Signal Processing Scheme Research Articles

Passive Radar DPCA Schemes With Adaptive Channel Calibration

This article addresses the problem of direct signal interference (DSI) and clutter cancellation for passive radar systems on moving platforms employing displaced phase centre antenna (DPCA) approach. Attention is focused on the development of signal processing strategies able to compensate for the limitations deriving from amplitude and phase imbalances that affect the two channels employed on receive. First, we show that using the signal received from the illuminator of opportunity as a source for channels calibration might be ineffective when DSI and clutter echoes have different directions of arrival, due to the effect of angle-dependent channel imbalance. Then, a two-stage strategy is proposed, consisting of a preliminary DSI removal stage at each receive channel, followed by a clutter-based calibration approach that basically enables an effective DPCA clutter suppression. Different strategies for channel calibration are proposed, aimed at compensating for potential angle and range-dependent channel errors, based on the maximization of the cancellation performance. Effectiveness of this scheme is shown against the experimental data from a DVB-T based moving passive radar, in the presence of both real and synthetic moving targets.

Editorial for Special Issue “Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms”

Microwave radar imaging plays a key role in several civilian and defense applications, such as security, surveillance, diagnostics and monitoring in civil engineering and cultural heritage, environment observation, with particular emphasis on disasters and crisis management, where it is required to remotely sense the area of interest in a timely, safe and effective way. To address these constraints, a technological opportunity is offered by radar systems mounted onboard smart and flexible platforms, such as ground-based ones, airplanes, helicopters, drones, unmanned aerial and ground vehicles (UAV and UGV). For this reason, radar imaging based on data collected by such platforms is gaining interest in the remote sensing community. However, a full exploitation of smart and flexible radar systems requires the development and use of image formation techniques and reconstruction approaches able to exploit and properly deal with non-conventional data acquisition configurations. The other main issue is related to the need to operate in challenging environments, and still deliver high target detection, localization and tracking. These environments include through the wall imaging, rugged terrain and rough surface/subsurface. In these cases, one seeks mitigation of the adverse effects of clutter and multipath via the implementation of effective signal processing strategies and electromagnetic modeling.

Sensing skins for robots: product developments and recent research

Purpose This paper aims to provide details of the use of sensing skins by robots through reference to commercial products and recent research. Design/methodology/approach Following an introduction, this paper first summarises the commercial status of robotic sensing skins. It then provides examples of recent safety skin research and is followed by a discussion of processing schemes applied to multiple sensor skin systems including humanoid robots. Examples of research into soft, flexible skins follow and the paper concludes with a short discussion. Findings The commercialisation of sensing skins has been driven by safety applications in the emerging cobot sector, and a market is emerging for skins that can be retrofitted to conventional robots. Sensing skin research is widespread and covers a multitude of sensing principles, technologies, materials and signal processing schemes. This will yield skins which could impart advanced sensory capabilities to robots and potential future uses include agile manipulation, search and rescue, personal care and advanced robotic prosthetics. Originality/value This paper provides details of the current role of sensing skins in robots and an insight into recent research.

Shaping nonlinear optical response using nonlocal forward Brillouin interactions

In most practical scenarios, optical susceptibilities can be treated as a local property of a medium. For example, in the context of nonlinear optics we can typically treat the Kerr and Raman response as local, such that optical fields at one location do not produce a nonlinear response at distinct locations in space. This is because the electronic and vibrational disturbances produced within the material are confined to a region that is smaller than an optical wavelength. By comparison, Brillouin interactions, mediated by traveling-wave acoustic phonons, can result in a highly nonlocal nonlinear response as the elastic waves generated in the process can occupy a region in space much larger than an optical wavelength. The unique properties of these interactions can be exploited to engineer new types of processes, where highly delocalized phonon modes serve as an engineerable channel that mediates scattering processes between light waves propagating in distinct optical waveguides. These types of nonlocal optomechanical responses have recently been demonstrated as the basis for information transduction, however the nontrivial dynamics of such systems has yet to be explored. In this work, we show that the third-order nonlinear process resulting from spatially extended Brillouin-active phonon modes involves mixing products from spatially separated, optically decoupled waveguides, yielding a nonlocal susceptibility. Building on these concepts, we illustrate how nontrivial multi-mode acoustic interference can produce a nonlocal susceptibility with a multi-pole frequency response, as the basis for new optical and microwave signal processing schemes within traveling wave systems.

