Signal Propagation Research Articles

Wave propagation in a light-temperature neural network under adaptive local energy balance.

External electric and mechanical stimuli can induce shape deformation in excitable media because of its intrinsic flexible property. When the signals propagation in the media is described by a neural network, creation of heterogeneity or defect is considered as the effect of shape deformation due to accumulation or release of energy in the media. In this paper, a temperature-light sensitive neuron model is developed from a nonlinear circuit composed of a phototube and a thermistor, and the physical energy is kept in capacitive and inductive terms. Furthermore, the Hamilton energy for this function neuron is obtained in theoretical way. A regular neural network is built on a square array by activating electric synapse between adjacent neurons, and a few of neurons in local area is excited by noisy disturbance, which induces local energy diversity, and continuous coupling enables energy propagation and diffusion. Initially, the Hamilton energy function for a temperature-light sensitive neuron can be obtained. Then, the finite neurons are applied noise to obtain energy diversity to explore the energy spread between neurons in the network. For keeping local energy balance, one intrinsic parameter is regulated adaptively until energy diversity in this local area is decreased greatly. Regular pattern formation indicates that local energy balance creates heterogeneity or defects and a few of neurons show continuous parameter shift for keeping energy balance in a local area, which supports gradient energy distribution for propagating waves in the network.

Noninvasive Diagnostic Method to Objectively Measure Olfaction and Diagnose Smell Disorders by a Molecularly Targeted Fluorescence Imaging Agent.

Despite the recent advances in understanding the mechanisms of olfaction, no tools are currently available to noninvasively identify loss of smell. Because of the substantial increase in patients presenting with coronavirus disease 2019-related loss of smell, the pandemic has highlighted the urgent need to develop quantitative methods. Methods: Our group investigated the use of a novel fluorescent probe named Tsp1a-IR800P as a tool to diagnose loss of smell. Tsp1a-IR800P targets sodium channel 1.7, which plays a critical role in olfaction by aiding the signal propagation to the olfactory bulb. Results: Intuitively, we have identified that conditions leading to loss of smell, including chronic inflammation and coronavirus disease 2019, correlate with the downregulation of sodium channel 1.7 expression in the olfactory epithelium, both at the transcript and at the protein levels. We demonstrated that lower Tsp1a-IR800P fluorescence emissions significantly correlate with loss of smell in live animals-thus representing a potential tool for its semiquantitative assessment. Currently available methods rely on delayed subjective behavioral studies. Conclusion: This method could aid in significantly improving preclinical and clinical studies by providing a way to objectively diagnose loss of smell and therefore aid the development of therapeutic interventions.

ECR Spotlight – Mathilde Martin

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Mathilde Martin is an author on ‘ Use of acoustic signals in Cape fur seal mother–pup reunions: individual signature, signal propagation and pup home range’, published in JEB. Mathilde conducted the research described in this article while a PhD student in Dr Isabelle Charrier's lab at the Institute of Neurosciences Paris-Saclay (NeuroPSI), CNRS & Paris-Saclay University, France. She is now a postdoctoral researcher in the lab of Prof. Marta Manser at the University of Zurich, Switzerland, investigating acoustic communication to decipher how vocal signals modulate social interactions in group-living mammals, in relation to the species' ecological constraints.

Use of acoustic signals in Cape fur seal mother-pup reunions: individual signature, signal propagation and pup home range.

The Cape fur seal (Arctocephalus pusillus pusillus) is one of the most colonial mammals, with colonies of up to hundreds of thousands of individuals during the breeding season. During the lactation period, mothers and pups are regularly separated as females undertake multi-day foraging trips at sea. Mothers and pups use a mutual vocal recognition system to reunite after separation. Such communication is highly constrained by both high background noise and risk of individual confusion owing to the density of seals. This study aimed to experimentally assess the acoustic features relevant for mother-pup vocal identification and the propagation properties of their calls. Playback experiments revealed that mother and pup individual vocal signatures rely on both temporal and frequency parameters: amplitude and frequency modulations, timbre and fundamental frequency (f0). This is more parameters than in any colonial species studied so far. The combinational use of acoustic features reinforces the concept that both environmental and social constraints may have acted as selective pressures on the individual vocal recognition systems. Theoretical propagation distances of mother and pup vocalisations were estimated to be below the range of distances at which mother-pup reunions can occur. This suggests that Cape fur seals may have strong abilities to extract vocal signals from the background noise, as previously demonstrated in the highly colonial king penguin. Investigating the transmission of information throughout the propagation of the signal as well as the ability of the receiving individual to decipher vocal signatures is crucial to understanding vocal recognition systems in the wild.

