Channel Response Research Articles

Zinc selenide based dual-channel SPR optical biosensor for HIV genome DNA hybridization detection

Simultaneous measurement of human immunodeficiency virus (HIV) genome DNA hybridization and the DNA melting temperature in a prism-based surface plasmon resonance (SPR) biosensor is modeled theoretically using a simple dual-channel construction. The proposed sensor consists of a BK7 prism coated with silver as a plasmonic material. The metal surface is divided into two channels to detect medium refractive index (RI) and temperature. One half is covered with zinc selenide (ZnSe) semiconductor to enhance the hybridization detection sensitivity and to protect silver from oxidation. The other half is covered with polydimethylsiloxane (PDMS) polymer to detect the temperature variations. The proposed sensor is optimized numerically, and the optimum structure provides an excellent sensitivity of 208 deg/RIU, thanks to the use of the ZnSe layer, which is greater than double the reported dual-channel prism-based sensor in thickness. The polymer channel shows high sensitivity to the temperature variations of − 0.125 deg/°C, which is nearly 10 times the response of the RI channel to temperature variations. The data obtained from the polymer channel is used to compensate for the thermal perturbations of the sensing medium RI, and at the same time, to monitor the increments of the temperature in order to avoid reaching the DNA melting temperature. A mathematical expression is provided to consider the effect of the temperature variations on the RI of the sensing medium to get a better accurate detection process. The DNA hybridization detection of HIV is theoretically discussed in detail starting from the preparation of the sensing medium with the different ingredients until the hybridization between probe and complementary target DNA (ct-DNA) molecules.

A Transformer-Based Channel Estimation Method for OTFS Systems.

Orthogonal time frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels, and a new method of channel response prediction is performed. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity.

A high-throughput electrophysiology assay to study the response of PIEZO1 to mechanical stimulation.

PIEZO1 channels are mechanically activated cation channels that play a pivotal role in sensing mechanical forces in various cell types. Their dysfunction has been associated with numerous pathophysiological states, including generalized lymphatic dysplasia, varicose vein disease, and hereditary xerocytosis. Given their physiological relevance, investigating PIEZO1 is crucial for the pharmaceutical industry, which requires scalable techniques to allow for drug discovery. In this regard, several studies have used high-throughput automated patch clamp (APC) combined with Yoda1, a specific gating modifier of PIEZO1 channels, to explore the function and properties of PIEZO1 in heterologous expression systems, as well as in primary cells. However, a combination of solely mechanical stimulation (M-Stim) and high-throughput APC has not yet been available for the study of PIEZO1 channels. Here, we show that optimization of pipetting parameters of the SyncroPatch 384 coupled with multihole NPC-384 chips enables M-Stim of PIEZO1 channels in high-throughput electrophysiology. We used this approach to explore differences between the response of mouse and human PIEZO1 channels to mechanical and/or chemical stimuli. Our results suggest that applying solutions on top of the cells at elevated pipetting flows is crucial for activating PIEZO1 channels by M-Stim on the SyncroPatch 384. The possibility of comparing and combining mechanical and chemical stimulation in a high-throughput patch clamp assay facilitates investigations on PIEZO1 channels and thereby provides an important experimental tool for drug development.

Image super resolution based channel estimation for future wireless communication

In this paper, a novel image-based Deep Learning (DL) approach for channel estimation for future wireless communications is proposed. The time–frequency response of the fast-fading wireless channel is represented as a 2D image to estimate the unknown values of the channel response using known values at the pilot locations. With given images, both image Super-Resolution (SR) and image Denoising Network (DnN), termed as Super-Resolution and Denoising Network (SRDnN), are combined to estimate the wireless channel. To show the effectiveness, the proposed SRDnN is applied to Massive Multiple-Input Multiple-Output (mMIMO) with Non-Orthogonal Multiple Access (NOMA). The enhanced performances of SRDnN are quantified in terms of Mean Square Error (MSE) and Symbol Error Rate (SER). In addition, the influence of pilot numbers on SRDnN performance for next-generation mMIMO-NOMA networks is presented. The simulation results demonstrate that SRDnN is comparable to the level of Maximum Likelihood (ML) detection for both with and without complete Channel State Information (CSI) at the receiver with less number of pilots.

