Incoming Signal Research Articles

Parameter Estimation and Mutual Coupling Self-Calibration with Orthogonal Polarization Sensitive Array

In order to address the mutual coupling impact with orthogonal polarization sensitive array, the mutual coupling self-calibration strategy is suggested in this study. Firstly, the mutual coupling properties are used to modify the steering vector of the array. By transforming the matrices, the properties of the polarization and direction-of-arrival (DOA) are isolated from the mutual coupling coefficients. Then, using the rank deficit matrix approach, the estimated the polarization properties and DOA values of the incoming signal are ascertained. The eigendecomposition of the transition matrix utilizing the modulus restrictions is then used to get the mutual coupling coefficient. The technique may be used to produce high-precision results of the mutual coupling coefficient when there is a substantial gap in the power of the signal sources. The outcomes of the simulation demonstrate the viability of the advanced method used in this work.

Multioctave Interference Detectors With Sub-Microsecond Response

High-power interferers are one of the main hurdles in wideband communication channels. To that end, this article presents a wideband interferer detection method. The presented technique operates by sampling the incoming signal as an input, and produces the frequency and the power readings of the detected interferer. The detection method relies on driving an open circuit stub, where the voltage is proportional to the power of the interferer, and the standing wave pattern is an indicator of its frequency. This approach is feasible over multioctave bandwidth with a wide power dynamic range. The concept is analyzed for design and optimization, and a prototype is built for a proof-of-concept. The measured results demonstrate the ability to detect an interferer within the 1–16 GHz frequency range, with a power dynamic range between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 20 and 20 dBm. The detection concept is also fit with different types of tunable bandstop filters (BSFs) for automatic detection and suppression of the interferer if its power exceeds a programmable threshold. With a measured response time of 500 ns, the presented method is a technology enabler for wideband receivers.

Beam Alignment for Analog Arrays Based on Gaussian Approximation

Beam alignment is a process for receive and transmit antenna arrays to find the correct beamforming directions. It is typically based on beam scanning and peak-energy searching, which lead to time-consuming beam training process in communication protocols, such as 802.11ad for vehicular networks. In this correspondence, we propose a fast beam alignment method for analog arrays, based on directly estimating the angle-of-arrival (AoA) of the incoming signals. We propose simple and highly efficient AoA estimators, by approximating the power of the array response as a Gaussian function. One estimator is based on the power ratio and can coherently combine multiple measurements scanned at arbitrary intervals and with different beam widths. The other two are based on an innovative idea of Gaussian curve fitting with weighted least square techniques, and one of them can even work without knowing the beam width. Simulation results validate the effectiveness of the proposed scheme.

Hybrid Reflection Modulation

Reconfigurable intelligent surface (RIS)-empowered communication has emerged as a novel concept for customizing future wireless environments in a cost- and energy-efficient way. However, due to double path loss, existing fully passive RIS systems that purely reflect the incident signals into preferred directions attain an unsatisfactory performance improvement over the traditional wireless networks in certain conditions. To overcome this bottleneck, we propose a novel transmission scheme, named hybrid reflection modulation (HRM), exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains. In the HRM scheme, the active reflecting elements using additional power amplifiers are able to amplify and reflect the incoming signal, while the remaining passive elements can simply reflect the signals with appropriate phase shifts. Based on this novel transmission model, we obtain an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach. Moreover, comprehensive computer simulations are performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems.

Learning cortical hierarchies with temporal Hebbian updates.

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning.

A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works.

Four-Channels High-Resolution Frequency Counter for QCM Sensor Array Using Generic FPGA XC6SLX9 Board

