Optimal Filter Research Articles

Fusion estimation against mixed network attacks for systems with random parameter matrices, correlated noises, and quantized measurements

This paper is concerned with the information fusion estimation problems for stochastic uncertain systems with quantized measurements and mixed network attacks including random deception attack and denial-of-service (DoS) attack. The cross-correlated random parameter matrices and addictive noises simultaneously exist in the studied system. By resorting to the optimal prediction compensation mechanism for DoS attack, an optimal centralized fusion filter in the linear minimum variance sense is proposed using an innovation analysis approach. In addition, the Kalman-like recursive distributed optimal linear fusion predictor and filter without feedback are presented based on local estimators from single-sensor subsystems. The estimation error cross-covariance matrices between two arbitrary local estimators, and those between local and prior fusion estimators are derived. They have good flexibility due to the parallel structure. However, they have lower accuracy than the centralized fusion estimators. To further improve the estimation accuracy, the distributed optimal linear fusion predictor and filter with feedback are also presented. They avoid the calculation of cross-covariance matrices. Moreover, it has been mathematically proved that they have the same estimation accuracy as the centralized fusion estimators. A simulation example demonstrates the effectiveness of the proposed algorithms.

Flexible fingerprint cuckoo filter for information retrieval optimization in distributed network

Flexible fingerprint cuckoo filter for information retrieval optimization in distributed network

A study on the characteristics and geometric optimization of a cyclone filter

Cyclone separator without filter is a common device used for separating solid–liquid/gas–solid/gas–liquid mixtures, which is widely used in chemical and household appliance industries. It has the advantages of high separation efficiency and low susceptibility to clogging. However, its separation efficiency cannot reach 100%, and some particles that enter the separator are not separated and will cause secondary pollution to downstream fluids. Combining the cyclone separator with a mesh filter to form a cyclone filter can achieve the complementary advantages of both, namely, high filtration efficiency and low susceptibility to clogging. For cyclone filters, particle shape has a significant impact on filter clogging, but there are few reports on this issue in the literature. In this paper, a new type of cyclone filter is proposed, and experimental and simulation methods are used to study its resistance characteristics. It is found that compared with the case of no filter, the flow resistance of the cyclone filter is significantly reduced, and the reason for the reduction in resistance is analyzed based on flow field analysis. The movement trajectory of flat and spherical particles in the cyclone filter and the problem of particle clogging on the filter are then studied. It is found that flat particles are more likely to cause clogging on the filter, and the reason for the clogging of the filter in the inlet area of the cyclone filter and the mechanism by which flat particles are more likely to clog the filter in the inlet area are analyzed based on the flow field and particle force in the separator. Finally, the inlet profile of the cyclone filter is optimized, and the changes in the inlet flow field and particle movement trajectory before and after optimization are compared. The problem of flat particle clogging on the filter is ultimately solved.

Single shot interferenceless coded aperture correlation holography via a learnable Wiener deconvolution network

One of the challenges in interferenceless coded aperture correlation holography (I-COACH) is high-quality single-shot reconstruction, as conventional cross-correlation reconstruction algorithm requires recording multiple holograms and complex reconstruction operations, resulting in serious background noise. Here, we present a deep learning based single-shot reconstruction by introducing a learnable Wiener deconvolution network in I-COACH. By feeding the point spread hologram (PSH) of I-COACH system and object hologram (OH) into Wiener deconvolution, a mapping relationship between Wiener deconvolution result and ground truth is generated. A Res-UNet is trained to learn optimal filter and noise regularization parameters, thereby achieving high quality reconstruction. Moreover, the training dataset used in our network are simulated using PSH that is obtained through only one experiment acquisition in advance, greatly simplifying experimental OH dataset acquisition. Compared to the conventional correlation reconstruction methods, the proposed Wiener deconvolution network can conveniently realize high-quality single-shot reconstruction in I-COACH, and the axial sensitivity in 3D reconstruction can be demonstrated by varying scattering degree of coded phase mask. Importantly, this Wiener deconvolution solution will provide an enlightening reference for dynamic imaging in I-COACH.

Oasis: An Optimal Disjoint Segmented Learned Range Filter

The learning-enhanced data structure has inspired the development of the range filter, bringing significantly better false positive rate (FPR) than traditional non-learned range filters. Its core idea is to employ piece-wise linear functions that uniformly map the entire key space into a bitmap sequentially. Nonetheless, such uniform mapping can be space-ineffective, impacting FPRs. This paper introduces Oasis, a novel learned range filter that divides the key space into disjointed intervals by excluding large empty ranges explicitly and optimally maps those unpruned intervals into a compressed bitmap. The configuration optimality in Oasis is guaranteed by a careful theoretical analysis. To enhance the versatility of Oasis, we further propose Oasis+, which integrates the design space of both learned and non-learned filters, delivering robust performance across a wide range of workloads. We evaluate the performance of both Oasis and Oasis+ when integrated into the key-value system RocksDB, using a diverse set of real-world and synthetic datasets and workloads. In RocksDB, Oasis and Oasis+ improve the performance by up to 1.4× and 6.2× when compared to state-of-the-art learned and non-learned range filters.

