Low-complexity Signal Processing Research Articles

Pothole detection and International Roughness Index (IRI) calculation using ATVs for road monitoring

The accelerated deterioration of roads is conditioned by parameters such as climate change, poor construction, and heavy vehicle traffic. Two relevant measures to monitor the condition of a road are the International Roughness Index (IRI) and the number of functional failures in a segment, mainly potholes, since they are associated with higher risks such as accidents or damage to vehicle mechanics. In the state of the art, pothole detection or International Roughness Index (IRI) calculation algorithms are proposed, but they use vehicles designed to produce less vibration and use phones that decrease the performance of the embedded sensors. In addition, some works propose complex algorithms of higher computational load that leads to use more hardware and power consumption. In this context, the present work aims to monitor the condition of a road through low-cost dedicated sensors implemented in an urban patrolling all-terrain vehicles (ATVs), where energy consumption is optimized using low-complexity signal processing techniques for noise reduction and detection algorithms. The results show an average accuracy of 90.5% in the detection of potholes, a relative error of 8.41% in the calculation of the International Roughness Index (IRI) and an average reduction of 65.4% in the monitoring time.

Distributed MMSE-based uplink receive combining, downlink transmit precoding and optimal power allocation in cell-free massive MIMO systems

In cell-free massive multiple-input multiple-output systems, a large number of distributed wireless access points (AP) simultaneously serve a number of user equipments (UEs). This setup has recently been introduced as a promising alternative for the current 5G cellular networks. The setup has the ability to offer a good quality of service, especially for the UEs on the cell edges, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal power allocation combined with uplink receive combining and downlink transmit precoding is tackled by providing efficient distributed MMSE-based algorithms. The necessary fronthaul communications to estimate the combining/precoding vectors and the necessary large-scale channel statistics are reduced to a minimum and rely on in-network summation that can be accomplished whenever the APs can be arranged into a tree-topology. Non-weighted max-sum and max–min are used as utilities for the power allocation, but the algorithms are not limited to these cases. Simulations show that the proposed algorithms outperform heuristic power allocation methods, both in uplink and downlink.

A Study of Point Laser for Automatic Local Road Condition Assessment: Feasibility, Challenges, and Limitations

Road condition assessment plays an important role in maintaining the integrity of mature and fast-expanding road networks. Such assessments provide invaluable information for local authorities that enable them to deliver timely preventative road network maintenance. To enable the best use of scarce maintenance resources, it is necessary to assess the condition of the roads periodically. In this article, we present the Point Laser (PL) system as an alternative approach for automatic road assessment. Compared to current systems, ground penetrating radar and the mobile laser scanning system, the PL system has the advantages of low hardware cost, low computational requirements, and fewer environmental restrictions in relation to its use. The article first outlines the principle of the PL system including a low-complexity signal processing approach. Afterward, the feasibility, challenges, and limitations of the PL system are discussed based on its use in a real environment. A visual-based road survey is used as a ground truth to determine the PL’s performance in relation to the requirements of road condition assessment at various levels of road management. The results of the comparison demonstrate that the PL system is an ideal candidate for the automatic assessment of the condition of local road networks, especially those that require frequent assessment for road asset management.

A Portable Traffic Counting, Speed Estimation, and Classification Terminal Using IR-UWB Radar

Because of the stable performance under any light and weather conditions, radar becomes one of the most important sensors for traffic flow surveillance in intelligent transportation system (ITS). This paper proposes a novel portable traffic counting, speed estimation, and classification terminal using impulse radio ultra-wideband (IR-UWB) radar. The proposed terminal is lightweight, installed at the roadside, and potable. Moreover, it can only operate on a micro control unit of ARM Cortex-M7 with low-complexity signal processing approaches. Specifically, a vehicle detection and counting approach based on target location combined with trajectory tracking, a non-coherent speed estimation approach by exploiting the trajectory and prior geometric information, and a vehicle type classification approach based on four different attribute features are developed. Finally, experiment results show the improved performance of the proposed approaches.

Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations

A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator’s location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance.