A frequency counter is essential for resonance-based sensors like quartz crystal microbalance. An electronic nose or tongue using a QCM sensor array requires a multichannel frequency counter to detect the frequency shift of the sensors simultaneously. The frequency counter’s resolution, precision, and sampling speed are important factors. Board size, energy consumption, and rapid deployment are also considered in the design. This work shows the development of an independent multichannel frequency counter using a commercial Xilinx Spartan 6 series XC6SLX9 board module and a microcontroller board. Both modules are general-purpose modules; therefore, there is no need for a printed circuit board design, resulting in a quick implementation: the use of FPGA results in a compact size and low energy consumption. The developed counter is designed based on a reciprocal counter utilizing the internal logic block of the FPGA. The FPGA module has a built-in 50 MHz TCXO clock and is the reference clock. The high-resolution timing of the counter is realized by multiplying the 50 MHz clock by 6 to reach 300 MHz. The multiplication utilizes the PLL modules in the FPGA. The high precision and accuracy of the counter are achieved by calibrating the timing clock to a 10 MHz rubidium oscillator. The data communication to the microcontroller is done via the SPI by implementing the SPI protocol in the FPGA. The resource is optimized by utilizing PLL and DSP blocks for the counter. Only 5% registers and 5% LUTs of the FPGA resource are used to build a four-channel frequency counter. The result shows that the counter can measure the frequency of incoming signals with a resolution of 0.033 Hz at 10 MHz with a sampling time of 1 second. The system has been tested to monitor the frequency changes of a QCM sensor array.

Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar

Time delay estimation (TDE) is of great interest for the thickness estimation of pavement using ground penetrating radar (a non-destructive testing tool that uses electromagnetic waves to probe civil engineering material), which determines the difference between the times of arrival of two incoming signals or backscattered echoes. However, conventional TDE methods suffer performance degradation because of limited resolution for thin layers and highly correlated backscattered echoes. In this paper, a deep neural network (DNN)-based TDE method is proposed. Firstly, a new DNN is constructed to classify and train the backscattered echoes; then, the time delays of the backscattered echoes can be estimated through the proposed DNN. The proposed method is based on the data processing of the backscattered echoes, which is more robust to the noise than conventional subspace-based methods (MUSIC, ESPRIT) and compressive sensing-based methods (OMP). The proposed method can directly process coherent backscattered echoes without decorrelation procedures, compared with MUSIC and ESPRIT. In addition, the proposed method is more powerful in resolving the close backscattered echoes than that of OMP. Simulation results show the efficiency of the proposed method in terms of signal-to-noise ratio (SNR) and BΔτ products. (The BΔτ products indicate the resolution of GPR, B is the frequency bandwidth of GPR and Δτ is the time delay between two incoming signals or backscattered echoes).

Backward masking reveals coarse-to-fine dynamics in human V1

Natural images exhibit luminance variations aligned across a broad spectrum of spatial frequencies (SFs). It has been proposed that, at early stages of processing, the coarse signals carried by the low SF (LSF) of the visual input are sent rapidly from primary visual cortex (V1) to ventral, dorsal and frontal regions to form a coarse representation of the input, which is later sent back to V1 to guide the processing of fine-grained high SFs (i.e., HSF). We used functional resonance imaging (fMRI) to investigate the role of human V1 in the coarse-to-fine integration of visual input. We disrupted the processing of the coarse and fine content of full-spectrum human face stimuli via backward masking of selective SF ranges (LSFs: <1.75cpd and HSFs: >1.75cpd) at specific times (50, 83, 100 or 150 ms). In line with coarse-to-fine proposals, we found that (1) the selective masking of stimulus LSF disrupted V1 activity in the earliest time window, and progressively decreased in influence, while (2) an opposite trend was observed for the masking of stimulus’ HSF. This pattern of activity was found in V1, as well as in ventral (i.e. the Fusiform Face area, FFA), dorsal and orbitofrontal regions. We additionally presented subjects with contrast negated stimuli. While contrast negation significantly reduced response amplitudes in the FFA, as well as coupling between FFA and V1, coarse-to-fine dynamics were not affected by this manipulation. The fact that V1 response dynamics to strictly identical stimulus sets differed depending on the masked scale adds to growing evidence that V1 role goes beyond the early and quasi-passive transmission of visual information to the rest of the brain. It instead indicates that V1 may yield a ‘spatially registered common forum’ or ‘blackboard’ that integrates top-down inferences with incoming visual signals through its recurrent interaction with high-level regions located in the inferotemporal, dorsal and frontal regions.

