Early Online (Volume - 4 | Issue - 7) at IgMin Research

This paper proposes a novel deep learning-based framework for the multi-class prediction analysis of 3-D objects through the application of phase-only digital holographic data obtained via the phase-shifting approach. The dataset utilised in this study comprises seven distinct 3-D object pairings: M-L, D-L, C-N, A-I, D-S, C-S, and H-R, all represented by phase-only holographic images that maintain crucial spatial and depth information. The digital holograms were formed using programmatically generated synthetic 3-D objects and further numerical...ly processed to create 2-D phase images that served as inputs to the prediction task. A custom convolutional neural network (CNN) architecture, along with a modified AlexNet architecture, was employed to simultaneously predict multiple continuous attributes associated with the 3-D objects from their respective phase-only inputs. Regression (Prediction) task model performance was evaluated using mean squared error (MSE), mean absolute error (MAE), and R2 score metrics, demonstrating the ability to perform multi-class prediction with high accuracy and robustness, while also being computationally efficient. The CNN has achieved better regression performance compared to AlexNet in terms of MSE, MAE, and R2 score values. The use of deep learning in this manner thus provides a scalable method of analysing 3-D objects using holographic imaging techniques, moving away from previous binary regression techniques and the limitations of traditional machine learning approaches towards richer predictions of multiple associated attributes.Read more

Micromobility has emerged as an important element of sustainable urban transport, offering an effective solution for first- and last-mile connectivity. Despite the growing adoption of shared electric vehicles, many existing fleet deployment and routing methods continue to prioritise minimising travel distance while paying limited attention to the impact of road topography on energy consumption. This omission is particularly relevant in cities with varying terrain, where elevation changes can significantly affect battery usage and overall operat...ional efficiency. This paper introduces a physics-informed optimisation framework that incorporates terrain-related energy demand into micromobility fleet deployment. Instead of relying solely on travel distance, the proposed approach estimates the energy required for vehicle movement by accounting for rolling resistance, aerodynamic drag, gravitational effects caused by road slopes, and energy recovery through regenerative braking on downhill sections. These energy calculations are embedded within a graph-based optimisation model whose objective is to identify routes with the lowest total energy consumption. To assess the effectiveness of the proposed methodology, a case study was conducted using selected road segments from the Vilnius street network that represent different topographical characteristics. The simulation results indicate that road elevation has a substantial influence on vehicle energy requirements. They also reveal that the shortest path does not always correspond to the most energy-efficient one. In several scenarios, longer routes consumed less energy because of more favourable elevation profiles and the additional benefits provided by regenerative braking. Compared with traditional distance-based routing strategies, the proposed framework offers a more accurate representation of real-world energy consumption, leading to better-informed fleet deployment decisions. The methodology is suitable for integration into real-time fleet management systems and smart city platforms, where it can contribute to lower energy consumption, improved battery utilisation, and more sustainable operation of shared micromobility services.Read more