Measurement and Analysis of In Vivo Gastroduodenal Slow Wave Patterns Using Anatomically-Specific Cradles and Electrodes.

Bioelectrical 'slow waves' regulate gastrointestinal contractions. We aimed to confirm whether the pyloric sphincter demarcates slow waves in the intact stomach and duodenum. We developed and validated novel anatomically-specific electrode cradles and analysis techniques which enable high-resolution slow wave mapping across the in vivo gastroduodenal junction. Cradles housed flexible-printed-circuit and custom cradle-specific electrode arrays during acute porcine experiments (N = 9; 44.92 kg ± 8.49 kg) and maintained electrode contact with the gastroduodenal serosa. Simultaneous gastric and duodenal slow waves were filtered independently after determining suitable organ-specific filters. Validated algorithms calculated slow wave propagation patterns and quantitative descriptions. Butterworth filters, with cut-off frequencies (0.0167 - 2) Hz and (0.167 - 3.33) Hz, were optimal filters for gastric and intestinal slow wave signals, respectively. Antral slow waves had a frequency of (2.76 ± 0.37) cpm, velocity of (4.83 ± 0.21) mm·s-1, and amplitude of (1.13 ± 0.24) mV, before terminating at the quiescent pylorus that was (46.54 ± 5.73) mm wide. Duodenal slow waves had a frequency of (18.13 ± 0.56) cpm, velocity of (11.66 ± 1.36) mm·s-1, amplitude of (0.32 ± 0.03) mV, and originated from a pacemaker region (7.24 ± 4.70) mm distal to the quiescent zone. Novel engineering methods enable measurement of in vivo electrical activity across the gastroduodenal junction and provide qualitative and quantitative definitions of slow wave activity. The pylorus is a clinical target for a range of gastrointestinal motility disorders and this work may inform diagnostic and treatment practices.

Optimal filter assignment policy against link flooding attack

A Link Flooding (LFA) attack is a special type of Denial-of-Service (DoS) attack in which the attacker sends out a huge number of requests to exhaust the capacity of a link on the path the traffic comes to a server. As a result, user traffic cannot reach the server. As a result, DoS and degradation of Quality-of-Service (QoS) occur. Because the attack traffic does not go to the victim, protecting the legitimate traffic alone is hard for the victim. The victim can protect its legitimate traffic by using a special type of router called filter router (FR). An FR can receive server filters and apply them to block a link incident to it. An FR probabilistically appends its own IP address to packets it forwards, and the victim uses that information to discover the traffic topology. By analyzing traffic rates and paths, the victim identifies some links that may be congested. The victim needs to select some of these possible congested links (PCLs) and send a filter to the corresponding FR so that legitimate traffic avoids congested paths. In this paper, we formulate two optimization problems for blocking the least number of PCLs so that the legitimate traffic goes through a non-congested path. We consider the scenario where every user has at least one non-congested shortest path in the first problem. We extend the first problem to a scenario where there are some users whose shortest paths are all congested. We transform the original problem to the vertex separation problem to find the links to block. We use a custom-built Java multi-threaded simulator and conduct extensive simulations to support our solutions.

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis.

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn-kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.

Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station

The idea of electric vehicles (EVs) is relatively new in the transportation industry. EVs are becoming increasingly popular due to many advantages. Apart from various advantages, some drawbacks of these vehicles are also a concern. One of the concerns for these vehicles is the substantial power requirement to charge the batteries. During charging, EVs deteriorate the power quality by injecting harmonic current into the system. As poor power quality is undesirable, the mitigation of this harmonic current is essential. Therefore, in this study, an optimal design of a shunt active power filter (SAPF) based EV charging system is suggested. The optimal design of the SAPF ensures that the DC-link voltage is efficiently regulated. To test the performance of the suggested scheme, a simulation system is developed and tested in MATLAB Simulink. The results show that the developed optimal control can efficiently regulate the DC-link voltage. The results, pre and post-compensation, show that the total harmonic distortion in the system is reduced to 4.33 % from 26.72 % using the suggested approach. This is even better by 12.93%, 7.39%, 14.32% and 20.32% in comparison to particle swarm optimization, whale optimization algorithms, bee colony optimization and ant colony optimization used for comparison.

