Discrete Signals Research Articles

Machine learning-based approach for online fault Diagnosis of Discrete Event System

The problem considered in this paper is the online diagnosis of Automated Production Systems with sensors and actuators delivering discrete binary signals that can be modeled as Discrete Event Systems. Even though there are numerous diagnosis methods, none of them can meet all the criteria of implementing an efficient diagnosis system (such as an intelligent solution, an average effort, a reasonable cost, an online diagnosis, fewer false alarms, etc.). In addition, these techniques require either a correct, robust, and representative model of the system or relevant data or experts’ knowledge that require continuous updates. In this paper, we propose a Machine Learning-based approach of a diagnostic system. It is considered as a multi-class classifier that predicts the plant state: normal or faulty and what fault that has arisen in the case of failing behavior.

Синтез бортовых приемных устройств совмещенной обработки радиолокационных и информационных сигналов беспилотных летательных аппаратов малого класса

The synthesis of a device for synchronous reception of radar signals, in the parameters of which information messages are integrated, has been carried out. The proposed approach is relevant for increasing the information exchange capabilities under the conditions of limited onboard small class unmanned aerial vehicles resources, critical to the mass of target loads, their power consumption and electromagnetic compatibility. The consecutive implementation of the proposed approach, based on the theory of joint filtering of discrete random and continuous Markov sequences is considered. It makes possible to synthesize an algorithm for a quasi-coherent receiver’s operation of discrete radio signals with the intra-pulse keying against the white noise background. The noise immunity characteristics of the receiver synchronization system were calculated, allowing for a given transmitter power to appropriately select the synchronized generator parameters and vice versa. The feature and advantage of the synthesized synchronization system is a implementation of the accumulation principle when receiving clock pulses.

Spiking Neural Networks-Inspired Signal Detection Based on Measured Body Channel Response

Spiking neural networks (SNNs) are inspired by biological behavior in the neural system processing information by the rate or delay components of discrete spiking signals in a massively parallel manner. Sparse and asynchronous spikes allow event-driven information processes, leading to low power consumption and fast inference. By exploiting these advantageous features of the SNNs, this paper presents a signal detection method for human body communication (HBC), which has recently emerged as an innovative alternative for wireless body area networks using the human body as a signal transmission medium. In particular, binary spike signaling in the SNNs is highly appropriate for application in the digital signal transmission-based HBC systems. The experiments of body channel response measurements using digital training signals show that the body channel characteristics vary with changes in body posture and device location, especially in wearable environments requiring small-sized devices powered by batteries. The proposed SNN structures can enhance communication performance from signal distortions, stemming from the effects of the time-dispersive body channel and bandwidth-limited receive-filter. The proposed SNN-based transmission symbol code detector (STD) can improve about 3.53 dB carrier-to-noise ratio (CNR) at a bit-error-rate (BER) of 10^-6 for a data rate of 1.3125 Mbps, compared to that of a conventional maximum likelihood detector. In addition, the proposed SNN-based preamble detector can secure an approximately 150 wider threshold range than that of a conventional correlator to achieve a detection probability higher than 99% of the frame existence at a CNR of approximately 0dB required for achieving a BER of 10^-6 by the STD.

Asymptotic characteristics of multichannel access systems with orthogonal filteration

The proposed article deals with digital modulation schemes that use a certain number of equidistant orthogonal subcarriers (the socalled OFDM technology). The theory of radio engineering chains and signals is used as the scientific and technical basis of the problem. The spectra of discrete signals were analysed, the influence of violations of the conditions of Kotelnikov's theorem and the asymptotics of the recovery errors of the original continuous signals were investigated. Methods of forming composite signals with arbitrary frequency difference of subcarriers have been developed. It is shown that when the amount of frequency shift of the basic series decomposition functions and the width of the spectrum of the main and side lobes in the spectrum of the output signal are matched, it is possible to count on the minimal influence of mutual interference and intersymbol interference of the composite signal. It was established that the method of orthogonal frequency multiplexing of channels (OFDM) allows to significantly increasing the bandwidth of the wireless communication system. In addition, with dynamic load changes in wireless networks, the impact of frequency collisions and an increase in the level of intra-system interference is minimized. Graphical analysis of the results of the series expansion on the quasiorthogonal system of basis functions shows that the errors of restoration of the original signals can reach unacceptable values. It was also established that when expanding into a non-orthogonal series with a larger number of members, the situation with the restoration of the original signals becomes a little more suitable, but no guarantees of monotonous improvement of the situation with restoration errors can be given in principle. This problem requires additional analytical research.

