Linear Detection Methods Research Articles

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

Portable 5-DOF measurement system using a parallel beam generation method for linear axis detection

Geometric error detection is crucial for evaluating the accuracy of the linear axis. However, the practicality of traditional dual-beam detection systems is limited by the parallelism of beams. This study proposes a portable 5-DOF measurement system using a novel parallel beam generation method. Two orthogonal corner cube retroreflectors (CCRs) and a beam splitter (BS) are utilized to achieve two measuring beams with excellent parallelism, which is determined solely by the CCR. The theoretical parallelism of beams is analyzed and experimentally verified. Two position sensitive detectors (PSDs) and one autocollimator are used to measure two straightness errors and three angular errors, and the detection deviations are modelled and compensated. The experiment proves that dual beam that are generated on the basis of the above structure could achieve a parallelism of 5.9′′ without careful adjustment. The designed 5-DOF measurement system has a straightness measurement range of ± 400 µm and an angle measurement range of ± 300′′. The repeatability of the system is 2.20 µm for straightness errors, 1.58′′ for yaw error, 1.82′′ for pitch error and 5.04′′ for roll error detection. The designed 5-DOF measurement system has the advantages of a simple structure and stable accuracy and is very practical in measuring the geometric errors of machine tools.

Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation

AbstractIn order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy.

Low complexity signal detection based on traditional methods and neural networks for massive MIMO systems

For the massive multiple-input multiple-output (MIMO) uplink, even though linear signal detection methods like zero-forcing (ZF) method or minimum mean square error (MMSE) method are capable of achieving near-optimal performance, the high computational complexity on account of matrix inversion has to be accepted. Existing methods to deal with this problem can be divided into three typical categories including approximation methods, iterative methods and methods based neural networks. In this paper, the characteristics of these algorithms and the connections between them are introduced in detail. Meanwhile, the letter points out the application of neural networks and machine learning is the future research direction in the field of communication systems.

Low complexity deep neural network based transmit antenna selection and signal detection in SM-MIMO system

In this paper, with the investigation of the conventional Euclidean distance based transmit antenna selection (ED-AS) and the maximum likelihood detection (MLD) in spatial modulation (SM) multiple-input multiple-output (MIMO) system, we propose the deep neural network (DNN) based transmit antenna selection (DNN-AS) and signal detection (DNN-SD), respectively, to effectively balance the system performance and the complexity. For the proposed DNN-AS, we transform the existing problem of AS into a prediction problem and design a low dimension multi-output classifier to achieve the low complexity solution of AS. For the DNN-SD, we present two sub-DNNs to recover the transmitted SM signal. Numerical results reveal that the proposed DNN-AS scheme gets a better average bit error rate (ABER) performance than the typical AS schemes in SM-MIMO system with lower complexity. In contrast to the ED-AS approach, it attains the optimal and suboptimal ABER performance at low-and-moderate signal-to-noise ratio (SNR) region and high SNR region, respectively. The proposed DNN-AS scheme achieves obvious SNR gains compared with the conventional SM system and gets about 3.5 dB gains over the typical maximum-norm based AS (Norm-AS) algorithm. Furthermore, the proposed DNN-SD scheme obtains a superior detection performance compared with the conventional linear detection methods and provides the same ABER performance as the optimum MLD scheme in the presence of correlated noise.

Applications of bispectrum analysis to inspection of fatigue damage in quay cranes

The quay crane is a special mechanical equipment which can lift the container vertically and horizontally through intermittent circulation movement. Fatigue crack is one of the main kinds of damage in quay cranes. The generation and development of fatigue crack are invisible, which makes the identification of fatigue cracks a great challenge. From the dynamic point of view, fatigue cracks have nonlinear dynamic characteristics beyond the linear dynamic category. The traditional damage detection methods based on linear hypothesis are difficult to effectively recognize the nonlinear dynamic characteristics of fatigue cracks, so they cannot be effectively used for structural fatigue crack detection. In order to overcome the limitations of the existing linear damage detection methods, bispectrum analysis, a nonlinear dynamic analysis method, is introduced in this thesis. By drawing on the use of bispectrum analysis to describe the nonlinear dynamic characteristics of rotating structures, a fatigue crack detection method based on nonlinear dynamic characteristics of quay cranes is developed.

