Coding Of Signals Research Articles

Electrophysiological signature of interoceptive attention: a spectral and source localization analysis.

The ability to attend to and consciously process interoceptive signals is deemed critical for the development of minimal self, adaptive self-regulation and affective experience, and optimal expression of both instrumental and executive cognitive functions. Yet, notwithstanding the richness of theoretical proposals concerning inferential accounts of interoception, empirical evidence is still scarce. Building on such premises, this study was designed to investigate the electrophysiological signature of cognitive processes leading to aware coding of interoceptive signals via EEG source localization. Thirty-six healthy participants completed an interoceptive accuracy task, i.e., the Heartbeat Counting Task (HCT), while we collected task-related and resting state electrophysiological activity. eLORETA modelling and statistical nonparametric mapping were used to estimate intracortical current density and link such estimates to participants' performance at the task. Source analysis highlighted higher current density estimates for alpha frequencies during HCT with respect to rest, with the primary cortical generator in the right parahippocampal gyrus. Also, a set of medial cortical structures-primarily represented by the cingulate gyrus-showed significant relation between task-related changes in current density estimates for beta oscillations and HCT scores. Findings suggest the informativity of EEG task-related measures of neural activation when used to assess interoceptive skills, as well as of the potential of metrics and analysis based on source localization in the quest to improve our understanding of interoceptive accuracy and neurofunctional correlates of related active inferences.

Molecular logic for cellular specializations that initiate the auditory parallel processing pathways.

The cochlear nuclear complex (CN), the starting point for all central auditory processing, comprises a suite of neuronal cell types that are highly specialized for neural coding of acoustic signals, yet molecular logic governing cellular specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple new subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the remarkable cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level illustrates molecular logic for cellular specializations and enables genetic dissection of auditory processing and hearing disorders with unprecedented specificity.

Cation-π Interactions Based Conductive Hydrogels with Slide-Ring Structure Toward Super Long-Time in-air/Underwater Linear Sensing and Communication.

Conductive hydrogels (CHs) are attracted more attention in the flexible wearable sensors field, however, how to stably apply CHs underwater is still a big challenge. In order to achieve the usage of CHs in aquatic environments, the integrated properties such as water retention ability, resistance to swelling, toughness, adhesiveness, linear GF sensing, and long-term usage are necessary to consider, but rarely reported in the previous reports. This paper proposes CHs prepared using cationic and aromatic monomers along with polyrotaxanes-based crosslinkers. Due to the intermolecular cation-π interactions and topological slide-ring-based polyrotaxanes, the CHs exhibit good mechanical performance, adhesive nature, and anti-swelling properties. The presence of slide-ring-based topological architecture effectively mitigates stress concentration. Additionally, the encapsulation of PA allows CHs to maintain functionality even after 240 days of direct placement at room temperature. Notably, the designed CHs exhibit linear sensitivity in detecting land/underwater human motions, and serve as Morse code signal transmitters for information transmission. Thus, the designed CHs may have broad applications in the underwater wearable sensors field.

Highly durable yarn-based strain sensor with enhanced underwater monitoring capabilities and rapid drowning detection for safety applications

Flexible strain sensors have garnered significant attention for their outstanding performance in fields such as telemedicine, motion management, and human–machine interfaces. However, in the demanding marine environments, these advanced sensors face challenges related to mechanical weakness, stability, and durability under wet conditions, which are critical for expanding their range of practical applications. In this study, a stable and durable yarn sensor was developed specifically tailored for underwater activity monitoring. This was achieved by using polydopamine to immobilize silver nanoparticles, thereby creating a highly conductive layer, followed by applying an impregnation coating to establish a hydrophobic layer. The resulting composite structure featured a micro-convex layer of silver nanoparticles, which provided a durable hydrophobic surface resistant to mechanical wear and capable of maintaining its electrical properties even under high-velocity water impacts. More significantly, a FWAS was engineered, which is activated by pre-stretching the yarn sensor using a water-soluble vinylon yarn. The FWAS automatically alters its resistance within 1.3 s upon detecting someone falling into the water, thereby sending out an accurate distress signal to the external world by transmitting Morse code signals via gesture movements. This innovation provides a straightforward and effective approach for timely rescue operations from drowning, demonstrating the versatility of yarn-based flexible strain sensors for use not only in air but also in underwater scenarios.

Conjunctive processing of spatial border and locomotion in retrosplenial cortex during spatial navigation.

