Dual Filtering Research Articles

Nonlinear Transceiver Design for Full-Duplex MIMO Relaying with Direct Link

This paper investigates the joint transceiver design for full-duplex (FD) multiple-input multiple-output relay systems where the direct link (DL) and nonlinear successive-interference-cancellation (SIC) detection are considered. The objective is to jointly optimize the source precoder, relay precoder, and receiver such that the resultant symbol-vector error rate (SVER) is minimized. The joint design is challenging since the coexistence of DL and FD relaying will introduce the inter symbol-vector interference. To address this problem, we first propose a novel QR-SIC receiver structure by performing the dual-filtering (DuF) and channel stacking before SIC. Then, the source and relay precoders are optimized for the proposed DuF-based QR-SIC receiver. Compared with the source precoder, optimizing the relay precoder is much more involved due to the complicated objective function. Hence, we transform the original optimization problem into a trace-minimization one, enabling a closed-form solution for the relay precoder. Furthermore, we theoretically show that the proposed design framework can be extended to the system with the DuF-based minimum mean-squared-error SIC receiver. Simulations show that our transceivers are indeed able to significantly improve the overall SVER performance.

Depth- averaged large eddy simulation of shallow turbulent mixing layer

This paper presents the DA-LES large-scale simulation model of turbulent shallow flows, which is based on dual filtering, the first filter removes vertical scales and the second filtering, as a filter spatial, removes all scales from the movement of sizes smaller than the mesh size. To validate this model, we consider the mixing layer problem in a shallow flow of two different velocities imposed on the input Uijttewaal, Booij,Van Prooijen and Uijttewaal [1,2]. In this work, we consider the finite volume method based on an unstructured mesh for turbulent equations, the numerical results are presented and compared to experimental data.

Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks

Defenses against adversarial attacks are essential to ensure the reliability of machine-learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. We proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through anomaly (outlier) detectors before making the final decision. Experimental results (using benchmark data-sets) demonstrated that our dual-filtering strategy could mitigate adaptive or advanced adversarial manipulations for wide-range of ML attacks with higher accuracy. Moreover, the output decision boundary inspection with a classification technique automatically affirms the reliability and increases the trustworthiness of any ML-based decision support system. Unlike other defense techniques, our dual-filtering strategy does not require adversarial sample generation and updating the decision boundary for detection, makes the ML defense robust to adaptive attacks.

Detection algorithm of defects on polyethylene gas pipe using image recognition

Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted into an optimized support vector machine for defect recognition. The experimental results show that the detection algorithm in this paper can effectively improve the observability of the image. Through the trained support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition rate of pipeline image defects reached 96.3%.

双重滤波扫频光源的研制

双重滤波扫频光源的研制

Cosine apodization of dual-resonance all-fiber acousto-optic tunable filters.

We experimentally demonstrate a novel cosine apodization technique for dual-resonance all-fiber acoustic-optic tunable filter. The technique is based on a hybrid control of input acoustic polarization state and circumferential fiber twist. We will show that intrinsic sidelobe spectra occurring between dual filtering bands are successfully suppressed through our approach, which will be also theoretically confirmed via our analytical and numerical studies. The results illustrate that the spectral positions of each resonance are tuned linearly and continuously by the fiber twist, and that overall sidelobe spectra between two resonances are suppressed regardless of fiber twist angle. The proposed scheme is useful to minimize cross talk between adjacent wavelength channels in optical sensor systems. We highlight that our approach is directly applicable to low-noise matched filtering.

Dual filtering in operational and joint spaces for reaching and grasping.

To study human movement generation, as well as to develop efficient control algorithms for humanoid or dexterous manipulation robots, overcoming the limits and drawbacks of inverse-kinematics-based methods is needed. Adequate methods must deal with high dimensionality, uncertainty, and must perform in real time (constraints shared by robots and humans). This paper introduces a Bayesian filtering method, hierarchically applied in the operational and joint spaces to break down the complexity of the problem. The method is validated in simulation on a robotic arm in a cluttered environment, with up to 51 degrees of freedom.

Reliable and Efficient Approach of BOLD Signal with Dual Kalman Filtering

By introducing the conflicting effects of dynamic changes in blood flow, volume, and blood oxygenation, Balloon model provides a biomechanical compelling interpretation of the BOLD signal. In order to obtain optimal estimates for both the states and parameters involved in this model, a joint filtering (estimate) method has been widely used. However, it is flawed in several aspects (i) Correlation or interaction between the states and parameters is incorporated despite its nonexistence in biophysical reality. (ii) A joint representation for states and parameters necessarily means the large dimension of state space and will in turn lead to huge numerical cost in implementation. Given this knowledge, a dual filtering approach is proposed and demonstrated in this paper as a highly competent alternative, which can not only provide more reliable estimates, but also in a more efficient way. The two approaches in our discussion will be based on unscented Kalman filter, which has become the algorithm of choice in numerous nonlinear estimation and machine learning applications.

相移光栅及取样光栅的双峰滤波特性

相移光栅及取样光栅的双峰滤波特性

Dual Nonlinear Filters and Entropy Production

This paper makes connections between nonlinear filtering and the entropic properties of Markov processes. It starts by developing information flow models for continuous-time, discrete-state filtering problems, identifying rates of information supply and dissipation. Time reversal yields a dual filtering problem in which these flows are interchanged. The dual problem comprises a diffusion signal with nonlinear dynamics, and observations of the point process variety, but yields a finite-dimensional nonlinear filter. The paper goes on to define an entropic time derivative for a general class of Markov processes and relates the entropic derivatives of the signal and filter to the rates of information supply and dissipation. This leads to the definition of a rate of interactive entropy production, which measures the time asymmetry of the interaction between the signal and filter. This asymmetry is of the same nature as that occurring in the theory of nonequilibrium statistical mechanics based on stochastic dynamics. In this context, the interaction between the signal and filter is nondissipative—a property intimately connected with the existence of a dual problem.