Anomalous to normal dispersion nonlinear optical dephasing switch in electromagnetically induced transparency using a Kerr effect

The atomic decoherence effect (DE) on a Kerr nonlinear (KNL) electromagnetically induced transparency (EIT)is studied in a Δ system. The DE between the ground state hyperfine levels is caused by the dephasing rate γ d which dramatically modifies the medium response. It controls the normal dispersive region which shows steep positive slopes for linear response at the line center while the nonlinear response experiences steep negative slopes for low γ d . The microwave field strength and γ d modify the nonlinear response from the anomalous dispersion to normal dispersion. The calculations show that room-temperature atoms are used to quantify the quantum interference (QI) on linear and nonlinear absorption with γ d . The EIT spectrum explores the understanding of the subluminal and superluminal wave propagation of probe signal and this study opens a new pathway for the understanding of the QI devices and their nonlinearities based on EIT.

Graphical Heatmap-Based Approach to Indoor Radio Signal Propagation: Adapting Advanced Ray Tracing and Global Illumination Algorithms

Graphical Heatmap-Based Approach to Indoor Radio Signal Propagation: Adapting Advanced Ray Tracing and Global Illumination Algorithms

Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

Comparing the Performance of Optical Communication Links using G.652 and G.655 Fiber in Python Packages

This study investigates and compares the performance of a 10 Gbps optical communication link utilizing two prevalent single-mode fibers: G.652 and G.655. The analysis employs both theoretical calculations and Python-based simulations to assess the effectiveness of each fiber type in this high-speed transmission scenario. With the ever-growing demand for bandwidth in communication networks, 10 Gbps transmission systems are becoming increasingly commonplace. Single-mode fibers like G.652 and G.655 play a vital role in these systems, offering low signal attenuation for long-distance data transmission. However, each fiber type exhibits distinct dispersion characteristics, which can impact signal integrity over extended distances. This investigation adopts a two method for performance evaluation. Firstly, link power budget calculations are performed to determine the optical signal power before and after propagating through a 50-kilometer fiber span. The received power serves as the foundation for subsequent Q-factor and Bit Error Rate (BER) analysis. These calculations establish the theoretical limitations of the system based on well-defined formulas. Secondly, Python-based simulations are conducted to corroborate the theoretical findings and provide a more comprehensive performance assessment. This approach leverages the capabilities of two prominent Python packages: Opticomlib and OpticommPy. Opticomlib excels at analyzing the behavior of individual optical pulses within the system, enabling an understanding of the signal propagation. On the other hand, OpticommPy specializes in parameter sweep analysis, allowing for the investigation of how critical parameters like received power influence the Q-factor. By combining these functionalities, the simulations provide a detailed picture of the system's performance under various conditions. The calculated BER and Q-factor values for both G.652 and G.655 fiber links surpass the industry-accepted performance standards. These results demonstrate the effectiveness of using Python-based tools for comprehensive performance analysis of optical communication systems. However, it's important to note that slight discrepancies exist between the calculated and simulated results.

Gap-junction-mediated bioelectric signaling required for slow muscle development and function in zebrafish

Bioelectric signaling, intercellular communication facilitated by membrane potential and electrochemical coupling, is emerging as a key regulator of animal development. Gap junction (GJ) channels can mediate bioelectric signaling by creating a fast, direct pathway between cells for the movement of ions and other small molecules. In vertebrates, GJ channels are formed by a highly conserved transmembrane protein family called the connexins. The connexin gene family is large and complex, creating challenges in identifying specific connexins that create channels within developing and mature tissues. Using the embryonic zebrafish neuromuscular system as a model, we identify a connexin conserved across vertebrate lineages, gjd4, which encodes the Cx46.8 protein, that mediates bioelectric signaling required for slow muscle development and function. Through mutant analysis and invivo imaging, we show that gjd4/Cx46.8 creates GJ channels specifically in developing slow muscle cells. Using genetics, pharmacology, and calcium imaging, we find that spinal-cord-generated neural activity is transmitted to developing slow muscle cells, and synchronized activity spreads via gjd4/Cx46.8 GJ channels. Finally, we show that bioelectrical signal propagation within the developing neuromuscular system is required for appropriate myofiber organization and that disruption leads to defects in behavior. Our work reveals a molecular basis for GJ communication among developing muscle cells and reveals how perturbations to bioelectric signaling in the neuromuscular system may contribute to developmental myopathies. Moreover, this work underscores a critical motif of signal propagation between organ systems and highlights the pivotal role of GJ communication in coordinating bioelectric signaling during development.