Spatial information and dynamic locomotor behaviours are equally important for achieving locomotor goals during spatial navigation. However, it remains unclear how spatial and locomotor information is integrated during the processing of self-initiated spatial navigation. Anatomically, the retrosplenial cortex (RSC) has reciprocal connections with brain regions related to spatial processing, including the hippocampus and para-hippocampus, and also receives inputs from the secondary motor cortex. In addition, RSC is functionally associated with allocentric and egocentric spatial targets and head-turning. So, RSC may be a critical region for integrating spatial and locomotor information. In this study, we first examined the role of RSC in spatial navigation using the Morris water maze and found that mice with inactivated RSC took a longer time and distance to reach their destination. Then, by imaging neuronal activity in freely behaving mice within two open fields of different sizes, we identified a large proportion of border cells, head-turning cells and locomotor speed cells in the superficial layer of RSC. Interestingly, some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals. Furthermore, these conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigator scenes using the border, turning and positive-speed conjunctive cells. Our study reveals that the RSC is an important conjunctive brain region that processes spatial and locomotor information during spatial navigation. KEY POINTS: Retrosplenial cortex (RSC) is indispensable during spatial navigation, which was displayed by the longer time and distance of mice to reach their destination after the inactivation of RSC in a water maze. The superficial layer of RSC has a larger population of spatial-related border cells, and locomotion-related head orientation and speed cells; however, it has few place cells in two-dimensional spatial arenas. Some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals, and the conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigation scenes. Our study reveals that the RSC is an important conjunctive brain region that processes both spatial and locomotor information during spatial navigation.

Weak signal detection capacity of type-II Morris–Lecar neuron system under presynaptic bombardments

The information encoding mechanism of the neuronal system is quite complex and is sensitive to environmental conditions. Neuronal noise, especially synaptic random inputs, is a natural part of the brain. Theoretical and experimental studies have revealed that noise plays a vital role in signal frequency selectivity and weak sensory input perception of the neuronal system. There is a consensus that sufficiently intense noise can trigger rhythmic brain activity, and these noise-induced oscillations could improve signal processing of the neuronal system, particularly when the external signal frequency aligns with the frequency of the noise-induced rhythm. Such behavior in response to noise in the signal-processing mechanism of biological systems is elucidated by stochastic resonance. In this regard, this paper systematically analyzes the stochastic resonance in a Type-II Morris–Lecar neuron under excitatory and inhibitory background spike bombardments. Our results indicate that Morris–Lecar Type II neurons enhance their signal coding capacity by exploiting background synaptic activity. In addition, it has been revealed that the biological properties of the background synaptic connections of neurons that oscillate at different frequencies, such as delta, theta, alpha, beta, and gamma bands, are different. On the other hand, it has been found that neurons adapt to the properties of pre-synaptic neurons to encode weak signals. This study ensures novel insights into the functional role of background synaptic input in neuronal coding mechanisms by demonstrating a biophysical factor of stochastic resonance via numerical analyses. Furthermore, our results show that the properties of the background synaptic inputs significantly determine what kind of signal to encode or not. These results are an important indicator of the signal frequency selectivity of neurons, such as auditory cells.

Cortical Coding of Gustatory and Thermal Signals in Active Licking Mice.

Eating behaviors are influenced by the integration of gustatory, olfactory, and somatosensory signals, which all contribute to the perception of flavor. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex. In this study, we recorded the spiking activity of more than 900 GC neurons and 500 neurons from the oral somatosensory cortex in mice allowed to freely lick small drops of gustatory stimuli or deionized water at varying non-nociceptive temperatures. We then developed and used a Bayesian-based analysis technique to assess neural classification scores based on spike rate and phase timing within the lick cycle. Our results indicate that GC neurons rely predominantly on rate information, although phase information is needed to achieve maximum accuracy, to effectively encode both chemosensory and thermosensory signals. GC neurons can effectively differentiate between thermal stimuli, excelling in distinguishing both large contrasts (14°C vs. 36°C) and, although less effectively, more subtle temperature differences. Finally, a direct comparison of the decoding accuracy of thermosensory signals between the two cortices reveals that while the somatosensory cortex showed higher overall accuracy, the GC still encodes significant thermosensory information. These findings highlight the GC's dual role in processing taste and temperature, emphasizing the importance of considering temperature in future studies of taste processing.

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis.

Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease-specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.

Ti‐Doped ZnO Thin Films‐Based Transparent Photovoltaic for High‐Performance Broadband and Wide‐Field‐of‐View Photocommunication Window

Transparent photovoltaics (TPVs) are crucial for developing next‐generation see‐through electronics. However, TPV devices (TPVDs) require wide‐bandgap materials that are typically only responsive to short wavelength (ultraviolet, UV) lights rather than visible lights. Is it possible to improve the TPV performance by harvesting the longer wavelength lights without degradation of transparency? It may be satisfied with the function of intermediate energy states, which can utilize the lower photon energy (longer wavelength light) by a two‐step transition. To achieve this, co‐sputtered Ti‐doped ZnO (Ti:ZnO) film‐based high‐performance TPVDs have been developed. Density functional theory analysis revealed the formation of intermediate energy states due to the hybridization of O 2p and Ti 3d orbitals in the Ti:ZnO system. The Ti:ZnO‐based TPVDs show 65% average visible transparency with a power production value of 655 μW cm−2. These devices exhibit good photodetection behavior under UV to visible illuminations with high responsivity and detectivity values of 1.85 A W−1 and 2.5 × 1013 Jones, respectively. Finally, a high‐performance UV to visible broadband and wide‐field‐of‐view photocommunication system is designed based on the TPVDs to generate the fast Morse code signal. Therefore, Ti doping in ZnO provides a good way to improve the device's functionality for futuristic applications.