Nonreciprocal transmission of multi-band optical signals in thermal atomic systems

Multi-band signal propagation and processing play an important role in quantum communications and quantum computing. In recent years, optical nonreciprocal devices such as an optical isolator and circulator are proposed via various configurations of atoms, metamaterials, nonlinear waveguides, etc. In this work, we investigate all-optical controlled nonreciprocity of multi-band optical signals in thermal atomic systems. Via introducing multiple strong coupling fields, nonreciprocal propagation of the probe field can happen at some separated frequency bands, which results from combination of the electromagnetically induced transparency (EIT) effect and atomic thermal motion. In the proposed configuration, the frequency shift resulting from atomic thermal motion takes converse effect on the probe field in the two opposite directions. In this way, the probe field can propagate almost transparently within some frequency bands of EIT windows in the opposite direction of the coupling fields. However, it is well blocked within the considered frequency region in the same direction of the coupling fields because of destruction of the EIT. Such selectable optical nonreciprocity and isolation for discrete signals may be greatly useful in controlling signal transmission and realizing selective optical isolation functions.

Errors of recovery of signals in multi-channel communication systems with orthogonal modulation

The errors of restoration of original continuous signals in digital communication systems with orthogonal modulation of equidistant subcarriers (the so-called OFDM technology) are considered. The theory of radio engineering circuits and signals is applied as a scientific and technical basis of the problem. The spectra of discrete signals were analyzed, the influence of violations of the conditions of Kotelnikov's theorem and the asymptotics of the recovery errors of the original continuous signals were investigated. Methods of analyzing composite signals with arbitrary selection of the sampling frequency of spaced subcarriers have been developed, and quantitative estimates of the quality of signal recovery on the receiving side using Euclidean distances have been obtained. . It is shown that when the amount of frequency shift of the basic series decomposition functions and the width of the spectrum of the main and side lobes in the spectrum of the output signal are matched, it is possible to count on the minimal influence of mutual interference and intersymbol interference of the composite signal. It was established that the correct selection of the sampling frequency of OFDM signals allows increasing the accuracy of signal recovery on the receiving side and the overall efficiency of the wireless communication system. In addition, with a dynamic change in the number of members of the decomposition series in wireless networks, the influence of frequency collisions, Gibbs ripples, and the level of intersystem interference is reduced. Graphical analysis of the results of the series expansion on the quasi-orthogonal system of basis functions shows that the errors of restoration of the original signals can reach unacceptable values. It was also established that when expanding into a non-orthogonal series with a larger number of members, the situation with the restoration of the original signals becomes a little more suitable, but guarantees of improving the situation with recovery errors can be given only when combining the correct selection of the sampling frequency with the number of members of the series expansion. This problem requires additional research of an analytical and computational nature.

Methodology for assessing the influence of non­fluctuation interferences on the immunity of receiving discrete signals with multi­position phase manipulation

The work deals with the assessment of the impact of non-fluctuating interference on the interference immunity of the reception of discrete signals with multi-position phase manipulation. It was determined that the improvement of immunity under the influence of fluctuating interference and further scientific work on improving the efficiency of data transmission form a new, urgent scientific task to assess the impact of non-fluctuating interference on the immunity of TCM based on BFM signals. It was found that the main types of non-fluctuating interference that can affect the immunity of reception of discrete signals with multi-position phase manipulation are the following: harmonic interference, phase-manipulated interference, relayed interference, scannin interference, chaotic impulse interference, multiplicative interference. In order to reduce the impact on the immunity of TCM based on BFM signals, an appropriate methodology for assessing the impact of non-fluctuating interference on the immunity of reception of discrete signals with multi-position phase manipulation is proposed. The following procedure is proposed as the above methodology. Formation of TCM parameters of data transmission on the basis of BFM signals according to the functional purpose. Development of a TCM interference protection model for data transmission based on BFM signals under the influence of fluctuation interference, its implementation and evaluation of application effectiveness. Analysis of conditions and factors that can affect the formation of non-fluctuation disturbances on TCM based on BFM signals. Determination of the list of non-fluctuating interferences that can be formed under the influence of specified conditions and factors, in which the TCM of data transmission based on BFM signals functions. Evaluation of the impact of non-fluctuation interference on the effectiveness of TCM interference protection based on BFM signals. Development of recommendations for reducing the impact of non-fluctuational interference on the effectiveness of TCM interference protection based on BFM signals.