Model-based clustering of multipath propagation in powerline communication channels

Powerline communication (PLC) channels are known to exhibit multipath propagation behaviour. The authors present a model-based framework to address the challenge of clustering multipath propagation components (MPCs) in PLC channels for indoor low-voltage (LV) environments. The framework employs a range of finite-mixture models (FMMs), including the gamma mixture model, the inverse gamma mixture model, the Gaussian mixture model, the inverse Gaussian mixture model, the Nakagami mixture model, the inverse Nakagami mixture model (INMM) and the Rayleigh mixture model, to identify clusters of MPCs. A measurement campaign of an unknown indoor LV PLC channel is conducted to obtain a channel response. From the channel response, the delay and magnitude parameters of the MPCs are extracted using the space-alternating generalised expectation maximisation algorithm adopted only for these parameters. A maximum likelihood approach and the expectation–maximisation algorithm are employed to fit the FMMs to the MPC delay-magnitude dataset to cluster MPCs in the delay domain. The results of the model-fitting process are then evaluated using the corrected Akaike information criterion (AICc), which enables a fair comparison of the candidate models over the feasible and finite range of clusters. A novel algorithm is introduced for estimating the feasible and finite range of clusters using the extracted delay and magnitude MPC parameters. The AICc’s ranking results show that the INMM model provides the best fit. Davies–Bouldin (DB) and Calinski–Harabasz (CH) indexes are used to compare the model-based clustering approach to the conventional distance-based clustering methods. Validation results show that CH and DB indexes closely agree in the optimal number of MPC clusters for model-based clustering, which corresponds to the most within-cluster compactness of MPCs and to the most between-cluster separation in the delay domain.

Numerical simulations of drill-string responses for downhole acoustic telemetry

In petroleum engineering, one major challenge for logging while drilling is that the transmission rate of downhole telemetry is too low for real-time evaluations. Downhole acoustic telemetry (DAT) uses acoustic guided waves in a drill string with periodic structures to transmit measurement data, expected to break through low-speed telemetry limitations. However, the lack of effective modeling methods makes the channel response difficult to predict, severely restricting equipment development and field applications. We propose a novel modeling approach to study the responses of the DAT channel. Excitation and propagation mechanisms of the mode waves in the DAT channel are investigated. The 2-D modeling issue is approximated to the 1-D plane-wave propagation along the borehole, considering transmission, reflection, and interconversion of the drill-string and fluid waves. The coefficients of each wave component and the full-wave channel function are derived from the transfer matrix. The efficiency and accuracy of the proposed means are validated by comparison with the finite difference. Numerical results show that the fluid-solid coupling leads to a generalized multipath effect in the DAT channel, making the acoustic responses exhibit discrete bandgaps and nonlinear phase distortion. This study provides a basis for frequency band selection and design for DAT systems.

Channel estimation with Zadoff–Chu sequences in the presence of phase errors

AbstractDue to their perfect periodic autocorrelation property, Zadoff–Chu sequences are often used as stimulus signals in the measurement of radio channel responses. In this letter, the cross‐correlation of a linear shift‐invariant system's response to a Zadoff–Chu sequence is derived for the case that a multiplicative perturbation such as phase noise is present at its output. The perturbation signal is shown to incur a spurious discrete Fourier transform, thus degrading the correlation's peak to sidelobe ratio. The implications for the performance limits of channel measurement systems are discussed.