Research on Time-Reversal Focusing Imaging Method to Evaluate a Multi-Layer Armor Composite Structure

Armor composite structures have attracted interest in structural health monitoring (SHM) for their applications in damage localization. The signal propagation and the frequency dispersion features of the Lamb wave signal on thick armor composite structures are more complicated than their counterparts on other composite plates. In this research, a time-reversal localization and imaging method for impact localization of armor composite structures is proposed. First, composite sandwich structures were designed that are typically composed of ballistic-resistant ceramic materials as the face panel and a composite material as the core layer, sandwiched between metal materials serving as the backplate. The results show that the proposed method can validate the position of impact efficiently, and radial error is within 4.12 mm and 5.39 mm in single-damage and multi-damage imaging localization, respectively.

Protein Kinase A Regulates Platelet Phosphodiesterase 3A through an A-Kinase Anchoring Protein Dependent Manner.

Platelet activation is critical for haemostasis, but if unregulated can lead to pathological thrombosis. Endogenous platelet inhibitory mechanisms are mediated by prostacyclin (PGI2)-stimulated cAMP signalling, which is regulated by phosphodiesterase 3A (PDE3A). However, spatiotemporal regulation of PDE3A activity in platelets is unknown. Here, we report that platelets possess multiple PDE3A isoforms with seemingly identical molecular weights (100 kDa). One isoform contained a unique N-terminal sequence that corresponded to PDE3A1 in nucleated cells but with negligible contribution to overall PDE3A activity. The predominant cytosolic PDE3A isoform did not possess the unique N-terminal sequence and accounted for >99% of basal PDE3A activity. PGI2 treatment induced a dose and time-dependent increase in PDE3A phosphorylation which was PKA-dependent and associated with an increase in phosphodiesterase enzymatic activity. The effects of PGI2 on PDE3A were modulated by A-kinase anchoring protein (AKAP) disruptor peptides, suggesting an AKAP-mediated PDE3A signalosome. We identified AKAP7, AKAP9, AKAP12, AKAP13, and moesin expressed in platelets but focussed on AKAP7 as a potential PDE3A binding partner. Using a combination of immunoprecipitation, proximity ligation techniques, and activity assays, we identified a novel PDE3A/PKA RII/AKAP7 signalosome in platelets that integrates propagation and termination of cAMP signalling through coupling of PKA and PDE3A.

Risks of Multipath Signal Propagation From GPS Satellites in Mountainous Areas During the Use of UAV as Supplementary Device for HEMS or SAR

It cannot be disputed that using unmanned aerial vehicles as a supplementary search and rescue component is still increasingly being discussed. Several approaches to solving the problem can be stated - buying specially designed unmanned aerial vehicles for HEMS (Helicopter Emergency Medical Service)/SAR (Search and Rescue) or modifying conventional UAVs to supplementary devices for HEMS or SAR. Regardless of which alternative the end-user prefers, the navigation method and determining the current position in space stays unchanged - primarily, the navigation signals from GNSS (Global Navigation Satellite Systems) satellites are used. However, this method can be loaded by the error caused by environmental conditions in which the rescue UAV moves, such as mountains, valleys, specific flora, trees, and much more. All of that causes attenuation, loss or multipath propagation of the signal. In the case of the remote-controlled flight of the UAV (by a pilot-operator or operator itself from the ground station) and when the rule of direct visibility (Visual Line of Sight - VLOS) is used for an unmanned aerial vehicle, this may not be a considerable problem. However, during an automated or fully autonomous flight and the flight of an unmanned aerial vehicle beyond direct visibility (Beyond Visual Line of Sight - BVLOS), knowledge about the exact position of the UAV in space is crucial. The presented article is dedicated to verify the claim about the significance of the problem of multipath signal propagation from the GPS (Global Positioning System) satellite in a selected mountain area, The Little Cold Valley in High Tatras, Slovak Republic.

