Full-duplex wireless communication is becoming an important research area because of its potential for increasing spectral efficiency. The challenge of such systems lies in cancelling the ...self-interference. In this paper, we focus on the design of digital cancellation schemes and use them to supplement RF/analog cancellation techniques. The performance of digital cancellation is limited by the non-ideal characteristics of different subsystems in the transceiver, such as analog/digital converter (ADC), power amplifier (PA), and phase noise. It is first shown that given the pre-cancellation achieved by existing RF/analog techniques, the effects of ADC, phase noise, and sampling jitter are not the bottleneck in the system. Instead, the performance of conventional digital cancellation approaches are mainly limited by nonlinearity of the PA and transmit I/Q imbalance. In addition, the output SINR of the desired signal is limited because the estimation precision of the self-interference channel is affected by the desired signal. To overcome these issues, we propose a two-stage iterative self-interference cancellation scheme based on the output signal of the power amplifier. Analytical and simulation results reveal that the proposed cancellation scheme substantially outperforms existing digital cancellation schemes for full-duplex wireless communication systems.
In this paper, a 2-D noncausal Markov model is proposed for passive digital image-splicing detection. Different from the traditional Markov model, the proposed approach models an image as a 2-D ...noncausal signal and captures the underlying dependencies between the current node and its neighbors. The model parameters are treated as the discriminative features to differentiate the spliced images from the natural ones. We apply the model in the block discrete cosine transformation domain and the discrete Meyer wavelet transform domain, and the cross-domain features are treated as the final discriminative features for classification. The support vector machine which is the most popular classifier used in the image-splicing detection is exploited in our paper for classification. To evaluate the performance of the proposed method, all the experiments are conducted on public image-splicing detection evaluation data sets, and the experimental results have shown that the proposed approach outperforms some state-of-the-art methods.
Stochastic Point Location problem considering that a learning entity (i.e. mechanisms, algorithm, etc) attempts to locate a certain point by interaction with a stochastic environment is encountered ...widely in Machine Learning. A conventional technique is to sample the search space into discrete points and perform a random walk. Nevertheless, the random walk is confined to the neighboring point. In this paper, an extended version of the random walk-based triple level algorithm is introduced to overcome the aforementioned defect. Specifically, the proposed algorithm exploits the multi-Markovian switching to generalize the random walk concerning adjacent nodes to intermittent nodes. Hence, the whole approach could be regarded as the Markov chain, and its transform matrix could be constructed, followed by a rigorous mathematical pf procedure of the convergence. The experimental results demonstrate the effectiveness and efficiency of the proposed algorithm, showing its abilities of stronger stability, a higher precision, and a faster speed in comparison with the counterparts available in open literatures.
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Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) ...is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly. Taking both PU's unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.
Studying fold-and-thrust belts is crucial in geoscience; fold-and-thrust belts are rich in petroleum and natural gas. However, the effects of friction properties and rheological structures on the ...deformation patterns and evolution of fold-and-thrust belts with two detachment layers are less frequently discussed. Therefore, using models with a basal silicone layer, a middle silicone layer and overlying quartz sand layers, this study conducted an analogue modelling analysis of the effects of friction conditions, rheological structures and compressional velocity on the deformation and evolution of fold-and-thrust belts. The results show the following. 1) The weak layer or low friction strength of the basement forms complex deformations of the structural style, forming forward thrust, backward thrust, symmetrical pop-up structure and frontward–backward oscillating structural characteristics. 2) The friction strength ratio between the lateral and basement layers does not determine the structural style of a fold-and-thrust belt; moreover, it is closely related to the construction of strata or rheological structures. 3) The deformation of double detachment layers complicates the structural style of fold-and-thrust belts. 4) The analogue modelling results are similar to the structural style of the simple fold belt of the Zagros fold-and-thrust belt (ZFTB), indicating that deep and middle detachment layers control the current deformation of the ZFTB.
•In this paper, the controlling factors of of the fold-and-thrust belt were analyzed by analogue modeling, and it shows that the basement friction strength has a very important influence on the structural deformation of the fold-and-thrust belt.•The structural pattern is not completely controlled by the ratio of lateral friction strength to basement friction strength, but is closely related to the boundary conditions and rheological structure.•The deformation characteristics of the Zagros fold -and- thrust belt is related to the simultaneous existence of the basal and intermediate detachment layers.