Efficient lossless compression scheme for multi-channel ECG signal processing

Electrocardiogram (ECG) represents the recording of the heart’s electrical activity and is used to diagnose heart disease nowadays. The diagnosis requires huge time consumption for acquiring enough multi-channel data. The storage and transmission of 12 lead ECG data results in massive cost. In this work, we propose a multi-channel ECG lossless compression which uses the adaptive linear prediction for intra and inter channel decorrelation. We also use the adaptive Golomb-Rice codec for entropy coding. The proposed technique for adaptive linear prediction and Golomb-Rice codec is based on the performance of passed samples. Thus, the coefficient of linear prediction and Golomb-Rice codec will make self-adjustments during the process. We evaluate the proposed algorithm with MIT-BIH Arrhythmia database for single-channel compression, and Physikalisch-Technische Bundesanstalt database (PTB) for multi-channel compression. The overall compression scheme is also implemented in embedded system with an ARM Cortex-M4 processor for real-time demonstration.

Design of varying frequency three-phase synchronization signal processing system for HL-2M tokamak phase-controlled power supply

• Synchronization signal is very essential for obtaining high precision control performance of thyristor power supply in tokamak. • The pulse generator with rectifier load make the output voltage waveform severe distortion and marked frequency variation in a wide range. • A real-time synchronization signal processing scheme is presented. • Based on Programmable Automation Controller (PAC) architecture the system is done and applied in HL-2A experiments. In HL-2 M tokamak, three-phase thyristor power supplies are utilized to output a direct current to energize the device coil to control the plasma current and shape configuration. Phase-controlled is currently the most effective control method in thyristor power supply. The quality of the synchronization signal is directly related to the performance of the thyristor power supply control. HL-2 M device uses a pulse generator with rectifier load to supply power to the device coil, it makes the thyristor power supply AC input voltage not only have a wide frequency variation but also a severe waveform distortion, so it is difficult to obtain the accurate synchronization signal of the thyristor power supply. In this paper, a real-time tracking and processing method for variable frequency synchronization signals are proposed, which applied the digital filter with point-to-point to process varying frequency synchronization signal, calculated the real-time frequency of the filtered data, obtained the delay angles at different input frequencies according to the phase-frequency characteristics of Butterworth low-pass filter, and reconstructed the synchronization signal by phase compensation. The system has been built to achieve a wide range of real-time frequency tracking from 70 to 110 Hz, and provides multiple synchronization signal output with 15° interval. The performance of the system was verified in the HL-2A experiment.

Direct 3-D Sparse Imaging Using Non-Uniform Samples Without Data Interpolation

As an emerging technique, sparse imaging from three-dimensional (3-D) and non-uniform samples provides an attractive approach to obtain high resolution 3-D images along with great convenience in data acquisition, especially in the case of targets consisting of strong isolated scatterers. Although data interpolation in k-space and fast Fourier transform have been employed in the existing 3-D sparse imaging methods to reduce the computational complexity, the data-gridding errors induced by local interpolation may usually result in poor imaging performance. In this paper, we directly regard the imaging problem as a joint sparse reconstruction problem from non-uniform data without interpolation in 3-D space. Combining dictionary reduction and Gauss iterative method with the optimized signal processing scheme, a sparse imaging algorithm is proposed to address the difficulty of large-scale computation involved in direct 3-D sparse reconstruction. Benefited from the optimized signal processing scheme and the avoidance of data interpolation, the direct 3-D sparse imaging (DTDSI) method proposed in this paper is of low computation scale and high imaging performance. Experiments of electromagnetic simulation data demonstrate the DTDSI method outperforms baseline methods in terms of resolving ability, lower side-lobes and higher accuracy.

Presenting Meso-Cenozoic seismic sequential stratigraphy of the Offshore Indus Basin Pakistan

