Athanassios Skodras

Antonios Lykourinas, Chinmay Pendse, Francky Catthoor et al.

Ultrasound (US) has emerged as a promising modality for Human-Machine Interfaces (HMIs), with recent research efforts exploring its potential for Hand Pose Estimation (HPE). A reliable solution to this problem could introduce interfaces with simultaneous support for up to 23 degrees of freedom encompassing all hand and wrist kinematics, thereby allowing far richer and more intuitive interaction strategies. Despite these promising results, a systematic comparison of models, input modalities and training strategies is missing from the literature. Moreover, there is only one publicly available dataset, namely the Ultrasound Adaptive Prosthetic Control (Ultra-Pro) dataset, enabling reproducible benchmarking and iterative model development. In this paper, we compare the performance of six different deep learning models, selected based on diverse criteria, on this benchmark. We demonstrate that, by using a step learning rate scheduler and the envelope of the RF signals as input modality, our 4-layer deep UDACNN surpasses XceptionTime's performance by $2.28$ percentage points while featuring $87.52\%$ fewer parameters. This result ($77.72\%$) constitutes an absolute improvement of $0.88\%$ from previously reported baselines. According to our findings, the appropriate combination of model, preprocessing and training algorithm is crucial for optimizing HMI performance.

Sofia Iliopoulou, Dimitris Ampeliotis, Athanassios Skodras

Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by jointly learning compact latent representations and probabilistic entropy models. However, many existing approaches achieve high rate-distortion efficiency at the expense of increased computational cost and limited parallelism. This paper presents ARCHE - Autoregressive Residual Compression with Hyperprior and Excitation, an end-to-end learned image compression framework that balances modeling accuracy and computational efficiency. The proposed architecture unifies hierarchical, spatial, and channel-based priors within a single probabilistic framework, capturing both global and local dependencies in the latent representation of the image, while employing adaptive feature recalibration and residual refinement to enhance latent representation quality. Without relying on recurrent or transformer-based components, ARCHE attains state-of-the-art rate-distortion efficiency: it reduces the BD-Rate by approximately 48% relative to the commonly used benchmark model of Balle et al., 30% relative to the channel-wise autoregressive model of Minnen & Singh and 5% against the VVC Intra codec on the Kodak benchmark dataset. The framework maintains computational efficiency with 95M parameters and 222ms running time per image. Visual comparisons confirm sharper textures and improved color fidelity, particularly at lower bit rates, demonstrating that accurate entropy modeling can be achieved through efficient convolutional designs suitable for practical deployment.