Measuring the amplitudes of the received symbols of the shift keyed radio signals

In high-speed discrete information transmission systems, multi-position signals with various types of phase and amplitude shift keying are widely used. There are multi-level amplitude shift keyed (ASK), amplitude and phase-shift keyed (APSK) and quadrature amplitude modulation (QAM) signals. When demodulating the amplitude code of such signals, the amplitude of the received symbol is determined, and its calibrated value is compared with the specified threshold levels. It is proposed to directly calculate the minimum and maximum amplitudes of the symbols at the output of the demodulator, with a subsequent definition of decision thresholds. It is noteworthy that there is no need to calibrate the amplitude characteristics of the reception path. The logarithmic ratio of the amplitudes of the received and previous symbols is further calculated. And if its value falls within the specified boundaries, independent of the signal and noise, then a decision is made about whether the amplitudes of these symbols belong to the maximum or minimum values. These obtained values are averaged after that and used to set thresholds for decision-making in the demodulator. The results of the statistical simulation confirm the high efficiency of the proposed technical solution.

An atlas of brain-bone sympathetic neural circuits in mice.

There is clear evidence that the sympathetic nervous system (SNS) mediates bone metabolism. Histological studies show abundant SNS innervation of the periosteum and bone marrow-these nerves consist of noradrenergic fibers that immunostain for tyrosine hydroxylase, dopamine beta-hydroxylase, or neuropeptide Y. Nonetheless, the brain sites that send efferent SNS outflow to the bone have not yet been characterized. Using pseudorabies (PRV) viral transneuronal tracing, we report, for the first time, the identification of central SNS outflow sites that innervate bone. We find that the central SNS outflow to bone originates from 87 brain nuclei, sub-nuclei, and regions of six brain divisions, namely the midbrain and pons, hypothalamus, hindbrain medulla, forebrain, cerebral cortex, and thalamus. We also find that certain sites, such as the raphe magnus (RMg) of the medulla and periaqueductal gray (PAG) of the midbrain, display greater degrees of PRV152 infection, suggesting that there is considerable site-specific variation in the levels of central SNS outflow to the bone. This comprehensive compendium illustrating the central coding and control of SNS efferent signals to bone should allow for a greater understanding of the neural regulation of bone metabolism, and importantly and of clinical relevance, mechanisms for central bone pain.

Intelligent analysis method of power grid monitoring signals based on affinity propagation clustering

Abstract As the scale of the power grid continues to expand, power supply companies centralize the management of grid monitoring signals at substations to adapt to the actual development of the power grid and improve the performance of grid operation. However, this leads to problems such as too many invalid signals, unidentified signal classification, excessive signal volume, and too many interfering signals at the monitoring site on a daily basis, which leads to difficulties in monitoring and certain security risks. Addressing this problem, this paper introduces a method of spatialization of text data using unique thermal coding and intelligent analysis of monitoring signals using AP clustering. The affinity propagation clustering algorithm can classify the monitoring signals faster and more accurately, count the number of signals, and deeply excavate the intelligent information implied therein. This greatly enhances the stability and safety of power grid operation. The proposed method is validated by the actual monitoring signal data.

РОЗРОБКА ТА ДОСЛІДЖЕННЯ АЛГОРИТМІВ ВИМІРЮВАННЯ ПАРАМЕТРІВ ЕЛЕКТРОЕНЕРГІЇ У РАЗІ ВИКОРИСТАННЯ ФІКСОВАНОЇ ЧАСТОТИ ДИСКРЕТИЗАЦІЇ СИГНАЛІВ

A study of the possibilities of measuring current values, phase shift angles and other parameters of electrical networks using these data, when sampling with a frequency not multiple of the network frequency, was carried out. In the proposed algorithm, an intermediate linear approximation is used to calculate the instantaneous values of interpolated signals at equidistant moments of time during the period of the main frequency of the network. The cases when such sampling is performed on one device with sequential or simultaneous coding of input signals and the case when sampling frequencies do not match in each measurement channel are considered. A study of the use of these methods was conducted using the developed computer model and layout of the distributed measurement system. References 32, figures 19.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Exploring Time Delay Interferometry ranging as a practical ranging approach in the Bayesian framework