A Covariant, Discrete Time-Frequency Representation Tailored for Zero-Based Signal Detection

Recent work in time-frequency analysis proposed to switch the focus from the maxima of the spectrogram toward its zeros, which, for signals corrupted by Gaussian noise, form a random point pattern with a very stable structure leveraged by modern spatial statistics tools to perform component disentanglement and signal detection. The major bottlenecks of this approach are the discretization of the Short-Time Fourier Transform and the boundedness of the time-frequency observation window deteriorating the estimation of summary statistics of the zeros, on which signal processing procedures rely. To circumvent these limitations, we introduce the Kravchuk transform, a generalized time-frequency representation suited to discrete signals, providing a covariant and numerically tractable counterpart to a recently proposed discrete transform, with a compact phase space, particularly amenable to spatial statistics. Interesting properties of the Kravchuk transform are demonstrated, among which covariance under the action of SO(3) and invertibility. We further show that the point process of the zeros of the Kravchuk transform of white Gaussian noise coincides with those of the spherical Gaussian Analytic Function, implying its invariance under isometries of the sphere. Elaborating on this theorem, we develop a procedure for signal detection based on the spatial statistics of the zeros of the Kravchuk spectrogram, whose statistical power is assessed by intensive numerical simulations, and compares favorably to state-of-the-art zeros-based detection procedures. Furthermore it appears to be particularly robust to both low signal-to-noise ratio and small number of samples.

Hyperchaotic Impulsive Synchronization and Digital Secure Communication

Based on chaos shift keying approach, impulsive signals from Hyperchaotic Chen system and Hyperchaotic Lü system are alternately emitted according to the transmission of binary signals “0” and “1”. In the receiver, these two hyperchaotic systems are adopted as response systems at the same time. The digital signals are recovered via comparing the discrete signals of the two error systems. Numerical simulations show the effectiveness of the method.

The method of discretization signals to minimize the fallibility of information recovery

The paper proposes a fundamentally new approach to the formulation of the problem of optimizing the discretization interval (frequency). The well-known traditional methods of restoring an analog signal from its discrete implementations consist of sequentially solving two problems: restoring the output signal from a discrete signal at the output of a digital block and restoring the input signal of an analog block from its output signal. However, this approach leads to methodical fallibility caused by interpolation when solving the first problem and by regularizing the equation when solving the second problem. The aim of the work is to develop a method for the signal discretization to minimize the fallibility of information recovery to determine the optimal discretization frequency.The proposed method for determining the optimal discretization rate makes it possible to exclude both components of the methodological fallibility in recovering information about the input signal. This was achieved due to the fact that to solve the reconstruction problem, instead of the known equation, a relation is used that connects the input signal of the analog block with the output discrete signal of the digital block.The proposed relation is devoid of instabilities inherent in the well-known equation. Therefore, when solving it, neither interpolation nor regularization is required, which means that there are no components of the methodological fallibility caused by the indicated operations. In addition, the proposed ratio provides a joint consideration of the properties of the interference in the output signal of the digital block and the frequency properties of the transforming operator, which allows minimizing the fallibility in restoring the input signal of the analog block and determining the optimal discretization frequency.A widespread contradiction in the field of signal information recovery from its discrete values has been investigated. A decrease in the discretization frequency below the optimal one leads to an increase in the approximation fallibility and the loss of some information about the input signal of the analog-to-digital signal processing device. At the same time, unjustified overestimation of the discretization rate, complicating the technical implementation of the device, is not useful, since not only does it not increase the information about the input signal, but, if necessary, its restoration leads to its decrease due to the increase in the effect of noise in the output signal on the recovery accuracy. input signal. The proposed method for signal discretization based on the minimum information recovery fallibility to determine the optimal discretization rate allows us to solve this contradiction.