Real-Time Light Field Signal Processing Using 4D/5D Linear Digital Filter FPGA Circuits

Light fields (LFs) and light field videos (LFVs) capture both angular and spatial variation of light rays emanating from scenes. This richness of information leads to novel applications such as post-capture refocusing, depth estimation and depth-velocity filtering which are not possible with images and videos. These capabilities come, however, with a significant increase in data to be processed. In order to fully exploit opportunities provided by LFs and LFVs, low-complexity signal processing algorithms that process LF and LFV data in real-time are required. In this brief, we survey such state-of-the-art algorithms, in particular for depth filtering, refocusing and denoising of LFs and depth-velcoty filtering for LFVs, and future directions for these real-time LF an LFV processing algorithms.

Low-Complexity Polar Coded Noncoherent Detection Using Information Reconstruction

Combining noncoherent detection (NCD) with polar codes is a promising way to improve the performance of polar-coded systems in presence of imperfect channel state information. This letter proposes a novel NCD architecture for polar-coded systems by exploiting information feedback and reconstruction. Specifically, extra low-complexity signal processing steps are merged into the conventional polar-coded scheme with NCD, yielding low detection complexity that is crucial for resource-limited systems. Moreover, a polar-coded NCD algorithm is proposed via the information feedback and reconstruction. Simulation results prove that our proposed algorithm outperforms conventional NCD algorithms in terms of bit error rate and block error rate. Finally, the complexity of the proposed NCD algorithm is analyzed in detail.

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

Microwave Non-Destructive Testing (NDT) techniques for dielectric coatings are vital processes in many industrial applications due to their superior defect detection capabilities over traditional NDT methods. However, the limited bandwidth and post-processing complexities of these techniques cause them to fall short of accurately estimating the delamination thickness in such structures. In this paper, a novel low-complexity signal processing method is proposed to estimate the delamination thickness in metal-backed dielectric coatings using bandwidth-limited Open-Ended Rectangular Waveguide (OERW) probes. Here, Inverse Fast Fourier Transform (IFFT) process is used to convert the measured complex reflection coefficients at the surface of the coating to the time domain. Next, the amplitude of a specific time step is used to accurately estimate the delamination thickness without extra time-consuming processing. Using a 3 mm-thick macor samples with machined defects, the proposed technique is validated by correctly estimating delamination thickness down to 0.4 mm using an OERW probe with 13.5 GHz bandwidth. Hence, this technique overcomes the challenge of the OERW's limited bandwidth, which highly affects the possibility of tracking shifting in the peak reflection in the time domain. Moreover, the proposed method does not add to the complexity of the NDT process, which makes it suitable for in-situ real time applications. Hence, following this approach would be of great importance to numerous industries, where accurate thickness estimation of minute delamination in coatings is essential to avoid system failure.

An Optimality Summary: Secret Key Agreement with Physical Unclonable Functions.

We address security and privacy problems for digital devices and biometrics from an information-theoretic optimality perspective to conduct authentication, message encryption/decryption, identification or secure and private computations by using a secret key. A physical unclonable function (PUF) provides local security to digital devices and this review gives the most relevant summary for information theorists, coding theorists, and signal processing community members who are interested in optimal PUF constructions. Low-complexity signal processing methods are applied to simplify information-theoretic analyses. The best trade-offs between the privacy-leakage, secret-key, and storage rates are discussed. Proposed optimal constructions that jointly design the vector quantizer and error-correction code parameters are listed. These constructions include modern and algebraic codes such as polar codes and convolutional codes, both of which can achieve small block-error probabilities at short block lengths, corresponding to a small number of PUF circuits. Open problems in the PUF literature from signal processing, information theory, coding theory, and hardware complexity perspectives and their combinations are listed to stimulate further advancements in the research on local privacy and security.

Low-Complexity System and Algorithm for an Emergency Ventilator Sensor and Alarm.

In response to anticipated shortages of ventilators caused by the COVID-19 pandemic, many organizations have designed low-cost emergency ventilators. Many of these devices are pressure-cycled pneumatic ventilators, which are easy to produce but often do not include the sensing or alarm features found on commercial ventilators. This work reports a low-cost, easy-to-produce electronic sensor and alarm system for pressure-cycled ventilators that estimates clinically useful metrics such as pressure and respiratory rate and sounds an alarm when the ventilator malfunctions. A low-complexity signal processing algorithm uses a pair of nonlinear recursive envelope trackers to monitor the signal from an electronic pressure sensor connected to the patient airway. The algorithm, inspired by those used in hearing aids, requires little memory and performs only a few calculations on each sample so that it can run on nearly any microcontroller.