Time Delay Interferometry (TDI) is an indispensable step in the whole data processing procedure of space-based gravitational wave detection, as it mitigates the overwhelming laser frequency noise, which would otherwise completely bury the gravitational wave signals. Knowledge on the inter-spacecraft optical paths (i.e. delays) is one of the key elements of TDI. Conventional method for inter-spacecraft ranging mainly relies on the pseudo-random noise (PRN) code signal modulated onto the lasers. To ensure the reliability and robustness of this ranging information, it would be highly beneficial to develop other methods which could serve as cross-validations or backups. This paper explores the practical implementation of an alternative data-driven approach – time delay interferometry ranging (TDIR) – as a ranging technique independent of the PRN signal. Distinguished from previous research, our TDIR algorithm significantly relaxes the stringent requirement for clock synchronization imposed by traditional TDI procedure. By framing TDIR as a Bayesian parameter estimation problem and employing a general polynomial parametrization, we demonstrate our algorithm with simulated data based on the numerical orbit of Taiji. In the presence of laser frequency noise and secondary noises, the estimated median values of delays are only 5.28 ns away from the ground truths, capable of suppressing laser frequency noise to the desired level. Additionally, we have also analyzed the requirements of mitigating optical bench noise and clock noise on TDIR, and presented an illustrative example for the impact of laser locking.

Adaptive bridge model for compressed domain point cloud classification

The recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require first decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifier, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifier largely compatible with the state-of-the-art spatial domain PointGrid classifier. However, the available Compressed Domain PC Classifier presents strong limitations by imposing a single, specific input size which is associated to specific JPEG Pleno PCC configurations; this limits the compression performance as these configurations are not ideal for all PCs due to their different characteristics, notably density. To overcome these limitations, this paper proposes the first Adaptive Compressed Domain PC Classifier solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using different coding configurations, now maximizing the compression efficiency. Experimental results show that the novel Adaptive Compressed Domain PC Classifier allows JPEG PCC to achieve better compression performance by not imposing a single, specific coding configuration for all PCs, regardless of its different characteristics. Moreover, the added adaptability power can achieve slightly better PC classification performance than the previous Compressed Domain PC Classifier and largely better PC classification performance (and lower number of weights) than the PointGrid PC classifier working in the decompressed domain.

Correlation properties of complete code of binary spread spectrum phase-shift keyed signals with polarization coding

Problem statement. Investigation of correlation properties of complete code of binary spread spectrum phase-shift keyed signals as the limiting case of large systems is a topical task for building large systems of binary spread spectrum phase-shift keyed signals with polarization coding with optimal correlation properties. Also, this task is of independent academic interest due to the fact that the alphabet of symbols for these signals lacks multicasting properties unlike the familiar cases of complete codes of spread spectrum phase-shift keyed signals. Objective. To accurately define the distribution laws as well as the moments about zero and the mean for the n-th order of presented values of aperiodic and periodic correlation functions of the complete code of binary spread spectrum phase-shift keyed signals with polarization coding. To offer approximations of accurate distribution laws suitable for engineering applications. Results. The accurate distribution laws for presented values of aperiodic and periodic correlation functions of the complete code of binary spread spectrum phase-shift keyed signals with polarization coding were found in the form of the related distribution series. The analytic expressions for the n-th order of presented values of these functions for the complete code of the specified signals were assigned. The calculation example for the distribution series of presented values of aperiodic and periodic correlation functions for the complete code of the specified signals with the length of vector code sequences N=4 was given. The values of the relative frequencies of the correlation functions values for this case were found by the means of direct computing and their exact match with the corresponding predicted probability values was demonstrated. The approximations of the accurate distribution laws were given: for periodic correlation functions case – by Gaussian probability density, and for the aperiodic functions case – by the Pearson curve of VII type. Practical relevance. The found accurate laws for presented values of aperiodic and periodic correlation functions of the complete code of binary spread spectrum phase-shift keyed signals with polarization coding and their approximations allow to assess the correlation properties of large systems of such signals.

Survey on Visual Signal Coding and Processing With Generative Models: Technologies, Standards, and Optimization

Survey on Visual Signal Coding and Processing With Generative Models: Technologies, Standards, and Optimization

Guest Editorial Advances in Generative Visual Signal Coding and Processing

Guest Editorial Advances in Generative Visual Signal Coding and Processing

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M. The simulation results for design examples outperform the techniques in the available literature in terms of amplitude and aliasing distortion, reaching distortion around 2.4489 × 10−4 and 3.4907 × 10−9, respectively. This optimization algorithm's usefulness is further demonstrated with the sub-band coding of ECG signals. Implementing optimal prototype filters has tangible real-world effects, especially in critical sectors like healthcare and communications, improving diagnostics accuracy, data transmission efficiency, and overall performance.