Median based algorithm for sub-pixel estimation of extrema positions of diffuse light reflection signal

It has been proposed to utilize the median algorithm for determination of the extrema positions of diffuse light reflectance intensity distribution by a discrete signal of a photodiode linear array. The algorithm formula has been deduced on the base of piecewise-linear interpolation for signal representation by cumulative function. It has been shown that this formula is much simpler for implementation than known centroid algorithm and the noise immune Blais and Rioux detector algorithm. Also, the methodical systematic errors for zero noise as well as the random errors for full common mode additive noises and uncorrelated noises have been estimated and compared for mentioned algorithms. In these terms, the proposed median algorithm is proportionate to Blais and Rioux algorithm and considerably better then centroid algorithm.

Reconstructing Signals from Aperture-Filtered Samples

Sampling plays a crucial role in remote sensing and signal analysis. In classic sampling theory, a signal is sampled at a uniform rate and at a minimum of twice the signal bandwidth. In practice, only finitely many samples of the signal are available and these can often only be obtained at irregular positions due to platform movement, scanning, pulsed operations, etc. In addition, often only local averages of the signal at these irregularly spaced locations can be measured, often with different apertures at different locations. We will use the one-to-one correspondence between the analog and discrete band-limited signal spaces established by the regular sampling theorem, and give a direct treatment of the discrete problem of irregular samplings. This problem is finite-dimensional and linear, easily accessible, and transparent. Its solution can be easily implemented and analyzed to solve engineering applications. We will see that—different from other approaches—only the number of samples, not their location or density, determine whether the signal can be recovered or not.

Visualizing the Potential Impairment of Polymyxin B to Central Nervous System Through MR Susceptibility-Weighted Imaging.

Polymyxin B (PMB) exert bactericidal effects on the cell wall of Gram-negative bacteria, leading to changes in the permeability of the cytoplasmic membrane and resulting in cell death, which is sensitive to the multi-resistant Gram-negative bacteria. However, the severe toxicity and adverse side effects largely hamper the clinical application of PMB. Although the molecular pathology of PMB neurotoxicity has been adequately studied at the cellular and molecular level. However, the impact of PMB on the physiological states of central nervous system in vivo may be quite different from that in vitro, which need to be further studied. Therefore, in the current study, the biocompatible ultra-uniform Fe3O4 nanoparticles were employed for noninvasively in vivo visualizing the potential impairment of PMB to the central nervous system. Systematic studies clearly reveal that the prepared Fe3O4 nanoparticles can serve as an appropriate magnetic resonance contrast agent with high transverse relaxivity and outstanding biosafety, which thus enables the following in vivo susceptibility-weighted imaging (SWI) studies on the PMB-treated mice models. As a result, it is first found that the blood-brain barrier (BBB) of mice may be impaired by successive PMB administration, displaying by the discrete punctate SWI signals distributed asymmetrically across brain regions in brain parenchyma. This result may pave a noninvasive approach for in-depth studies of PMB medication strategy, monitoring the BBB changes during PMB treatment, and even assessing the risk after PMB successive medication in multidrug-resistant Gram-negative bacterial infected patients from the perspective of medical imaging.

Tupi Nodes pilot: A successful 4D seismic case for Brazilian presalt reservoirs

The oil and gas industry has established 4D seismic as a key tool to maximize oil recovery and operational safety in siliciclastic and low- to medium-stiffness carbonate reservoirs. However, for the stiffer carbonate reservoirs of the Brazilian presalt, the value of 4D seismic is still under debate. Tupi Field has been the stage of a pioneering 4D seismic project to field test the time-lapse technique's ability in monitoring production and water-alternating-gas (WAG) injection in the Brazilian presalt. Ocean-bottom node (OBN) technology was applied for the first time in the ultra-deep waters of Santos Basin, leading to the Tupi Nodes pilot project. We started with feasibility studies to forecast the presalt carbonate time-lapse responses. The minerals that constitute these carbonate rocks have an incompressibility modulus that is generally twice as large as those of siliciclastic rocks. This translates into discrete 4D signals that require enhanced seismic acquisition and processing techniques to be correctly detected and mapped. Consequently, two OBN seismic acquisitions were carried out. Time-lapse processing included the application of top-of-the-line processing tools, such as interbed multiple attenuation. The resulting 4D amplitude images demonstrate good signal-to-noise ratio, supporting both static and dynamic interpretations that are compatible with injection and production histories. To unlock the potential of 4D quantitative interpretation and the future employment of 4D-assisted history-matching workflows, we conducted a 4D seismic inversion test. Acoustic impedance variations of about 1.5% are reliably distinguishable beyond the immediate vicinity of the wells. These 4D OBN seismic surveys and interpretations will assist in identifying oil-bypassed targets for infill wells and calibrating WAG cycles, increasing oil recovery. We anticipate that studies of the entire Brazilian presalt section will greatly benefit from the results and conclusions already reached for Tupi Field.

