Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems....
The increasing demand for accurate and efficient Global Navigation Satellite System (GNSS) receivers, especially in smartphones, has driven the need to combine multi- GNSS signals. Current receivers, based on Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) signals, often process these signals side-by-side, increasing complexity, size, and power consumption. Meanwhile, the next generation of smartphones and navigation applications will involve Galileo receivers to improve positioning accuracy, faster time to first fix, and reduce the multipath effect, especially in urban canyons or dense forests. Therefore, to avoid the drawbacks of sideby- side implementation; i.e. minimize the large size, and preserve processing time with low power consumption, this work presents a novel approach to simultaneously acquire GPS L1 and Galileo E1 signals in a single processing chain. This achieved by taking advantage of both GPS and Galileo signals are sharing the same carrier frequency and chipping rate. However, our solution requires just a single pre-processing step to overcome the subcarrier effect and one extra multiplication for the Galileo code. The results based on Monte Carlo simulation show that the combining solution; firstly, maintains the acquisition performance for each signal and has the same performance as acquiring each signal alone. Secondly, offers about 48% reduction in the implementation/computational complexity. Eventually, the solution preserves the reliability of the acquisition process by comparing the ratio of the highest peak to the average value in the correlation process, where the results illustrate that both GPS and Galileo signals have the same ratio tendency....
Developing spherical sector harmonics (SSHs) benefits sound field decomposition and analysis over spherical sector regions. Although SSHs demonstrate potential in the field of spatial audio, a comprehensive investigation into their properties and performance is absent. This paper seeks to close this gap by revealing three key limitations of SSHs and exploring their performance in two aspects: sector sound field radial extrapolation and sector sound field decomposition and reconstruction. First, SSHs are not solutions to the Helmholtz equation, which is their main limitation. Then, due to the violation of the Helmholtz equation, SSHs lack the ability to conduct sound field radial extrapolation, especially for interior cases. Third, when using SSHs to decompose and reconstruct a sound field, the shifted associated Legendre polynomials and scaled exponential function in SSHs result in severe distortion around the edge of the sector region. In light of these three limitations, the future implementation of SSHs should focus on processing and analyzing the measurement sector region without any extrapolation process, and the measurement region should be larger than the target sector region....
In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS–Gaussian random matrix to compress the signal. However, the signal is not sparse which makes the reconstruction algorithms ineffective. We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. The proposed RFDM establishes a novel signal modulation and detection method to target better transmission efficiency, and the simulation results show that the proposed method can achieve good BER, offering a new research paradigm to improve the spectrum efficiency of a multi-subcarrier, multi-antenna, multi-user system....
In recent years, new algorithms have been continuously applied in the field of geophysical data processing, all of which have achieved good results. However, there is currently no dedicated signal separation method for selfpotential field signal processing. In this paper, we propose a self-potential signal separation algorithm based on non-negative matrix factorization (NMF) to perform blind source signal separation. We aim to separate different self-potential signals from the collected mixed signals, laying the foundation for subsequent work such as feature recognition. We utilized analytical formulas of simple polarization bodies and forward modeling procedures to generate a series of selfpotential signal data. Subsequently, we conducted numerical simulation experiments for signal separation. The numerical simulation results demonstrate that the proposed algorithm is capable of separating self-potential signals of different models from mixed signals....
Loading....