Performance analysis of MIMO and MISO time division duplexing wireless links with SWIPT and antenna selection

It is expected that many small battery-operated devices will connect to next generation of cellular networks. In this work, a two-way wireless link is considered, where due to slow variations of the channel, forward link (FL) and reverse link channels are reciprocal, and the remote device’s battery is charged via wireless energy harvesting. We analyze the performance of a two-way link setup, with very low complexity signal processing at the node that relies on battery, and consists of antenna selection and maximum ratio combining-based multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) techniques, wherein FL simultaneous wireless information and power transmission is assumed to be utilized. By mathematical tractability, a closed-form analytical expression for probability of outage, ergodic capacity and bit error rate are derived and compared with simulation results. It is shown that the assumed low complexity setup achieves full diversity in both cases of MISO and MIMO.

Improper Gaussian Signaling for Integrated Data and Energy Networking

The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network’s throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.

Multi-depth filtering and occlusion suppression in 4-D light fields: Algorithms and architectures

Low-complexity signal processing algorithms and reconfigurable digital hardware architectures are proposed for multi-depth filtering and occlusion suppression in 4-D light fields (LFs). The proposed multi-depth-pass (MDP) and multi-depth-reject (MDR) 4-D filters allow simultaneous enhancement of planar objects at multiple desired depths and attenuation of planar objects at multiple undesired depths, thereby achieving electronically tunable multi-depth focusing. Partial-separability and recursive nature of the proposed MDP and MDR transfer functions ensure ultra-low hardware complexity suitable for real-time processing of large volume of input samples encountered in 4-D LFs. Filter synthesis details and examples with synthetic and real LFs of size 15 × 15 × 434 × 625 are presented. A generic framework encapsulating a multitude of possible digital hardware realizations is described, employing J-unfolding and look-ahead pipelining albeit with modifications as applicable to the underlying multi-dimensional signal flow graphs to obtain increased real-time throughput. Proof-of-concept digital designs are provided for the 2-passband case of a MDP filter verifying a real-time throughput of ≈ 490 LFs of size 9 × 9 × 434 × 625 per second for filters operating at 135 MHz on a Xilinx Virtex-7 FPGA device.

Design and Implementation of 5-D IIR Depth Velocity Filters for Light Field Video Processing

The design and hardware implementation of a low-complexity signal processing algorithm is proposed for real-time depth-velocity filtering in 5-D light field videos (LFVs). The proposed design is based on a stable 5-D infinite impulse response digital filter having three cascaded sections, each synthesized using the concept of multidimensional passive network resonance. A novel semi-systolic hardware implementation is proposed. Each section of the filter is implemented and tested on a Xilinx Virtex-7 FPGA platform using MATLAB-based hardware co-simulation with both synthetic and real LFV signals. A real-time processing throughput of 467 LFV frames/s is implied with each section of the filter operating at 204, 164, and 115 MHz for input LFV frames of size $9 \times 9 \times 220 \times 360$ .

Low‐Complexity separable beamformers for massive antenna array systems

Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low-complexity signal processing methods. We propose optimising a Kronecker-separable beamforming filter that takes advantage of the bi-dimensional array geometry to reduce computational costs. The Kronecker factors are obtained using two strategies: alternating optimisation, and sub-array minimum mean square error beamforming with Tikhonov regularization. According to the simulation results, the proposed methods are computationally efficient but come with source recovery degradation, which becomes negligible when the sources are sufficiently separated in space.