Preserved deformed depositional sequences along passive continental margins are important repositories of natural resources as well as key laboratories for understanding rift-drift tectonics. With better post signal processing schemes, interpretation techniques, and potential data, imaging sedimentary basins along these margins has reached unique levels of refinement. This, in turn, has led to considerable improvement in unfolding the complex tectono-sedimentary history since rifting to basin development. In this paper, deep reflection seismic coupled with well data and biostratigraphic information have been used to decipher the third-order depositional sequences of Meso-Cenozoic succession in western parts of the Indian Plate Passive Continental Margin (Offshore Indus Basin) as pioneer research. Based on morpho-tectonic considerations, the depositional sequences are divided into syn-rift, passive margin, and post-rift stages deformed by steeply dipping normal faults. Using the principles of classic sequence stratigraphy, five third-order mappable depositional sequences have been recognized on seismic records that are controlled by regional tectonics and sea-level fluctuations coupled with sedimentation type and rates in the region. The associated system tracts were recognized and mapped based on genetic reflection terminations on seismic profiles and log motifs, and constraint surfaces include sequence boundaries (SBs), maximum flooding surfaces (MFSs) and transgressive surfaces (TSs). The depositional systems encompass lowstand system tracts (LSTs), transgressive system tracts (TSTs), and highstand system tracts (HSTs). The LSTs are composed of shallowing-upward lowstand deltas or terraces prograding in the basin-ward direction while pinches out landward at preceding depositional shoreline breaks. The sand units of TSTs are interpreted as shoreface sands deposited in the shelfal part during rising sea-levels and HSTs are characterized by coarsening and shallowing upward intervals, with both fluvial and laterally prograding deltaic deposits. A hydrocarbon entrapment model is proposed suggesting an interesting petroleum trap. Based on the entire course of this research, valuable recommendations have been made for answering past exploration failures and future efforts. Such studies can be applied to worldwide passive margins dominated by third-order sequences with complex sediments-tectonic interplay.

Enhanced FSR Asymmetrical Triple Optical Ring Resonator

The paper proposes an Asymmetrical Triple Optical Ring Resonator (ATORR) and investigates its performances. Delay line signal processing scheme along with Mason’s Gain Formula is implemented to develop the transfer function of this configuration. Two different materials are used for this model, i.e., SOI (2.0167) and SIN (2.811), and their frequency responses are analyzed and compared to obtain different Free Spectral Range (FSR) outputs on MATLAB. Along with widening of FSR, crosstalks and resonance loss are also reduced. The extended FSR output is in THz range that has wide application in the field of photonics.

Microwave Filtering Using Forward Brillouin Scattering in Photonic-Phononic Emit-Receive Devices

Microwave photonic systems are compelling for their ability to process signals at high frequencies and over extremely wide bandwidths as a basis for next generation communication and radar technologies. However, many applications also require narrow-band $(\sim\text{MHz})$ filtering operations that are challenging to implement using optical filtering techniques, as this requires reliable integration of ultra-high quality factor $(\sim 10^8)$ optical resonators. One way to address this challenge is to utilize long-lived acoustic resonances, taking advantage of their narrow-band frequency response to filter microwave signals. In this paper, we examine new strategies to harness a narrow-band acoustic response within a microwave-photonic signal processing platform through use of light-sound coupling. Our signal processing scheme is based on a recently demonstrated photon-phonon emitter-receiver device, which transfers information between the optical and acoustic domains using Brillouin interactions, and produces narrow-band filtering of a microwave signal. To understand the best way to use this device technology, we study the properties of a microwave-photonic link using this filtering scheme. We theoretically analyze the noise characteristics of this microwave-photonic link, and explore the parameter space for the design and optimization of such systems.

Sensing for aerospace combustor health monitoring

Purpose This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing. Design/methodology/approach Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy. Findings Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service. Practical implications The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided. Originality/value The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making.

Audiologists’ preferences in programming cochlear implants: A preliminary report

Background: There is little data available addressing how clinical audiologists handle cochlear implant (CI) programming between device manufacturers and make decisions on related services, particularly in the United States. Objectives: This study sought to understand the techniques and settings professionals use with their patients, how they approach bimodal fitting, which tests they use to evaluate patient and device performance, and their overall preferences of (re)habilitative options. Methods: A questionnaire was developed and distributed to CI audiologists throughout the United States electronically. Results: All respondents reported either always or almost always using Cochlear’s default signal processing strategy in contrast to the 64% reported for Med-El and 40% for Advanced Bionics. A trend of less use of electrically evoked stapedial reflex threshold (eSRT) than electrically evoked compound action potentials (eCAP) for objective measures was revealed. Higher likelihood of performing speech recognition tests in quiet than in noise was revealed. Preferences for bimodal fitting trend toward using a partner company’s hearing aid, although preferences were comparable in adopting four types of hearing aid formulas surveyed in the questionnaire. Conclusions: These data confirm high variability among audiologists’ CI programming practices, and documenting these differences is an important step to understanding how to best treat patients.

A New Method for Specific Emitter Identification With Results on Real Radar Measurements

Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mod signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.

Benefits of Beamforming With Local Spatial-Cue Preservation for Speech Localization and Segregation.

