
The domain of Intelligent Transportation Systems (ITS) is becoming a key candidate to enable safer and efficient mobility in IoT enabled smart cities. Several recent research in cooperative autonomous systems are conducted over simulation frameworks as real experiments are still too costly. In this paper, we present a platooning robotic test-bed platform with a 1/10 scale robotic vehicles that functions based on the input front commercially off the shelf technologies (COTS) such as Lidars and cameras. We also present an in-depth analysis of the functionalities and architecture of the proposed system. We also compare the performance of the aforementioned sensors in some real-life emulated scenarios. From our results, we were able to concur that the camera based platooning is able to perform well at partially observable scenarios than its counterpart.

Abstract
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

Abstract
The Gemini Infrared Multi-Object Spectrograph (GIRMOS) will be a near-infrared, multi-object, medium spectral resolution, integral field spectrograph (IFS) for Gemini North Telescope, designed to operate behind the future Gemini North Adaptive Optics system (GNAO). In addition to a first ground layer Adaptive Optics (AO) correction in closed loop carried out by GNAO, each of the four GIRMOS IFSs will independently perform additional multi-object AO correction in open loop, resulting in an improved image quality that is critical to achieve top level science requirements. We present the baseline parameters and simulated performance of GIRMOS obtained by modeling both the GNAO and GIRMOS AO systems. The image quality requirement for GIRMOS is that 57% of the energy of an unresolved point-spread function ensquared within a 0.1 x 0.1 arcsecond at 2.0 mu m. It was established that GIRMOS will be an order 16 x 16 adaptive optics (AO) system after examining the tradeoffs between performance, risks and costs. The ensquared energy requirement will be met in median atmospheric conditions at Maunakea at 30 degrees from zenith.

Abstract

Abstract
As software applications continue to become more complex and attractive to cyber-attackers, enhancing resilience against cyber threats becomes essential. Aiming to provide more robust solutions, different approaches were proposed for vulnerability detection in different stages of the application life-cycle. This article explores three main approaches to application security: Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA). The analysis conducted in this work is focused on open-source solutions while considering commercial solutions to show contrast in the approaches taken and to better illustrate the different options available. It proposes a baseline comparison model to help evaluate and select the best solutions, using comparison criteria that are based on community standards. This work also identifies future opportunities for application security, highlighting some of the key challenges that still need to be addressed in order to fully protect against emerging threats, and proposes a workflow that combines the identified tools to be used for vulnerability assessments.

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.