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

Efficient Channel Equalization and Performance Analysis for Uplink FBMC/OQAM-Based Massive MIMO Systems

For offset quadratic amplitude modulation filter bank multi-carrier (FBMC/OQAM) based massive multiple-input multiple-output (MIMO) systems, a response difference phenomenon in the process of designing multi-tap equalizers is founded in this paper, which degrades the equalization performance. To address this issue, we propose a multi-stage equalization based design scheme of multi-tap equalizers with low implementation complexity. The scheme can alleviate the impact brought by the phenomenon effectively and improve the equalization performance significantly. Besides, we analyze the signal-to-interference-plus-noise ratio (SINR) and ergodic achievable rate performance of the proposed scheme, and further elaborate its self-equalization property that inter-user interference and noise vanishes as the number of BS antennas increases. In this case, there only remains some residual interference within the same user, which is tiny enough to be ignored. Thus, SINR can approach the signal-to-interference ratio (SIR) upper bound, i.e., the SIR in single-input single-output (SISO) scenario when the channel response is unit impulse. Simulation results confirm the performance analysis and show that the proposed scheme with shorter equalizer length can outperform other schemes with longer equalizer length. Moreover, even with finite number of BS antennas, the interference brought by the channels can be almost eliminated.

Automatic Indoor Radio Map Construction and Localization via Multipath Fingerprint Extrapolation

For fingerprint-based localization, the time-consuming and labor-intensive construction of offline radio map is the bottleneck which hinders its large-scale implementation. In this paper, we propose a radio map extrapolation and localization algorithm by exploiting the angles and delays of specular multipath components. First, using the concept of virtual anchor nodes (VANs), we calculate the virtual transmitter (VT) of each multipath component according to angle and delay information measured at a known point. By estimating the positions of reflector with these VTs, the angles and delays of the uplink multipath components transmitted at other locations in the indoor environment are extrapolated. Then, a channel fingerprint composed of the extrapolated angles and delays is proposed to represent the channel response in the angle-delay domain, which is spatially unique and discriminative. Thus, the radio map constructed such can significantly reduce the labor and time costs. Lastly, a convolutional neural network (CNN) is applied for indoor localization. The performance of the proposed fingerprint extrapolation and localization method is validated through extensive simulations with a ray-tracing channel model, which exhibits promising localization performance for our proposed scheme with reduced construction costs.

An Enhanced Approach to Virtually Increase Quasi-Stationarity Regions Within Geometric Channel Models for Vehicular Communications

Vehicular communication channels are intrinsically non-stationary, as they present high mobility and abundant dynamic scatterers. Quasi-stationary regions can assess the degree of non-stationarity within a determined scenario and time variant observation of the channel can be extracted. These regions can aid geometrical models as to increase channel sampling intervals or to develop hybrid stochastic-geometric channel models. In this work, a new methodology for the use of virtual quasi-stationary regions within geometric channel models is proposed, in order to leverage the inherent location information to virtually increase their size. Overall, the use of delay-shifted channel responses improves the mean correlation coefficient between consecutive locations, ultimately reducing computation time for time-variant geometric channel models.

Modulation of Voltage-Gating and Hysteresis of Lysenin Channels by Cu2+ Ions.

The intricate voltage regulation presented by lysenin channels reconstituted in artificial lipid membranes leads to a strong hysteresis in conductance, bistability, and memory. Prior investigations on lysenin channels indicate that the hysteresis is modulated by multivalent cations which are also capable of eliciting single-step conformational changes and transitions to stable closed or sub-conducting states. However, the influence on voltage regulation of Cu2+ ions, capable of completely closing the lysenin channels in a two-step process, was not sufficiently addressed. In this respect, we employed electrophysiology approaches to investigate the response of lysenin channels to variable voltage stimuli in the presence of small concentrations of Cu2+ ions. Our experimental results showed that the hysteretic behavior, recorded in response to variable voltage ramps, is accentuated in the presence of Cu2+ ions. Using simultaneous AC/DC stimulation, we were able to determine that Cu2+ prevents the reopening of channels previously closed by depolarizing potentials and the channels remain in the closed state even in the absence of a transmembrane voltage. In addition, we showed that Cu2+ addition reinstates the voltage gating and hysteretic behavior of lysenin channels reconstituted in neutral lipid membranes in which lysenin channels lose their voltage-regulating properties. In the presence of Cu2+ ions, lysenin not only regained the voltage gating but also behaved like a long-term molecular memory controlled by electrical potentials.