A Battery-Less Non-Hybrid Six-Port RFID-Based Wireless Sensor Architecture for IoT Applications

This paper introduces a novel battery-less wireless sensor architecture, which is based on the principle of direct frequency conversion. The proposed architecture uses a non-hybrid six-port structure to integrate a UHF RFID chip with sensing elements to create a sensor node. The RFID chip provides a unique identification to the sensor node and the sensing element enables the reading of environmental conditions. The non-hybrid six-port structure unequally divides an incoming RFID interrogator signal into an in-phase and quadrature branch. Using the novel unequal distribution in a six-port allows higher power being directed to the RFID chip to ensure a longer read range. The I and Q signals reflected by the RFID chip and the sensing element, respectively, are mixed without the use of a lossy or an active mixer. The mixed signal’s amplitude and phase are directly dependent on the values of the attached sensing element. To read the value of the sensing element wirelessly at a reader, an IQ demodulator is used, which determines the phase of the backscattered signal. As a result, various sensed parameters such as light intensity, voltage, or force may be read wirelessly without requiring a battery at the node. To demonstrate the performance, a pin diode is used as a sensing element to read voltages wirelessly at a distance of up to 2.45 m.

Topology optimization of microwave frequency dividing multiplexers

We use material-distribution-based topology optimization to design a three-port frequency dividing multiplexer at microwave frequencies. That is, by placing a good electric conductor inside the design domain, we aim to design a passive device that splits the incoming signal’s frequencies into two frequency bands and transmits them to their respective output ports. The Helmholtz equation models the time-harmonic wave propagation problem. We solve the governing equation using the finite element method. The adjoint variable method provides the required gradients, and we solve the topology optimization problem using Svanberg’s MMA algorithm. In this study, we present a technique for modeling the distribution of a good electric conductor within the design domain. In addition, we derive a power balance expression, which aids in formulating a series of three objective functions. In each successive objective function, we add more information and evaluate its impact on the results. The results show that by selecting a suitable objective function, we achieve more than 93.7,% transmission for both the frequency bands. Moreover, the numerical experiments suggest that the optimization problem is self penalized and is sensitive to the initial design.

Penggunaan Virtual lab. Circuit Wizard Untuk Simulasi Rangkaian High Pass Filter Pasif

Passive High Pass Filter will be discussed in this paper; the filter itself is a circuit that determines the amount of a certain frequency to be streamed and will reject frequencies that are not suitable. Meanwhile, a passive filter is an electrical circuit that can block intervals at certain signal frequencies to filter incoming signals. The benefit of passive filters is that they can be used at higher frequencies because their dimensions are larger than active filters. Measurement of the voltage gain in the Passive High Pass Filter circuit using laboratory methods using a simulation approach with the Circuit Wizard simulator or directly. The research method consists of two experimental scenarios that aim to compare the signals generated from stage I to stage V. For the scenario I, the simulation is carried out using the Circuit Wizard simulator. The capacitor is one of the reactive components that will offer a variety of resistance to different frequency signals based on a High Pass RC Filter circuit with a capacitor that has a high value of resistance to low frequencies or resistance to high-frequency transmissions will be minimal. The capacitor feature makes it resistant to low-frequency or DC signals, so only high-frequency signals can travel through the capacitor and block the path of low-frequency impulses. The high pass filter can attenuate or maintain low frequencies below the cut-off frequency while passing high signal frequencies or passing signals above them.

All‐Optical Parametric‐Assisted Oversampling and Decimation for Signal Denoising Amplification

AbstractDecimation is a common process in digital signal processing that involves reducing the sampling rate of an oversampled signal by linearly combining consecutive samples. Among other applications, this process represents a simple means to mitigate noise content in the digital signal. In this work, a novel optical signal processing concept inspired by these operations is proposed, which is called Parametric‐assisted Oversampling and Decimation (POD). By using a simple all‐fiber setup, the POD processor first realizes an ultra‐fast parametric oversampling of the incoming temporal signal (at &gt;100 Gigasamples per second), a process that is followed by a decimation that reduces the sampling rate by any user‐defined factor in a lossless manner. In this way, the POD delivers an amplified sampled copy of the optical signal, where the peak‐to‐peak gain results from the combination of parametric amplification and a “passive” amplification equal to the decimation factor. In this report, joint parametric and passive amplification by a factor ≈50 on GHz‐bandwidth signals is demonstrated. Furthermore, it is shown that the decimation process can effectively mitigate effects of narrowband noise, outperforming traditional optical and digital filtering techniques. By experimentally achieving ultra‐high decimation factors (&gt;750), narrowband (MHz‐bandwidth) optical waveformsthat are lost in a much stronger noise background are recovered.

Exploring the limits to our understanding of whether fish feel pain.

