Low Implementation Complexity Research Articles

DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification.

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

An efficient implementation of three-level boost converter with capacitor voltage balancing for an advanced MPPT approach in PV Systems

Ensuring optimal extraction of power from photovoltaic (PV) systems under diverse climatic circumstances is a significant challenge, which requires an efficient Maximum Power Point (MPP) Tracking (MPPT) algorithm and an adequate conversion stage. Therefore, this paper proposes an advanced MPPT strategy implemented by a Three-Level Boost converter (TLB), which serves as the interface between the PV generator and the DC load, offering advantages such as reduced output voltage distortion, minimized inductor current ripple, and decreased switching losses. To achieve more accurate tracking, a robust voltage balance control across the TLB converter capacitors is also suggested. The proposed system is meticulously implemented and evaluated using MATLAB/Simulink, demonstrating its robust control capabilities in MPP tracking and voltage balancing across the TLB converter capacitors under diverse climatic scenarios. Furthermore, comparative analysis under various test circumstances including static and dynamic insolation conditions and sudden load variations, shows that the suggested MPPT algorithm offers noteworthy improvements over the InC algorithm. Additionally, a thorough comparison with existing research methods proves its effectiveness over these methods. The advanced MPPT strategy achieves a steady-state error of 0 W, an average tracking efficiency between 99.13 % and 99.44%, and fast-tracking velocity. Further, a comprehensive real-time evaluation using the cutting-edge RT-LAB platform confirms the consistency of the results with the simulation outputs, emphasizing the robustness and low implementation complexity of the overall proposed control strategy.

Adaptive Segmented Aggregation and Rate Assignment Techniques for Flexible-Length Polar Codes.

Polar codes have garnered a lot of attention from the scientific community, owing to their low-complexity implementation and provable capacity achieving capability. They have been standardized to be used for encoding information on the control channels in 5G wireless networks due to their robustness for short codeword lengths. The conventional approach to generate polar codes is to recursively use 2×2 kernels and polarize channel capacities. This approach however, has a limitation of only having the ability to generate codewords of length Norig=2n form. In order to mitigate this limitation, multiple techniques have been developed, e.g., polarization kernels of larger sizes, multi-kernel polar codes, and downsizing techniques like puncturing or shortening. However, the availability of so many design options and parameters, in turn makes the choice of design parameters quite challenging. In this paper, the authors propose a novel polar code construction technique called Adaptive Segmented Aggregation which generates polar codewords of any arbitrary codeword length. This approach involves dividing the entire codeword into smaller segments that can be independently encoded and decoded, thereby aggregated for channel processing. Additionally a rate assignment methodology has been derived for the proposed technique, that is tuned to the design requirement.

Real-time passive underwater localization using a compact acoustic sensor array

This paper proposes an underwater passive localization system that can be installed on remote platforms and can locate in real-time sound sources using a compact 4-element array. The localization system relies on both the Time Difference of Arrival (TDoA) algorithm to localize sources that are impulsive, and a low-complexity implementation of the matched field processing (MFP) algorithm for broadband continuous waves, such as propeller noise. The array is intended to be mounted on small platforms and compact geometries are investigated to optimize the localization accuracy. Figures of merit are defined to assess the accuracy and reliability of the algorithm as a function of the distance between the elements. The proposed algorithm is implemented on a System-on-Chip (SoC) which executes the algorithm on an embedded system for real-time operation. Finally, the performance is assessed in realistic ocean conditions in different environments. It is shown that the MFP provides an excellent angle estimate for broadband continuous sources, but TDoA is more reliable, particularly when the deployment is sensitive to the environment conditions.

A cascaded bit-weighted distribution matching for LDPC-coded PS-64QAM DMT in a high-speed Unamplified IM-DD system

We experimentally demonstrate the generation and transmission of probabilistic shaping (PS) 64-ary quadrature amplitude modulation (64-QAM) discrete multi-tone (DMT) signal with low-density parity-check (LDPC)-coded modulation based on two-stage bit-weighted distribution matching (TS-BWDM) in a high-speed unamplified intensity modulation and direct detection system. The TS-BWDM scheme is based on a two-stage bit-weighted inversion algorithm, which has the advantages of no complex multiplication and division operations and low hardware implementation complexity. As the key content of TS-BWDM, before FEC coding, the bit-weighted intervention operation is used to change the bit probability of a specific sequence in the parallel binary sequence. The two-stage bit-weighted inversion operation and one-stage weight-labeling bit insertion is performed for achieving the target symbol probability distribution. As a result, it does not add a significant amount of extra redundant bits. Additionally, the high-accuracy training symbols-based sampling frequency offset (SFO) estimation method and digital interpolation are used to compensate SFO. In our experiments, the TS-BWDM-based PS-64QAM DMT signals with a net rate of 157.3-Gb/s transmission over 10-km non-zero dispersion- shifted fiber can be achieved. The system performance is investigated under two LDPC code rates (3/4 and 9/10) and three PS parameter values (k = 3, 5 and 9). The experimental results show that the receiver power sensitivity and the system fiber nonlinear effect tolerance can be significantly improved compared with uniformly-distributed signals.