Local Control Model of a Human Ventricular Myocyte: An Exploration of Frequency-Dependent Changes and Calcium Sparks.

Calcium (Ca2+) sparks are the elementary events of excitation-contraction coupling, yet they are not explicitly represented in human ventricular myocyte models. A stochastic ventricular cardiomyocyte human model that adapts to intracellular Ca2+ ([Ca2+]i) dynamics, spark regulation, and frequency-dependent changes in the form of locally controlled Ca2+ release was developed. The 20,000 CRUs in this model are composed of 9 individual LCCs and 49 RyRs that function as couplons. The simulated action potential duration at 1 Hz steady-state pacing is ~0.280 s similar to human ventricular cell recordings. Rate-dependence experiments reveal that APD shortening mechanisms are largely contributed by the L-type calcium channel inactivation, RyR open fraction, and [Ca2+]myo concentrations. The dynamic slow-rapid-slow pacing protocol shows that RyR open probability during high pacing frequency (2.5 Hz) switches to an adapted "nonconducting" form of Ca2+-dependent transition state. The predicted force was also observed to be increased in high pacing, but the SR Ca2+ fractional release was lower due to the smaller difference between diastolic and systolic [Ca2+]SR. Restitution analysis through the S1S2 protocol and increased LCC Ca2+-dependent activation rate show that the duration of LCC opening helps modulate its effects on the APD restitution at different diastolic intervals. Ultimately, a longer duration of calcium sparks was observed in relation to the SR Ca2+ loading at high pacing rates. Overall, this study demonstrates the spontaneous Ca2+ release events and ion channel responses throughout various stimuli.

The effects of relative position of anomalous body and topography on the semi-airborne transient electromagnetic survey

The semi-airborne transient electromagnetic method using short-offset has the characteristics of high signal-to-noise ratio and large detection depth, which can adapt to environmental monitoring, groundwater mapping and metal survey in complex terrain. The terrain effects will modulate semi-airborne transient electromagnetic method response. Most terrains effects studies assume that the anomalous body is located below the centre of topography or under the slope, however, in the direction of electromagnetic propagation, several scenarios exist regarding the position of an anomalous body relative to a topography, and different situations will have different anomalous characteristics. In this study, the time-domain finite-element modelling using unstructured mesh is used to investigate the distribution of the electromagnetic response of different time channels of different anomalous bodies in different positions relative to a topography under two conditions: mountains and valleys. The results show that (1) The influence of the relative position of the anomalous body and the topography is similar in the mountain and valley. When the anomalous body is located on the side of the topography far from the transmitter, it is more affected by the topography than when the anomalous body is located on the side of the topography near the transmitter, and (2) in the early stage, the distribution of the electromagnetic response is primarily determined by the topography, in the middle stage, the coupling between anomalous bodies at different locations and the topography results in an extremely complex response distribution, in the late stage, the response distribution primarily originates from the eddy current of the anomalous body, and the effect of the topography is moderate. When using semi-airborne transient electromagnetic method for environmental monitoring and shallow resources survey in complex terrain, data processing and interpretation should be performed based on the position of the anomalous body relative to the topography.

Assessing channel geometry in response to land use disturbance in a low‐relief, glacially conditioned setting