Optimal weak measurement in the photonic spin Hall effect for arbitrary linear polarization incidence

The photonic spin Hall effect (SHE) has great potential in precision metrology due to its unique spin modulation characteristics. To improve its potential, the effective enhancement of detection precision has become an important issue. In this work, we theoretically and experimentally demonstrate the optimal weak measurement (optimal overlap of pre-selected and post-selected states) with arbitrary linear polarization incidence for both amplified transverse and in-plane shift. Also, based on photonic SHE, a method for arbitrary linear polarization angle detection is then proposed experimentally with a detection accuracy of 0.04 degree. It can provide a guidance for the weak measurement and enlarge the potential application of photonic SHE in field of precision measurement.

DsMLP: A Learning-Based Multi-Layer Perception for MIMO Detection Implemented by Dynamic Stochastic Computing

As the number of antennas increases in multi-input and multi-output (MIMO) systems, even linear detection methods suffer from sharply increasing complexity. This paper proposes a learning-based multi-layer perception (MLP), named dynamic stochastic multi-layer perception (DsMLP), which is implemented by dynamic stochastic computing (DSC). We first establish a similar form between the MLP structure and minimum mean square error (MMSE) matrix operations. Consequently, DsMLP transforms the complex computation problem into an optimization problem of MLP training. Due to the specific design of MLP structure, e.g., same input/output dimension and single layer without activation function, the mathematical representation of DsMLP is identical to the MMSE matrix operations. Therefore, DsMLP guarantees sound model explainability in mathematics, fast convergence in training, and low complexity in computation. Furthermore, we transform the MLP training process to the DSC domain and propose a hardware-efficient scheme for DsMLP. Compared with other state-of-the-art MIMO detectors, DsMLP achieves 1.2× energy efficiency and 1.74× area efficiency.

Optimization design of O-ring linear accelerator gantry and mechanical isocenter detection method.

With the development of the precise radiotherapy, the accuracy of radiotherapy equipment is gradually improved. The gantry, the carrier of the treatment head and various testing devices, is the most important component that determines the accuracy of the entire equipment. In this paper, the layout of the O-ring linear accelerator and the structure of the gantry are optimized to reduce the weight of the gantry by 50% and the moment of inertia by 60%. A mechanical isocenter detection method based on the laser tracker is proposed to conduct a rapid and accurate isocenter measurement for the optimized gantry. The experimental results show that the maximum deformation of the optimized gantry under load is 0.13mm during rotation and the maximum intersection distance between the gantry axis and the treatment head axis at the isocenter position is 0.21mm.

Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack.

A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures.

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

Low-complexity soft-output signal detector for massive MIMO with higher order QAM constellations

As the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems is dramatically increased, linear detection methods are able to achieve the near optimal performance at the expense of performing the complicated matrix inversion of a high dimensional matrix, which makes hardware implementation infeasible. Meanwhile, the straightforward application of the hard symbol-to-bit decision algorithm yields a very poor performance for the higher order quadrature amplitude modulation (QAM) that is widely adopted as an efficient tool to accommodate high demand of data throughput in modern wireless systems. In this work, we propose a low-complexity soft-output signal detector for multiuser massive MIMO communication systems employing Gray-coded higher order QAM with square constellations. In the proposed detector, the two-dimensional double successive projection (2D-DSP) algorithm is utilized to iteratively realize zero-forcing (ZF) algorithm for multiuser signal recovery, which circumvents the matrix inverse operation. Moreover, a simplified implementation of Max-Log-MAP algorithm is developed to calculate log likelihood ratios (LLRs) for soft-input channel decoding by fully exploiting the bit-flipping property of Gray-coded modulation scheme along with the utilization of the channel hardening property of the massive MIMO systems. Numerical results show that the proposed soft-output detector provides a relatively good tradeoff between the complexity and performance compared with several existing detectors, and approaches the performance of the ZF algorithm with only 3 iterations.

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Large-Scale Antenna Systems and Massive Machine Type Communications

In this paper, we identify issues and possible solutions in the key area of large-scale antenna systems, also know as Massive Multiple Input Multiple Output (MIMO) systems. Additionally, we propose the use of Massive MIMO technology as a means to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), the capacity of the MTC network and, in turn, that of the whole system, can be increased by grouping Machine-Type Communication (MTC) devices into clusters and letting each cluster share the same time-frequency physical resource blocks. We study the feasibility of applying sub-optimal linear detection to the problem of detecting a large number of MTC devices sharing the same time-frequency resources at the uplink of a base station (BS) equipped with a large number of antennas, M. In our study, we derive the achievable lower-bound rates for the studied sub-optimal linear detectors and show that the transmitted power of each MTC device can be reduced as M increases, which is a very important result for power-constrained MTC devices running on batteries. Our simulation results suggest that, as M is made progressively larger, the performance of sub-optimal linear detection methods approach the matched filter bound, also known as perfect interference-cancellation bound.