Моделирование КВ-радиотрасс на основе волноводного подхода

We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a complex algorithm for simulating propagation conditions of HF radio signals, which includes a medium model, algorithms for calculating signal characteristics, and operational diagnostics of radio channel. We have compared the results of the HF signal propagation characteristic modeling and the experimental oblique sounding data obtained along paths of different lengths and orientation. To analyze experimental ionograms, determine the maximum usable frequencies for propagation modes along radio paths, we employ the method of automatic processing and interpretation of oblique sounding ionograms.

HF radio path modeling by waveguide approach

We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a complex algorithm for simulating propagation conditions of HF radio signals, which includes a medium model, algorithms for calculating signal characteristics, and operational diagnostics of radio channel. We have compared the results of the HF signal propagation characteristic modeling and the experimental oblique sounding data obtained along paths of different lengths and orientation. To analyze experimental ionograms, determine the maximum usable frequencies for propagation modes along radio paths, we employ the method of automatic processing and interpretation of oblique sounding ionograms.

Enhancing longevity: Sustainable channel modeling for wireless-powered implantable BANs

Wireless body area network (WBAN) has revolutionized the healthcare sector by enabling remote monitoring and control of wearable and implantable devices, providing freedom of mobility to patients. However, wireless channel modeling in BAN is a crucial aspect for designing an efficient off-body, on-body and in-body communication. Due to the unique characteristics of the human body, it aims to characterize the signal propagation through skin, tissues, internal organs and biological fluids of a patient’s body. Moreover, it is important to enhance the battery life of the low-powered devices for a sustainable BAN. In this work, we provide a hybrid communication channel model for wireless power transfer in a BAN including both off-body and in-body communication channels. An indoor room scenario is considered in which a movable patient having an implant inside its body is present along with an RF power source (for example, a Wi-Fi access point) situated in a ceiling corner. Implant is assumed to inhibit energy harvesting capability. For practicability, we have considered the effect of path loss, partition walls, floor attenuation factor along with other important body parameters. Specifically, we aim to statistically characterize this hybrid communication system, for which unique closed-form expressions of the probability distribution functions of the received power have been derived, thereby first calculating the instantaneous power at different layers of human body and then obtaining the closed-form expression for average received power. All the derived mathematical expressions have been verified via numerical simulations. Further, for elongating the lifespan of implants, we investigated the average power harvested by an implant and its power outage probability for analyzing the sustainability of implants. The results are numerically validated, considering different types of indoor room scenarios, in addition to providing key design insights.

Information flow and catalytic dyad in SARS-CoV2 main protease Mpro enzyme using embedded discrete Markov chains and centrality measures

Abstract In this work, we use a network representation of the globular crystalline structure of a given protein as a graph structure with N nodes and E edges in order to analyze quantitatively the flow information and to identify key sites within the globular structure. Each node nj represents a $ C_{\alpha}^{i}$ carbon of the main backbone whereas the node’s degree ki is a measure of its physical interactions. In order to identify biologically relevant and active nodes, we compute local per residue closeness $ C_{c}(i)$, betweenness $ C_{b}(i)$ and eigenvector centralities $ C_{e}(i)$. Further analysis is done by embedding a stochastic dynamic discrete Markov chain in order to evaluate the dynamics of a set of normal random walkers (NRW’s) within the network. From this, we compute the mean first passage time matrix M and the stationary occupation probability vector ψi for each node. These two measures provide very useful information on the dynamical process embed within the $ C_{\alpha}$ network. We apply this to SARS-CoV2 Mpro main protease which is a key enzyme in the virus replication cycle. In particular, we focus our attention to the properties of the catalytic dyad integrated by His41-Cys145 in Mpro main protease since this active site has been under intense scrutiny as a pharmaceutical target. In addition, our results show the existence of additional relevant aminoacids that might play a fundamental role on signal propagation and allosteric pathways in SARS-CoV main proteases.