AbstractBankfull stage, the highest flow elevation contained by a river channel before overbank flooding occurs, is the presumed threshold for channel morphological change. At bankfull, the channel boundary experiences the highest shear stress, producing somewhat predictable hydraulic relationships and a cross‐sectional form. However, land use and glacial conditioning can profoundly impact a channel's geomorphic responses. Two common methods characterize bankfull flow: one based on flow frequency and other based on channel form. This study considers a simplified approach to identify upstream land use relative to estimates of bankfull flow versus a channel's geometric form. The approach compares archived geomorphic surveys of 140 river reaches in southern Ontario, Canada, to 2‐year flood quantiles modelled from historical flood data of 207 gauge stations. Flood frequency analysis determines that annual maximum series (AMS) datasets, fitted to optimized probabilistic distributions, underestimate discharge for low‐magnitude, high‐frequency flood events compared to partial duration series (PDS) datasets. For smaller drainage areas (<100 km2) associated with an extensive agricultural activity and/or urbanization, the estimates of bankfull discharge (Qbf) generated by cross‐sectional channel geometry are greater than the gauge‐derived Q2 values. Channels impacted by high levels of upstream land use disturbance show statistically significant lower width‐to‐depth ratios (p < 0.001) and a trend towards a finer D50 bed material, suggesting enhanced surface runoff delivering more mobile finer sediments and channel confinement or incision. This research quantifies the formative bankfull stage to better understand the link between land use and a channel's ‘natural’ hydrogeomorphic response in a low‐relief, glacially conditioned setting.

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Simultaneous Detection of Staphylococcus aureus and E. coli O157:H7 Using a Self-Calibrated Potentiometric Sensors Array Based on Peptide Recognition

A novel self-calibrated potentiometric sensors array for simultaneous detection of staphylococcus aureus (SA) and E. coli O157:H7 (E. coli) based on peptide recognition have been proposed on a 4-channel screen printed carbon electrode (SPCE) modified with DenAu and PTAA. Two specific peptides for recognition of SA and E. coli were assembled on two working channels, respectively. For two calibration channels, two non-specific peptides with the same isoelectric point and length as the specific ones were coated on the surface to eliminate the influence of the background matrix on the potentiometric responses of working channels, and improve the detection accuracy. Under the optimal conditions, the sensors array exhibited high sensitivity to SA and E. coli in the range of 1.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CFU/mL - 1.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> CFU/mL and 1.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> CFU/mL - 1.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> CFU/mL with the detection limits of 38 CFU/mL and 241 CFU/mL, respectively, and also good specificity and repeatability. Furthermore, it was successfully applied to the detection of milk samples, providing an effective strategy the on-site simultaneous detection of multiple targets.

A Simultaneous Wireless Power and High-Rate Data Transfer System Based on Transient Responses Regulation

In this letter, a simultaneous wireless power and data transfer (SWPDT) method for wireless power transfer systems is proposed. Data and power are transferred via the same coupler. To achieve a high transfer power and a high data transfer rate, double-side LCC compensation topology with band-pass filtering is adopted to create a favorable condition for data transfer. An inductor fully compensated at the power carrier frequency and connected serially with the coupled coil is used to inject and extract the data carrier and multiplexed to conduct the power carrier. In addition, the duration time of the transient responses of the data transfer channel is regulated based on complex frequency-domain analysis. Finally, a 1.1 kW SWPDT experiment prototype with a data transfer rate of 1 Mbps and a data transfer delay of 0.4 μs is built to verify the feasibility of the proposed method.

Feed-Forward Frequency Domain Equalizer for Fast SOP Transient Tracking in Coherent Optical Modems

We propose a feed-forward frequency domain adaptive linear equalizer that can track fast state-of-polarization SOP transients in the fiber. In the proposed method, we rely on the pilot symbols that are inserted into the DSP frame to estimate the channel response and then compensate for it. Unlike the decision-directed least mean squares DD-LMS algorithm, the proposed circuit is designed in a feed-forward fashion and avoids the latencies involved in DD-LMS feedback loop in order to achieve the best possible SOP tracking capabilities. The proposed algorithm is shown to be robust against high order differential group delay DGD and polarization dependent loss PDL and offers a significant tracking improvement compared to DD-LMS.

Fingerprint-Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV fingerprint comprises the channel characteristics of the cascaded channel, such as the angle of arrival (AOA) at the RIS and the time delay from the MU to the RIS, which are indicative of the position of the MU. To process the STCRV with more features, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU with higher accuracy. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).