Single-Carrier Spatial Modulation for the Internet of Things: Design and Performance Evaluation by Using Real Compact and Reconfigurable Antennas

In this paper, for the first time, we propose two new solutions to boost the data rate between small connected objects such as glasses and cams and the 5th generation (5G) mobile network, based on spatial modulation, single carrier waveform, compact reconfigurable antennas at the object side and massive multiple input multiple output (M-MIMO) at the network side. In the first new wireless communication system, a "transmitting object" uses transmit spatial modulation with a compact reconfigurable antenna and a constant envelop amplifier to transmit in high data rate with a low complexity and low power consumption. The space-time digital processing capability of the M-MIMO 5G base station is used to detect such signal. In the second new wireless communication system, a "receiving object" uses receive spatial modulation, a compact multiport antenna and a low complexity detection algorithm to receive in high data rate with a low complexity signal processing. The space-time beamforming capability of the M-MIMO 5G base stations is exploited to deliver a signal that is pre-equalized enough to be detected by the object. For the first time, we present experiments showing that M-MIMO allows for the re-introduction of single carrier modulation waveform. For the first time, we present performance results obtained with real existing compact antennas and compact reconfigurable antennas, showing that the two new communication systems outperform conventional modulation in terms of energy efficiency and complexity.

Cell-Free Massive MIMO: A New Next-Generation Paradigm

Cell-free (CF) massive multiple-input-multiple-output (MIMO) systems have a large number of individually controllable antennas distributed over a wide area for simultaneously serving a small number of user equipments (UEs). This solution has been considered as a promising next-generation technology due to its ability to offer a similar quality of service to all UEs despite its low-complexity signal processing. In this paper, we provide a comprehensive survey of CF massive MIMO systems. To be more specific, the benefit of the so-called channel hardening and the favorable propagation conditions are exploited. Furthermore, we quantify the advantages of CF massive MIMO systems in terms of their energy- and cost-efficiency. Additionally, the signal processing techniques invoked for reducing the fronthaul burden for joint channel estimation and for transmit precoding are analyzed. Finally, the open research challenges in both its deployment and network management are highlighted.

An Interference Cancellation Scheme for High Reliability Based on MIMO Systems

This article proposes a new interference cancellation scheme in a half-duplex based two-path relay system. In the conventional two-path relay system, inter-relay-interference (IRI) which severely degrades the error performances at a destination occurs because a source and a relay transmit signals simultaneously at a specific time. The proposed scheme removes the IRI at a relay for higher signal-to-interference plus noise ratio (SINR) to receive interference free signal at a destination, unlike the conventional relay system, which removes IRI at a destination. To handle the IRI, the proposed scheme uses multiple-input multiple-output (MIMO) signal detection at the relays and it makes low-complexity signal processing at a destination which is a usually mobile user. At the relays, the proposed scheme uses the low-complexity QR decomposition-M algorithm (QRD-M) to optimally remove the IRI. Also, for obtaining diversity gain, the proposed scheme uses cyclic delay diversity (CDD) to transmit the signals at a source and the relays. In simulation results, the error performance for the proposed scheme is better when the distance between one relay and another relay is low unlike the conventional scheme because the QRD-M detects received signal in order of higher post signal-to-noise ratio (SNR).

R–DFT-based Parameter Estimation for WiGig

In this paper we present parameter estimation methods for IEEE 802.11ad transmission to estimate the frequency offset value and channel impulse response. Furthermore a less known low complexity signal processing architecture – the Recursive Discrete Fourier Transform (R-DFT) – is applied which may improve the estimation results. The paper also discusses the R-DFT and its advantages compared to the conventional Fast Fourier Transform.

Time Domain Audio Features for Chainsaw Noise Detection Using WSNs

In this paper, we present an analysis of low complexity signal processing algorithms capable of identifying special noises, such as the sounds of forest machinery (used for forestry, logging). Our objective is to find methods that are able to detect internal combustion engines in rural environment, and are also easy to implement on low power devices of WSNs (wireless sensor networks). In this context, we review different methods for detecting illegal logging, with an emphasis on autocorrelation and TESPAR audio techniques. The processing of extracted audio features is to be solved with limited memory and processor resources typical for low cost sensors modes. The representation of noise models is also considered with different archetypes. Implementations of the proposed methods were tested not by simulations but on sensor nodes equipped with an omnidirectional microphone and a low power microcontroller. Our results show that high recognition rate can be achieved using time domain algorithms and highly energy efficient and inexpensive architectures.