Wykorzystanie metody radaru penetrującego grunt w badaniu gleby zanieczyszczonej węglowodorami w obszarze Navodari - Rumunia

Ground penetrating radar (GPR) is a very useful geophysical method for use in hydrogeologic and near-surface mapping studies. It can be used to study contaminants in groundwater, subsurface faulting, and underground cavities (natural or man-made), all of which pose potentially dangerous geological hazards. The GPR technique is similar in principle to seismic reflection and sonar techniques. The propagation of the radar signal depends on the frequency-dependent electrical properties of the ground.Electrical conductivity of the soil or rock materials along the propagation paths introduces significant absorptive losses which limit the depth of penetration into the earth formations and is primarily dependent upon the moisture content and mineralization present.Reflected signals are amplified, and transformed to the audio-frequency range, recorded, processed, and displayed. From the recorded display, subsurface features such as soil/ soil, soil/rock, and unsaturated/saturated interfaces can be identified. In addition, the presence of floating hydrocarbons on the water table, the geometry of contaminant plumes, and the location of buried cables, pipes, drums, and tanks can be detected. The GPR data are presented as a two-dimensional depth profile along a scanned traverse line in which the vertical axis is two-way travel time measured in nanoseconds. The location of hydrocarbon contamination in the ground using the GPR method is based mainly on information taken from reflected signals. In the cases investigated in Romania contaminated sites (Navodari area), such signals were very rarely recorded. A long time after spillage, contamination takes the form of plumes with different size and distribution, which depends on the geological and hydraulic properties of the ground. The survey discussed in this paper was carried out using the GPR system-Noggin with two antennas (250 and 500mHz) Data collected were processed using software(EKKO_Project™ GPR Data Analysis) to produce 2D radargram in time scale. The presence of contaminant plumes as well as the water table are observed in the GPR sections at depths approximately of 0.5 to 1.5 m. In the GPR section, the oil contaminated layer exhibits discontinuous, subparallel, and chaotic high amplitude reflection patterns. Promising results were also obtained in the GPR survey where three obvious reflection patterns representing the top sand-silt layer, oil-contaminated zone and, the underlying thick soft clay were detected in all 2D radargrams of the GPR traverse lines.

CDT: Cross-interface Data Transfer scheme for bandwidth-efficient LoRa communications in energy harvesting multi-hop wireless networks

With the capability of generating sustainable energy by exploiting the ambient environment (e.g., light, heat, vibrations, etc.), the energy harvesting (EH) technology is increasingly used on low-power smart objects, forming self-powered green Internet of Things (IoTs). Despite targeting different application domains, many of these green IoT systems adopt the distributed network paradigm by forming energy harvesting multi-hop wireless networks (EH-MWNs). While distributed networks have good scalability during the deployment phase, they are often complex, inelastic to change, and costly to manage. To this end, LoRa, a promising long-range wireless technology, has been suggested for centralized single-hop network controls in EH-MWNs. However, the signal propagation of LoRa is severely affected by the surrounding environment (e.g., obstructions, terrains, etc.), which not only creates a significant difference in packet delivery rate (PDR) between nodes but also increases the bandwidth cost for network controls. To address this issue, we propose a Cross-interface Data Transfer (CDT) scheme, which leverages the ZigBee interfaces coexisting in EH-MWNs to transfer LoRa data traffic from the nodes with a lower PDR to the nodes with a higher PDR for bandwidth-efficient transmissions. By jointly determining the pairs of nodes performing data transfer and the data transfer rates between them, CDT can reduce the overall LoRa bandwidth consumption while ensuring continuous operation of the EH-MWN. A prototype system is implemented by integrating commercial off-the-shelf LoRa and ZigBee interfaces into an IoT platform. Extensive real-world experiments show that under a moderate data traffic input, the bandwidth consumption of CDT is 25.8% and 48.8% lower than the standard LoRaWAN and a state-of-the-art data transfer scheme, respectively. The average energy cost per node is kept as low as 2.29 mW at the same time. Moreover, CDT also shows its performance advantages in terms of PDR and sustainability.

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

Introducing the double validation metric for radar sensor models

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these sensors, as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called double validation metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation in comparison to the state of the art of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.