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Cooperative localization can improve both the availability and accuracy of positioning systems, and distributed belief propagation is a promising enabling technology. Difficulties with belief ...propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. This limits its application in practical systems with mobile nodes that have limited battery size and processing capabilities. In this paper, we propose an efficient cooperative localization algorithm that can be applied to a real indoor localization system with a nonGaussian ranging error distribution. We first propose an asymmetric double exponential ranging error model based on empirical ranging data. An efficient cooperative localization algorithm based on distributed belief propagation is then proposed. The communication and computational cost is reduced by passing approximate beliefs represented by Gaussian distributions between neighbours and by using an analytical approximation to compute peer-to-peer messages. An extension of the proposed algorithm is also proposed for tracking dynamic nodes. The proposed algorithms are validated on an indoor localization system deployed with 28 nodes covering 8000 m 2 , and are shown to outperform existing algorithms. In particular, the fraction of nodes located to one-meter accuracy is doubled using the proposed ranging error model and localization algorithm.
Learning automata (LA), a powerful tool for reinforcement learning in the field of machine learning, could explore its optimal state by continuously interacting with an external environment. ...Generally, the traditional LA algorithms, especially estimator LA algorithms, can be ultimately abstracted out as P- or Q-models, which are simply located in the stationary environments. A more comprehensive consideration would be S-model operating in the non-stationary environment. For this specific LA, presently the most popular achievement belongs to stochastic estimator LA (SELA). However, synchronously handing four parameters involved in SELA is an intractable job, as these parameters may vary dramatically in values under different environments, making it essential to develop a strategy for parameter tuning. In this paper, we first propose a scheme to determine the parameter searching scope and subsequently present a series of parameter searching methods, including a four-dimensional method and a two-dimensional method, making SELA applicable for any environment with switching non-stationary characteristics. Furthermore, to decrease the tuning cost, a reduced parameter SELA supported by the new two-dimensional parameter searching method emerges. And to break the traditional limit that the environmental reward probability must be symmetrically distributed, the S-model is constructed from a new perspective, thus forming a novel reduced parameter S-model of SELA (rpS-SELA). A detailed mathematical proof theoretically reveals the absolute expediency of rpS-SELA. In addition, it is demonstrated by experimental simulations that rpS-SELA outperforms others with a reduced tuning cost, a minor time consumption, a higher accuracy rate, and above all, a stronger tracking ability to the environmental switches.
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Recently, detecting the traces introduced by the content-preserving image manipulations has received a great deal of attention from forensic analyzers. It is well known that the median filter is a ...widely used nonlinear denoising operator. Therefore, the detection of median filtering is of important realistic significance in image forensics. In this letter, a novel local texture operator, named the second-order local ternary pattern (LTP), is proposed for median filtering detection. The proposed local texture operator encodes the local derivative direction variations by using a 3-valued coding function and is capable of effectively capturing the changes of local texture caused by median filtering. In addition, kernel principal component analysis (KPCA) is exploited to reduce the dimensionality of the proposed feature set, making the computational cost manageable. The experiment results have shown that the proposed scheme performs better than several state-of-the-art approaches investigated.
•We study price clustering for intraday Bitcoin prices of different time frames.•We find that open, high and low prices have different patterns of clustering.•We explore the relation of these ...patterns with time frame and price level.•The psychological barrier hypothesis plays a major role in our discussion.
This paper extends current literature on price clustering in Bitcoin market. We analyze intraday data of various time frames and document evidence of clustering for open, high and low prices. We discover and explain different patterns of clustering and their relation with the time frame. We further examine the effect of price level on these findings.
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In this paper, the mean first-passage time of a delayed tumor cell growth system driven by colored cross-correlated noises is investigated. Based on the Novikov theorem and the method of probability ...density approximation, the stationary probability density function is obtained. Then applying the fastest descent method, the analytical expression of the mean first-passage time is derived. Finally, effects of different kinds of delays and noise parameters on the mean first-passage time are discussed thoroughly. The results show that the time delay included in the random force, additive noise intensity and multiplicative noise intensity play a positive role in the disappearance of tumor cells. However, the time delay included in the determined force and the correlation time lead to the increase of tumor cells.
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