Performance Analysis of an Incremental Conductance MPPT Algorithm for Photovoltaic Systems Under Rapid Irradiance Changes

The control algorithm for tracking the maximum power point (MPPT) is a crucial factor that influences the system's capacity to harness the maximum energy from the sun using photovoltaic. Incremental conductance stands out as one of the most commonly employed MPPT techniques due to its simplicity and low implementation complexity. However, it has various shortcomings, rendering it less effective in adapting to changing solar radiation conditions. Therefore, this study proposed a modification to enhance the algorithm's performance. The proposed modified algorithm is validated through experiments, revealing that the fastest tracking time for the adjusted incremental conductance algorithm was 72 ms, whereas the unmodified algorithm took 84 ms. Additionally, oscillations observed during changes in radiation values were 2 W and 1.66 W for the unmodified algorithm, occurring during decreases and increases in radiation values, respectively. In contrast, the proposed modified incremental conductance algorithm resulted in a consistent oscillation of 1.33 W for each change in radiation value.Furthermore, it can be concluded that the modified incremental conductance algorithm can increase the tracking time as well as reduce oscillations that occur under various conditions of changes in radiation values.

A Self-Adaptive Automatic Incident Detection System for Road Surveillance Based on Deep Learning.

We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera's field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2-5% in automatic mode and 2-7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices.

Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing

AbstractAutomatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.

Optimized Sigmoid Functions for Speech Presence Probability and Gain Function in Speech Enhancement

Speech presence probability (SPP) and gain functions such as Wiener filter or MMSE estimators require an estimate of the a-priori signal-to-noise ratio (SNR). However, the estimation of the a-priori SNR is computationally involved and sensitive to noise variations. This paper proposes to approximate the SPP and the overall gain function of a speech enhancement system by using sigmoid functions to reduce the need of estimating the a-prior SNR. By applying an approximation via the sigmoid functions it is shown that only the a-posteriori estimate of SNR is needed, resulting in a low complexity system. The sigmoid function is designed with an optimization algorithm to optimize its parameters with respect to speech quality measures. The optimization algorithm is based on the idea that the solution obtained for a given problem should move towards the best solution and avoid the worst solution. The proposed algorithm requires minimal control parameters and does not require any algorithm specific parameters. Simulation results show that the proposed sigmoid functions achieve good results in terms of speech quality measures when compared with existing methods while providing significantly lower complexity for implementation.

Separated Wideband CIC Compensator with High-Flatness and Low-Complexity Implementation

Separated Wideband CIC Compensator with High-Flatness and Low-Complexity Implementation

Noise reduction in digital holography phase maps by phase-preserving discrete Fourier resampling.

Several methods have been proposed to reduce the detrimental effects of coherent noise in holographic imaging. Among them, the use of spatial-frequency masking or resampling has been widely applied because of its low implementation complexity and well-studied trade-off between denoising effectiveness and spatial resolution. While the digital application of this method has been successfully demonstrated for intensity images, its application to phase maps fails. This work shows that the phase applicability of these methods depends on the use of resampling masks that strictly keep the zero-order spatial frequencies. Alternative masks are proposed that demonstrate effective single-shot noise reduction in experimental phase maps from digital holographic microscopy. The resulting method is potentially extendable to any other complex-valued-field retrieval technique.

Low Power and Complexity Implementation of the Modified FFT with a New Bit-Slicing Scheme

Low Power and Complexity Implementation of the Modified FFT with a New Bit-Slicing Scheme

The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals.

In modern hospitals, monitoring patients' vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.

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.

Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method.

Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works on dictionary learning adopt an off-line approach, and there are two main off-line ways of conducting it. One is to alternately optimize both the dictionary and the sparse code, while the other is to optimize the dictionary by restricting it over the orthogonal group. The latter, called orthogonal dictionary learning (ODL), has a lower implementation complexity and, hence, is more favorable for low-cost devices. However, existing schemes for ODL only work with batch data and cannot be implemented online, making them inapplicable for real-time applications. This article, thus, proposes a novel online orthogonal dictionary scheme to dynamically learn the dictionary from streaming data, without storing the historical data. The proposed scheme includes a novel problem formulation and an efficient online algorithm design with convergence analysis. In the problem formulation, we relax the orthogonal constraint to enable an efficient online algorithm. We then propose the design of a new Frank-Wolfe-based online algorithm with a convergence rate of O(łn t/t1/4). The convergence rate in terms of key system parameters is also derived. Experiments with synthetic data and real-world Internet of things (IoT) sensor readings demonstrate the effectiveness and efficiency of the proposed online ODL scheme.

