In learning-based image compression methods, entropy modelling remains crucial for the achievement of higher compression performance with low computational cost. In this study, we introduce a framework for learned image compression that combines channel-wise autoregressive entropy modelling, LSTM-based context adaptation, and latent residual prediction (LRP) to improve entropy estimation and reduce reconstruction errors. The model employs channel-wise conditioning to progressively refine latent distributions, while an autoregressive prior captures spatial dependencies, allowing for more accurate probability predictions. Furthermore, latent residual prediction reduces quantization errors, resulting in more accurate reconstructions. Experimental results indicate that the proposed method surpasses other deep learning-based compression techniques, achieving a 12.7% BD-rate (Bjontegaard Delta-Rate) reduction compared to a baseline model that utilises channel-conditioning and LRP. These findings highlight the effectiveness of combining hierarchical entropy modelling with spatial and channel-wise context adaptation to achieve state-of-the-art compression efficiency.
As the security of public spaces remains a critical issue in today's world, Digital Twin technologies have emerged in recent years as a promising solution for detecting and predicting potential future threats. The applied methodology leverages a Digital Twin of a metro station in Athens, Greece, using the FlexSim simulation software. The model encompasses points of interest and passenger flows, and sets their corresponding parameters. These elements influence and allow the model to provide reasonable predictions on the security management of the station under various scenarios. Experimental tests are conducted with different configurations of surveillance cameras and optimizations of camera angles to evaluate the effectiveness of the space surveillance setup. The results show that the strategic positioning of surveillance cameras and the adjustment of their angles significantly improves the detection of suspicious behaviors and with the use of the DT it is possible to evaluate different scenarios and find the optimal camera setup for each case. In summary, this study highlights the value of Digital Twins in real-time simulation and data-driven security management. The proposed approach contributes to the ongoing development of smart security solutions for public spaces and provides an innovative framework for threat detection and prevention.
We study a recently proposed model for opinion dynamics that takes into account social pressure, that is, the fact that agents are reluctant to reveal their actual opinions in an environment where other agents may have different views. In the examined model, each agent has an inherent opinion that represents their actual views, while agents may choose to declare an opinion that is different from their inherent one when they sense that they are in an environment where neighboring agents do not agree with their inherent opinion. At any point in time, agents communicate their stated views within the network which depend on the social pressure they receive within the network as well as their inherent views. Nodes tend to conform to the views of other nodes in the network. We perform numerical experiments using this model and several types of graphs that are commonly used to represent social networks. Our scope is to measure the strength of social pressure in various scenarios, by demonstrating the honesty percentage, i.e., the percentage of agents that declare their true inherent opinion, as a function of time. The numerical results produced show that the type of network significantly affects the characteristics of the social pressure.
Objective: To compare three deep-learning architectures-Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM) and Transformers-for binary gait classification from a single inertial sensor. Methods: Sixty-eight participants (25 healthy controls, 43 patients with neurological gait impairment) walked for 60 s while a tri-axial IMU placed at C7 recorded linear acceleration, angular velocity and magnetic field at 50 Hz. Each trial was segmented into 2.56 s windows (128 samples) with 50 % overlap, producing 25 040 labelled samples (74 % normal, 26 % pathological). Architectures were optimised with random search and evaluated in subject-wise 5-fold stratified cross-validation. Balanced accuracy, macro-F1 and inference time were the primary metrics. Results: The bidirectional LSTM achieved the best balanced accuracy (63.5 %) and macro-F1 (0.34), marginally ahead of the CNN (62.9 %, 0.30). The Transformer reached only 50.5 % and 0.03, failing on the minority class. Conclusions: Even with a single sensor and a modest cohort, sequence-aware models capture discriminative temporal cues. LSTMs offer the best trade-off between performance and computational load, but class imbalance remains a limiting factor that future work must address.
Accurate detection of epileptic seizure events is an extremely challenging problem due to the complex and highly variable nature of seizure activity, which can manifest differently in each individual. This study aims to obtain a meaningful representation of EEG data by utilizing a) Poincare plots, which can effectively capture the underlying temporal characteristics of EEG signal; and b) hybrid autoencoder-classifier model. The bottleneck layer of autoencoder helps to provide a compressed latent representation, while classification head is used for accurate class predictions. The proposed approach is validated on four benchmark datasets, which are publicly available. The mean accuracy and false detection rate across all the subjects in each of these datasets show its superior performance, not only in identifying seizure and non-seizure events, but also in distinguishing across different types of seizures. The method is able to distinguish healthy controls and epileptic patients from BONN dataset with 100% accuracy. It provides 90% accuracy in classifying ictal, interictal, and preictal segments from New Delhi dataset. This classification task becomes comparatively more challenging, as it requires the model to differentiate between subtle variations in brain activity that occur during different phases of the seizure cycle. For the Epileptic EEG dataset, it provides 97% accuracy in the identification of different types of seizures and healthy data. In case of Turkish EEG dataset which comprises of 121 subjects, the model achieves 88% accuracy in classifying healthy and epileptic segments in a subject-independent manner. Overall, the proposed method provides results that are on par with, or even surpass, those reported in existing works.