A study was conducted to examine the benefits afforded by a signal-processing strategy that imposes the binaural cues present in a natural signal, calculated locally in time and frequency, on the output of a beamforming microphone array. Such a strategy has the potential to combine the signal-to-noise ratio advantage of beamforming with the perceptual benefit of spatialization to enhance performance in multitalker mixtures. Participants with normal hearing and with hearing loss were tested on both speech localization and speech-on-speech masking tasks. Performance for the spatialized beamformer was compared with that for three other conditions: a reference condition with no processing, a beamformer with no spatialization, and a hybrid beamformer that operates only in the high frequencies to preserve natural binaural cues in the low frequencies. Beamforming with full-bandwidth spatialization supported speech localization and produced better speech reception thresholds than the other conditions.

Intelligent Offloading Strategy Design for Relaying Mobile Edge Computing Networks

To support the application of IoT and smart city, high data-rate wireless transmission is required. To meet the demand of high data-rate, the techniques of multiple antennas and mobile edge computing (MEC) networks have been proposed in order to enhance the data transmission rate significantly. However, there still exist lots of challenges array signal processing assisted MEC networks. In this paper, we propose an intelligent framework of offloading strategy for MEC networks assisted by array signal processing, where one user with multiple antennas has some computational tasks. These tasks can be computed by the user itself which however has limited computational capability, or computed by the near-by computational access points (CAPs) which has a powerful computational capability at the cost of wireless transmission. We consider the system cost by jointly taking into account the computational price, the energy consumption and the latency. By minimizing the system cost, we propose an intelligent offloading strategy based on ant colony optimization (ACO) algorithm, where the ants randomly visit the CAPs in order to obtain the final results. To further enhance the MEC network performance, the array signal processing is utilized at the user, where either the maximum ratio transmission (MRT) or selection combining (SC) is used to assist the data transmission from the user to CAPs. Simulation results with MRT and SC are finally demonstrated to verify the effectiveness of the proposed ACO-based offloading strategy and array signal processing schemes.

Intelligent Mobile Edge Computing With Pricing in Internet of Things

In this paper, we investigate mobile edge computing (MEC) networks for intelligent information services, where there are $N$ users equipped with $K$ antennas and one access point (AP). The users have some computational tasks, and some of them can be decoupled by the AP, at the cost of a fee charged by the AP. For the considered system, we firstly consider two important metrics of interest: latency and fee. Then, we formulate a stochastic game to model the interaction between users and the AP. In this game, the AP sets prices to maximize its profit, while users devise the offloading strategy to reduce both the latency and charge. We further optimize the system by applying the array signal processing schemes on the users, in order to reduce the transmission latency. Simulation results are finally presented to verify the effectiveness of the stochastic game, and it is shown that the array signal processing scheme can help reduce the transmission latency significantly.

Evaluation of an Adaptive Dynamic Compensation System in Cochlear Implant Listeners.

Cochlear implant (CI) sound processing typically uses a front-end automatic gain control (AGC), reducing the acoustic dynamic range (DR) to control the output level and protect the signal processing against large amplitude changes. It can also introduce distortions into the signal and does not allow a direct mapping between acoustic input and electric output. For speech in noise, a reduction in DR can result in lower speech intelligibility due to compressed modulations of speech. This study proposes to implement a CI signal processing scheme consisting of a full acoustic DR with adaptive properties to improve the signal-to-noise ratio and overall speech intelligibility. Measurements based on the Short-Time Objective Intelligibility measure and an electrodogram analysis, as well as behavioral tests in up to 10 CI users, were used to compare performance with a single-channel, dual-loop, front-end AGC and with an adaptive back-end multiband dynamic compensation system (Voice Guard [VG]). Speech intelligibility in quiet and at a +10 dB signal-to-noise ratio was assessed with the Hochmair–Schulz–Moser sentence test. A logatome discrimination task with different consonants was performed in quiet. Speech intelligibility was significantly higher in quiet for VG than for AGC, but intelligibility was similar in noise. Participants obtained significantly better scores with VG than AGC in the logatome discrimination task. The objective measurements predicted significantly better performance estimates for VG. Overall, a dynamic compensation system can outperform a single-stage compression (AGC + linear compression) for speech perception in quiet.