A Simple Chaotic Model With Complex Chaotic Behaviors and Its Hardware Implementation

Recently, due to the initial sensitivity, ergodicity, boundedness, and unpredictability of chaos, it has been widely used in the fields of secure communication, signal processing, chaotic synchronization, etc. Moreover, due to the low complexity of hardware implementation, widespread attention to one-dimensional (1D) chaotic maps has been paid from researchers. However, some existing 1D maps have simple chaotic behaviors and discrete chaotic intervals, which have significantly affected the practical application. To address these shortcomings, this paper proposes a 1D chaotic model based on the absolute value function and the cosine function called the cosine-coupled chaotic model (CCCM). It can use existing 1D chaotic maps as seed maps to construct a series of 1D chaotic maps with continuous chaotic intervals and complex chaotic behaviors. To verify the effectiveness of the CCCM, we first generate three new chaotic maps using the CCCM, and then their chaotic properties are analyzed by using various measures including Lyapunov exponent (LE), sample entropy (SE), correlation dimension (CD), 0-1 test and sensitivity. The experimental results confirm that the new chaotic maps have wider chaotic intervals and more complex chaotic behaviors than the corresponding seed maps and some other advanced chaotic maps. Then we use a field programmable gate array (FPGA) as the hardware platform for simple hardware implementations of the new chaotic maps. And a new pseudo-random number generator (PRNG) is introduced to verify practical applications of the new maps. Finally, experimental results show that the new chaotic maps can be implemented on FPGA and the proposed PRNG has strong random properties.

Resource-Efficient Low-Latency Modified Pietra-Ricci Index Detector for Spectrum Sensing in Cognitive Radio Networks

In this article, the modified Pietra-Ricci index detector (mPRIDe) is devised, and its field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs are reported. The mPRIDe attains lower implementation complexity with respect to its predecessor, without performance penalty. Moreover, the mPRIDe is blind, robust against time-varying noise and received signal powers, and exhibits the constant false alarm rate property. Two versions of the mPRIDe, namely mPRIDe v1 and mPRIDe v2, have been designed. The former privileges lower hardware complexity, whereas the latter aims lower latency, with both versions having a linear scalability with the number of sensors in cooperation. In comparison with the smallest area consumed by a state-of-the-art sensor, mPRIDe v1 and mPRIDe v2 consume 56.6% and 47.3% lower areas, respectively. The sensing times of the proposed sensors are 1.6 and 2.9 times better than the fastest sensing time of contemporary sensors. Moreover, the proposed designs deliver the lowest area-time-product and power-delay-product among state-of-the-art implementations. These metrics make both mPRIDe v1 and mPRIDe v2 the most hardware-efficient and power-efficient sensors reported in the literature.

Initial Rotor Position Estimation of SynRM Based on Pulsating Voltage Injection Combined With Finite Position Set Algorithm

Initial rotor position estimation is the basis of stable starting without reverse rotation; also initial position estimation directly affects the maximum starting torque. DC pulse voltage injection is one of the common methods for estimating the initial rotor position, which has low implementation complexity. However, the conventional DC voltage pulse injection methods, which works through injecting three DC voltage pulses, has low accuracy and slow response in estimating initial position. A rotor initial position estimation method based on pulsating voltage injection combined with an iteration algorithm is proposed in this paper. This method works by injecting only one DC voltage pulse to the motor and measuring the peak values of currents and fluxes of the motor; afterwards these values should be applied to an iterative algorithm. The iteration algorithm in the proposed method leads to increment of the accuracy of initial position estimation, and reduction of the number of injected dc pulses in comparison with the conventional methods. The performance of the proposed method is verified experimentally on a synchronous reluctance motor (SynRM). Also, a comparison between the two conventional methods and the proposed method is provided by this SynRM drive experimental setup.

Design and Low Complexity Implementation of FRM-Based OMFB With STB

Design and Low Complexity Implementation of FRM-Based OMFB With STB

FPGA Implementation of Compact Hardware Accelerators for Ring-Binary-LWE-based Post-quantum Cryptography

Post-quantum cryptography (PQC) has recently drawn substantial attention from various communities owing to the proven vulnerability of existing public-key cryptosystems against the attacks launched from well-established quantum computers. The Ring-Binary-Learning-with-Errors (RBLWE), a variant of Ring-LWE, has been proposed to build PQC for lightweight applications. As more Field-Programmable Gate Array (FPGA) devices are being deployed in lightweight applications like Internet-of-Things (IoT) devices, it would be interesting if the RBLWE-based PQC can be implemented on the FPGA with ultra-low complexity and flexible processing. However, thus far, limited information is available for such implementations. In this article, we propose novel RBLWE-based PQC accelerators on the FPGA with ultra-low implementation complexity and flexible timing. We first present the process of deriving the key operation of the RBLWE-based scheme into the proposed algorithmic operation. The corresponding hardware accelerator is then efficiently mapped from the proposed algorithm with the help of algorithm-to-architecture implementation techniques and extended to obtain higher-throughput designs. The final complexity analysis and implementation results (on a variety of FPGAs) show that the proposed accelerators have significantly smaller area-time complexities than the state-of-the-art designs. Overall, the proposed accelerators feature low implementation complexity and flexible processing, making them desirable for emerging FPGA-based lightweight applications.