Exploring the limits to our understanding of whether fish feel pain.

Polychrony as Chinampas

In this paper, we study the flow of signals through linear paths with the nonlinear condition that a node emits a signal when it receives external stimuli or when two incoming signals from other nodes arrive coincidentally with a combined amplitude above a fixed threshold. Sets of such nodes form a polychrony group and can sometimes lead to cascades. In the context of this work, cascades are polychrony groups in which the number of nodes activated as a consequence of other nodes is greater than the number of externally activated nodes. The difference between these two numbers is the so-called profit. Given the initial conditions, we predict the conditions for a vertex to activate at a prescribed time and provide an algorithm to efficiently reconstruct a cascade. We develop a dictionary between polychrony groups and graph theory. We call the graph corresponding to a cascade a chinampa. This link leads to a topological classification of chinampas. We enumerate the chinampas of profits zero and one and the description of a family of chinampas isomorphic to a family of partially ordered sets, which implies that the enumeration problem of this family is equivalent to computing the Stanley-order polynomials of those partially ordered sets.

Single line noise cancellation using derivative of normalized least mean square algorithm

Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original error data normalized step size (EDNSS) algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.

An 8b9b 77.44-Gb/s Noise-Immune Spatial-Delta Coded Transceiver for Short-Reach Memory Interfaces in 28-nm CMOS

A noise-immune spatial-delta coding (SDC) scheme is proposed for single-ended multi-lane links. The SDC technique allows differential sensing of single-ended PAM2 signals for high signal integrity while still achieving the same pin efficiency as the 8b9b data bus (DBI) scheme, providing high compatibility with conventional DDR interfaces. The SDC encoding/decoding functions are implemented with simple XOR gates to minimize power and latency overhead. Continuous-time linear equalizers in the receiver compensate for distortions in incoming pseudodifferential signals with high tolerance on dynamic common-mode switching associated with the SDC operation. An 8b9b SDC transceiver prototype array composed of nine data channels and a differential clock channel has been fabricated in 28-nm CMOS technology for demonstration. The transceiver array achieves a 77.44 Gb/s aggregate data rate with 9 wires at 9.68 Gbaud/s. The bit error rate is measured to be less than 1e-12 while consuming 4.9 pJ/b from a 1.25-V supply.

Proactive Eavesdropping Strategies Using Hybrid Reflecting-Backscatter Intelligent Surface

This correspondence proposes to employ a hybrid reflecting-backscatter intelligent surface for proactive eavesdropping in a wireless information surveillance system. To be specific, some elements of intelligent reflecting surface (IRS) are used to reflect the incoming signal from the source, while the remaining recast the signal to artificial noise through backscatter techniques. To maximize the effective proactive eavesdropping rate, which is defined as the suspicious communication rate that satisfies the condition of successful eavesdropping, three strategies are presented, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Enhancing-Strategy</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jamming-Strategy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hybrid-Strategy</i> . For the first two, a semidefinite relaxation based algorithm is adopted to design the passive beamforming at IRS. For the last strategy, closed-form expressions of optimal solutions are derived. In addition to the passive beamforming design, the deployment of IRS is optimized by a simulated annealing algorithm. Finally, simulations verify the effectiveness of the proposed schemes.

Performance analysis of a dual-hop parallel relayed mixed FSO-UWOC system

Abstract In this paper, we propose a dual-hop parallel relayed mixed free space optical-underwater optical communication (FSO-UWOC) system model to setup a high-speed communication link connecting a distant sender located at the lighthouse tower and the receiver inside the deep ocean to transmit and receive the data for the applications such as deep sea exploration, marine life study etc. The proposed model is the setup of multiple parallel cooperative dual hop communication link for the betterment of the overall system performance under the worst case scenarios. The receiver examines the end-to-end signal to noise ratio and a relay is selected which has the maximum SNR. The selected relay then decodes the incoming optical signal through FSO link into its corresponding visible light signal and further transmits it to the underwater exploration devices such as underwater drone via VLC link. The FSO link is modelled by the Gamma-Gamma fading distribution and the VLC link is modelled by Exponential Generalized Gamma distribution. The end-to-end SNR of the system is utilized to derived the outage probability of the FSO-UWOC system and the impact of the individual link parameters is studied and presented for the proposed system model.

Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems

To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (&lt;0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.