In this paper we propose and present a straightforward generalization of the Hamburger moment problem for a single random variable to higher-order stochastic processes via generalized tensor approach. The problem is considered as the Nevanlinna-Pick type tensor moment interpolation (often termed also the Schur interpolation) vs. covariance interpolation problem for 2M-th order stochastic processes. Its orthogonal solution via the generalized tensor Schur Transform and the Lossless Inverse Scattering (LIS) theory is presented in our second paper submitted to this conference.
This paper presents a multi-layered augmented image encryption algorithm that combines the Knight’s Tour scrambling algorithm, DNA encoding, a 4D hyperchaotic system-based key and S-box, and a Mersenne Twister-generated key. The process begins with the generation of a pseudo-random key using the 4D hyperchaotic system, which is XORed with the original image data. The scrambled image is then further processed using Knight’s Tour paths generated by a Hamiltonian path algorithm. DNA encoding is applied next, preparing the data for logical operations and the application of the hyperchaotic S-box. Finally, a second key generated by the Mersenne Twister is used in an additional XOR operation to produce the fully encrypted image. The proposed method is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR), histogram analysis, and information entropy, demonstrating excellent encryption performance and strong resistance to various attacks. Moreover, the scheme features an extensive key space of 22604, ensuring robust security against brute-force attacks.
The paper presents the design and implementation of a modular perception and control system for an autonomous surface vehicle (ASV), developed on a System-on-Module (SoM) embedded platform. The system was created with the goal of participating in the international RoboBoat competition. The high-level architecture integrates data from a stereo camera, LiDAR sensor, and a GNSS unit with an Inertial Measurement Unit (IMU), enabling object detection, simultaneous localization and mapping (SLAM), and autonomous navigation. We evaluated the use of a quantized convolutional neural network (YOLOv11) for object classification, obstacle detection based on LiDAR data, and control module operation using NAV2 within the ROS 2 environment under limited computational resources. Accelerating visual detection algorithms with CUDA and TensorRT libraries on the onboard NVIDIA Jetson Orin Nano platform enabled effective real-time and low-energy classification. Tests carried out in simulation demonstrated the correct operation of the perception and control system. The average runtime of the system using the quantised YOLO model with GPU was 26.01 ms. This equates to an approximate speed increase of 6.7 times compared to inference performed on an ARM processor, and an approximate speed increase of 5.5 times compared to a standard CPU.
The article concerns the evaluation of the impact of microwave radiation with a power of 700 W on the neutral and acidic food safety heated in currently popular plant-based materials intended for contact with food (so called food contact materials, FCMs). Low molecular weight carbonyl compounds (especially formaldehyde, acetaldehyde and acetone) were treated as markers of undesirable dynamic FCM-food interactions. In the migration studies, appropriate food simulants were used, imitating neutral food (distilled water, 10% ethanol) and acidic food (3% acetic acid). The preliminary preparation of samples for testing, including derivatization, extraction and purification, enabled the use of a sensitive method: gas chromatography with an electron capture detector (GC-ECD) to identify and quantify migrating organic contaminants from plant-based FCMs to neutral and acidic food. It enabled to assess the safety of ecological materials for rapid heating of food in microwave ovens.
Event-based cameras are gaining popularity as the sensor of choice for mobile robotics, due to their high performance in dynamic environments. However, these applications require efficient real-time data processing with low latency and power consumption. One strategy to meet these stringent requirements is hardware acceleration of efficient algorithms that preserve the temporal sparsity of event data. In this work, we propose an optimization strategy for Graph Convolutional Neural Networks models aimed at adapting their architecture to the limited resources of embedded heterogeneous FPGA platforms. Our method incorporates hardware-aware pruning and quantization, taking into account the trade-off between on-chip memory savings and inference accuracy. Strategic exploration of the design space with Fine Grid Search and Greedy layer-wise Iterative Deepening Search methods enables flexible adaptation of the model architecture to the target platform. Our approach was evaluated across various network configurations and multiple datasets, resulting in BRAM memory reductions of 28.8