High-Precision 5DoF Tracking and Visualization of Catheter Placement in EVD of the Brain Using AR

External ventricular drainage (EVD) is a high-risk medical procedure that involves inserting a catheter inside a patient’s skull, through the brain and into a ventricle, to drain cerebrospinal fluid and thus relieve elevated intracranial pressure. Once the catheter has entered the skull, its tip cannot be seen. The neurosurgeon has to imagine its location inside the cranium and direct it toward the ventricle using only anatomic landmarks. The EVD catheter is thin and thus hard to track using infra-red depth sensors. Traditional optical tracking using fiducial or other markers inevitably changes the shape or weight of the medical instrument. We present an augmented reality system that depicts the catheter for EVD and a new technique to precisely track the catheter inside the skull. Our technique uses a new linear marker detection method that requires minimal changes to the catheter and is well suited for tracking other thin medical devices that require high-precision tracking.

Research on network intrusion detection security based on improved extreme learning algorithms and neural network algorithms

In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion; the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability; it has good application value in network security.

Research on network intrusion detection security based on improved extreme learning algorithms and neural network algorithms

In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion; the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability; it has good application value in network security.

Nonlinear crack assessment method in beams based on bispectrum-normal cloud model

Fatigue damage in engineering structures is universal. The occurrence of fatigue cracks brings unpredictable hidden dangers to a structure in terms of safety and service performance. Traditional damage identification methods, such as power spectrum analysis, are mostly based on linear elasticity theory that cannot reflect the typical nonlinear characteristics of fatigue cracks and cannot meet the higher requirements of the signal analysis method put forward by current mass detection data. To solve this problem, a numerical model of a cantilever beam with a breathing crack is established in this study. A method for diagnosing fatigue damage is studied by combining bispectral analysis and a statistical normal cloud model, which characterize the nonlinear characteristics of the structure. This method can effectively describe the nonlinear characteristics of the structure and reasonably evaluate the degree of fatigue damage in the structure. The bispectrum-normal cloud model method proposed in this study overcomes the limitations of existing linear damage detection methods in nonlinear damage detection, and can improve the efficiency of signal analysis from a statistical point of view. It has good prospects for structural nonlinear damage assessment.

Massively Parallel Tree Search for High-Dimensional Sphere Decoders

The recent paradigm shift towards the transmission of large numbers of mutually interfering information streams, as in the case of aggressive spatial multiplexing, combined with requirements towards very low processing latency despite the frequency plateauing of traditional processors, initiates a need to revisit the fundamental maximum-likelihood (ML) and, consequently, the sphere-decoding (SD) detection problem. This work presents the design and VLSI architecture of MultiSphere; the first method to massively parallelize the tree search of large sphere decoders in a nearly-concurrent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity comparable to that of highly-optimized sequential sphere decoders. For a $10\times 10$10×10 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements, our MultiSphere architecture can reduce latency by $29\times$29× against well-known sequential SDs, approaching the processing latency of linear detection methods, without compromising ML optimality. In MIMO multicarrier systems targeting exact ML decoding, MultiSphere achieves processing latency and hardware efficiency that are orders of magnitude improved compared to approaches employing one SD per subcarrier. In addition, for $16\!\times \!16$16×16 both “hard”- and “soft”-output MIMO systems, approximate MultiSphere versions are shown to achieve similar error rate performance with state-of-the art approximate SDs having akin parallelization properties, by using only one tenth of the processing elements, and to achieve up to approximately $9\!\times$9× increased energy efficiency.

Performance of ZF and MMSE decoders for massive multi-cell MIMO systems in impulsive and Laplacian noise channels

In this paper, we examine the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF linear detection methods in non-Gaussian impulsive and Laplacian multi-cell MIMO channels. We start by showing the symbol error rate performance in non-Gaussian channels for ZF detection, and then we build over it and extend to the derivations for MMSE ZF system. The lower and upper bound derivations are clearly shown and verified through computer software Montecarlo simulations.