High Fidelity Physics Simulation of 128 Channel MIMO Sensor for 77GHz Automotive Radar

Automotive radar is one of the enabling technologies for advanced driver assistance systems (ADAS) and subsequently fully autonomous vehicles. Along with determining the range and velocity of targets with fairly high resolution, autonomous vehicles navigating complex urban environments need radar sensors with high azimuth and elevation resolution. Size and cost constraints limit the physical number of antennas that can be used to achieve high resolution direction-of-arrival (DoA) estimation. Multiple-input/multiple-output (MIMO) schemes achieve larger virtual arrays using fewer physical antennas than would be needed for a single-input/multiple-output (SIMO) system. This paper presents a high-fidelity physics simulation of a 77GHz, frequency-modulated continuous-waveform (FMCW)-based 128 channel (8 transmitters (T x ), 16 receivers (R x )) MIMO radar sensor. The 77GHz synthetic radar returns from full scale traffic scenes are obtained using a high-fidelity physics, shooting and bouncing ray electromagnetics solver. A fast Fourier transform (FFT) based signal processing scheme is used across slow-time (chirp) and space (channel) to obtain range-Doppler and DoA maps, respectively. Detection and angular separation performance comparisons of 16, 64 and 128 channel MIMO radar sensors are made for two complex driving scenarios.

Human Frequency Following Responses to Vocoded Speech: Amplitude Modulation Versus Amplitude Plus Frequency Modulation.

The most commonly employed speech processing strategies in cochlear implants (CIs) only extract and encode amplitude modulation (AM) in a limited number of frequency channels. proposed a novel speech processing strategy that encodes both frequency modulation (FM) and AM to improve CI performance. Using behavioral tests, they reported better speech, speaker, and tone recognition with this novel strategy than with the AM-alone strategy. Here, we used the scalp-recorded human frequency following responses (FFRs) to examine the differences in the neural representation of vocoded speech sounds with AM alone and AM + FM as the spectral and temporal cues were varied. Specifically, we were interested in determining whether the addition of FM to AM improved the neural representation of envelope periodicity (FFRENV) and temporal fine structure (FFRTFS), as reflected in the temporal pattern of the phase-locked neural activity generating the FFR. FFRs were recorded from 13 normal-hearing, adult listeners in response to the original unprocessed stimulus (a synthetic diphthong /au/ with a 110-Hz fundamental frequency or F0 and a 250-msec duration) and the 2-, 4-, 8- and 16-channel sine vocoded versions of /au/ with AM alone and AM + FM. Temporal waveforms, autocorrelation analyses, fast Fourier Transform, and stimulus-response spectral correlations were used to analyze both the strength and fidelity of the neural representation of envelope periodicity (F0) and TFS (formant structure). The periodicity strength in the FFRENV decreased more for the AM stimuli than for the relatively resilient AM + FM stimuli as the number of channels was increased. Regardless of the number of channels, a clear spectral peak of FFRENV was consistently observed at the stimulus F0 for all the AM + FM stimuli but not for the AM stimuli. Neural representation as revealed by the spectral correlation of FFRTFS was better for the AM + FM stimuli when compared to the AM stimuli. Neural representation of the time-varying formant-related harmonics as revealed by the spectral correlation was also better for the AM + FM stimuli as compared to the AM stimuli. These results are consistent with previously reported behavioral results and suggest that the AM + FM processing strategy elicited brainstem neural activity that better preserved periodicity, temporal fine structure, and time-varying spectral information than the AM processing strategy. The relatively more robust neural representation of AM + FM stimuli observed here likely contributes to the superior performance on speech, speaker, and tone recognition with the AM + FM processing strategy. Taken together, these results suggest that neural information preserved in the FFR may be used to evaluate signal processing strategies considered for CIs.

Perceived sound quality of different signal processing algorithms by cochlear implant listeners in real-world acoustic environments

Well-documented benefits of noise-reduction technologies in laboratories do not always yield a significant difference in real-world acoustic environments. Many possible reasons were proposed and studied to address this discrepancy. The purpose of this study was to examine the effectiveness of different noise reduction strategies for cochlear implants in real-world acoustic environments. Sixteen listeners were fit with hearing aid preprocessors with electrical outputs and cochlear implant speech processors receiving the electrical inputs. The preprocessors were programmed to 1) no noise reduction: omnidirectional microphone (OMNI), 2) moderate noise reduction: a combination of omnidirectional and adaptive directional microphone modes with modulation-based noise reduction (TRI+NR); and 3) maximum noise reduction: adaptive directional microphone in all frequency channels with NR (ADM+NR). Listeners listened to sentences in a noisy café, a noisy restaurant, and a quiet hotel lobby. They were instructed to rate the overall sound quality preference, ease of listening, speech intelligibility, and listening comfort of sentences using a paired-comparison categorical rating paradigm. Results indicate cochlear implant listeners had no microphone preference in quiet but they preferred adaptive directional microphones in noisy environments. The paired-comparison categorical rating paradigm is a viable means to evaluate the benefits of signal processing strategies in real-world acoustic environments.