Wavelet transform-based cross-correlation in the time-delay estimation applications

The article discusses the application of wavelet analysis for the time-frequency time-delay estimation. The proposed algorithm is wavelet transform-based cross-correlation time delay estimation that applies discrete time wavelet transform to filter the input signal prior to computation of cross-correlation function. The distinguishing feature of the algorithm that it uses the variation of continuous wavelet transform to process the discrete signals instead of dyadic wavelet transform that is normally applied to the case. Another feature that the implication of convolution theorem is used to compute coefficients of the wavelet transform. This makes possible to omit redundant discrete Fourier transforms and significantly reduce the computational complexity. The principal applicability of the proposed method is shown in the course of a computational experiments with artificial and real-world signal. So the method demonstrated expected selectivity for the signals localized in the different frequency bands. The application of the method to practical case of pipeline leak detection was also successful. However, the study concluded that this method provides no specific advantages in comparison with the conventional one. In the future, alternative applications in biological signal processing will be considered.

An Application of the Z-Transform in Extracting the Density Regulation from the Periodic Output of an Age-Structured Population Model

The application of the Z-transform, a manipulation tool from the discrete signal processing (DSP) toolbox, on an ecological model was motivated by the mathematical similarities between an age-structured fish population model with a non linear density regulation and a linear time invariant (LTI) control system. Both models include a switching mechanism in regulating stock/signal throughput in accordance with a given density limitation/set value and both models can be expressed in terms of a negative feedback loop difference equations (Getz & Haight,1989; °Astr¨om & Murray, 2008). In the fish model, the switching mechanism is a density regulated stock-recruitment (SR) function which models the strategies implemented by the population in keeping the vulnerable egg-larvaejuvenile densities within an environmental limitation thereof (Subbey et al, 2014). A switching mechanism is also present in control engineering, for example, in the mechanism associated with cruise control in cars which keeps traveling speed close to a chosen set value midst varying weather and road conditions (Antsaklis and Gao, 2005). In both cases, the choosing of the control action and the tuning of its parameters requires careful consideration to avoid failures such as incorrectly timed switching actions in a control plant (see Kuphaldt (2019)) and errors in estimating total allowable catch (TAC) in the fishing industry (see Borlestean et al (2015), Skagen et al (2013) and Taboadai and R. Anadn (2016)). The Z-transform has proven itself useful in tuning LTI controlmodels for a desired control action (see Orfanidis, (2010) and Smith, (1999)) and it is on this account that its application was extended to the ecological model in pursuit of a more efficient way of estimating SR parameters to simulate an already existing output. It was however found that it could not be used for parameter tuning but rather for the extraction of the SR component hidden in the output together with components resulting from the age structure itself. Such an extraction can greatly assist in the mathematical identification of the SR, reducing the complexity of its choosing as there are many different types used in the fishing industry such as the classic Beverton-Holt model, the Ricker model and Shepherd model (Myers, 2001; Iles, 1994; Shepherd, 1982). It can also be used to monitor changes in the SR over time which can indicate the presence of strategy evolution (Apaloo et al, 2009; Br¨annstr¨om et al, 2013). In 1998 Schoombie and Getz investigated the latter by subjecting the Shepherd SR to strategy optimization with regards to a parameter associated with population interventions in regulating recruitment throughput and it is because of this versatility that the Shepherd SR is chosen for the intended extraction. In true control style, Simulink, a graphic environment for designing control simulations, is used to visualize the production of the output as well as the extraction of the SR from it. This paper showcases the versatility of the Z transform and the possibilities and unexpected finds when applied to similar systems designed to regulate signals or, in this case, recruitment densities.

Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset.

Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geographical information, remote sensing and photogrammetric approaches are useful tools for analyzing and managing wetlands. In this study, the water areas of five different wetlands obtained with Sentinel-2 images in Turkey were classified. Although obtaining large amounts of high-dimensional dataset labeled for various land types is costly, it is a significant advantage to use it after model training in a wide range of applications. In this paper, the EuroSAT dataset was used in the validation process. Proposed deep learning-based 1D convolutional neural networks (CNN) and traditional machine learning methods (i.e., support vector machine, linear discriminant analysis, K-nearest neighborhood, canonical correlation forests, and AdaBoost.M1) were compared quantitatively (i.e., accuracy, recall, precision, specificity, F-score, and image quality assessment metrics) and qualitatively. Finally, pairwise comparison was made with chi-square-based McNemar's test. There is a statistical difference between 1D CNN and machine learning method (except the support vector machine vs linear discriminant analysis in Test 1 area). CNN models outperform machine learning algorithms in terms of non-linear function approximation and the ability to extract and articulate data features. Since 1D CNNs can process data in a highly complex and unique feature space, they are very successful in segmenting strongly related and highly correlated discrete signals. It also has advantages over machine learning methods for water body extraction in that it can be integrated with sophisticated image pre-processing and standardization tools, is less susceptible to low-level random noise, and provides shift in variations and contrast-invariant image local transforms.

Approximate methods for solving amplitude-phase problem for discrete signals

Methods for solving amplitude and phase problems for one and two-dimensional discrete signals are proposed. Methods are based on using nonlinear singular integral equations. In the one-dimensional case amplitude and phase problems are modeled by corresponding linear and nonlinear singular integral equations. In the two-dimensional case amplitude and phase problems are modeled by corresponding linear and nonlinear bisingular integral equations. Several approaches are presented for modeling two-dimensional problems: 1) reduction of amplitude and phase problems to systems of linear and nonlinear singular integral equations; 2) using methods of the theory of functions of many complex variables, problems are reduced to linear and nonlinear bisingular integral equations. To solve the constructed nonlinear singular integral equations, methods of collocation and mechanical quadrature are used. These methods lead to systems of nonlinear algebraic equations, which are solved by the continuous method for solution of nonlinear operator equations. The choice of this method is due to the fact that it is stable against perturbations of coefficients in the right-hand side of the system of equations. In addition, the method is realizable even in cases where the Frechet and Gateaux derivatives degenerate at a finite number of steps in the iterative process. Some model examples have shown effectiveness of proposed methods and numerical algorithms.

Simultaneous optical transmission of pulse amplitude modulation and discrete multitone signals in passive optical link using derivative and subtractive equalization

A derivative and subtractive equalization (DSE), which simultaneously transmits both four-level pulse amplitude modulation (PAM-4) and discrete multitone (DMT) signals encoded by quadrature amplitude phase shift keying (QPSK) with the same bandwidth, is proposed. The proposed DSE recovers the DMT signal by tracking the change of derivative value between adjacent sampling points and then subtracting the recovered PAM-4 signal after derivative value tracking from the received signals. A 20-km optical link is implemented to experimentally verify the proposed DSE technique. The bit error rate (BER) of the PAM-4 signal (1 Gbaud) and the error vector magnitude (EVM) of QPSK signal (1 Gbaud) are measured in order to examine the difference in transmission performance before and after using the proposed DSE technique. The 20% EVM of QPSK signal and the 5.4 × 10 − 5 BER of PAM-4 signal is observed at the 25% power ratio of DMT signal to PAM-4 signal. It is found that the signal-to-noise ratio of input PAM-4 and DMT signal should be 25 and 40 dB, respectively, with the 7% forward error correction to successfully transmit them at the same time. The 4-dB power penalty is observed before and after using DSE technique. These experimental results tell us that the transmission capacity of 4 times the bandwidth can be achieved using the proposed DSE technique.

Adaptive Compressed Spectrum Sensing for Multiband Signals

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.