Design, optimization, and testing of ridge filters for proton FLASH radiotherapy at TRIUMF: The HEDGEHOG

TRIUMF has recently developed and implemented a workflow to design and fabricate application-specific ridge filters, which is a key upgrade to enable research using ultra-high (FLASH) dose rate beam delivery at its Proton Therapy Research Centre. These static ridge filters, called HEDGEHOGs, replace the rotating modulator wheels used to create a spread-out Bragg peak (SOBP) during conventional proton radiotherapy beam delivery, which are incompatible with the fast irradiation times of clinical doses at FLASH dose rates at TRIUMF. Initial beam commissioning of HEDGEHOGs demonstrated that they produce spread-out Bragg peaks matching their design parameters. Additional studies of some of the design parameters, including SOBP widths as well as different pin diameters and fabrication options (material, 3D printer technology) have now been completed. Based on those studies, the use of liquid medium printers (resin, jet) instead of filament printers is recommended. These studies also demonstrate that the lateral dose uniformity of the beam at 36.5 cm from the HEDGEHOG location is acceptable even with relatively large 7 mm base-diameter pins. The code base and workflow are made publicly available and can easily be adapted to other facilities. The feasibility of adapting the HEDGEHOG workflow to other beam lines or facilities was validated on a second proton beam line at the same facility at which the HEDGEHOG workflow was developed.

Design of fractional-order transitional filters of the Butterworth-Sync-Tuned, Butterworth-Chebyshev, and Chebyshev-Sync-Tuned types: optimization, simulation, and experimental verification

This paper presents the optimal and generalized design of three different fractional-order (FO) transitional filters for the first time in the literature. The transitional filters considered are of the FO Butterworth-sync-tuned, the FO Butterworth-Chebyshev, and the FO Chebyshev-sync-tuned types. A metaheuristic swarm intelligence optimizer, namely the Crow Search Algorithm (CSA), helps to achieve the optimal FO filter model that minimizes the magnitude error with the theoretical function. The accuracy of the proposed approximants is examined for 19 different combinations of orders of the constituent filters for each of the three types of FO transitional filters. Comparisons with the modified stability boundary locus-based second-, third-, and fourth-order filter approximants demonstrate the compactness and superior accuracy of the proposed models. The average performance regarding the approximation accuracy, computational time, and convergence of CSA for solving the proposed filter design problems is investigated. Circuit simulations conducted on the OrCAD PSPICE platform for the proposed filter using the current feedback operational amplifier as an active element highlight good matching between the proposed model and theoretical filter function. Experimental validation is also carried out to justify the practical feasibility of the proposed filter with printed circuit board fabricated FO capacitor emulators.

Sample-Constrained Black Box Optimization for Audio Personalization

We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter h*, which applied to any music or speech, will maximize the user’s satisfaction. This is a black-box optimization problem since the user’s satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter hi, and query the user for their satisfaction scores f(hi). A family of “surrogate” functions is then designed to fit these scores and the optimization method gradually refines these functions to arrive at the filter ˆh* that maximizes satisfaction. In certain applications, we observe that a second type of querying is possible where users can tell us the individual elements h*[j] of the optimal filter h*. Consider an analogy from cooking where the goal is to cook a recipe that maximizes user satisfaction. A user can be asked to score various cooked recipes (e.g., tofu fried rice) or to score individual ingredients (say, salt, sugar, rice, chicken, etc.). Given a budget of B queries, where a query can be of either type, our goal is to find the recipe that will maximize this user’s satisfaction. Our proposal builds on Sparse Gaussian Process Regression (GPR) and shows how a hybrid approach can outperform any one type of querying. Our results are validated through simulations and real world experiments, where volunteers gave feedback on music/speech audio and were able to achieve high satisfaction levels. We believe this idea of hybrid querying opens new problems in black-box optimization and solutions can benefit other applications beyond audio personalization.

The first cut is the cheapest: optimizing Athena/X-IFU-like TES detectors resolution by filter truncation

The X-ray Integral Field Unit (X-IFU) instrument on the future ESA mission Athena X-ray Observatory is a cryogenic micro-calorimeter array of Transition Edge Sensor (TES) detectors designed to provide spatially-resolved high-resolution spectroscopy. The onboard reconstruction software provides energy, spatial location and arrival time of incoming X-ray photons hitting the detector. A new processing algorithm based on a truncation of the classical optimal filter and called 0-padding, has been recently proposed aiming to reduce the computational cost without compromising energy resolution. Initial tests with simple synthetic data displayed promising results. This study explores the slightly better performance of the 0-padding filter and assess its final application to real data. The goal is to examine the larger sensitivity to instrumental conditions that was previously observed during the analysis of the simulations. This 0-padding technique is thoroughly tested using more realistic simulations and real data acquired from NASA and NIST laboratories employing X-IFU-like TES detectors. Different fitting methods are applied to the data, and a comparative analysis is performed to assess the energy resolution values obtained from these fittings. The 0-padding filter achieves energy resolutions as good as those obtained with standard filters, even with those of larger lengths, across different line complexes and instrumental conditions. This method proves to be useful for energy reconstruction of X-ray photons detected by the TES detectors provided proper corrections for baseline drift and jitter effects are applied. The finding is highly promising especially for onboard processing, offering efficiency in computational resources and facilitating the analysis of sources with higher count rates at high resolution.

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

AbstractThis article is concerned with the optimal sequential fusion filter design issue for a class of sampled‐data systems subject to random transmission delays and multiplicative noise. The considered delay is modeled as a multistate Markov chain, while the multiplicative noise corrupting the observation measurements is assumed be a white noise sequence with known statistical properties. First, by reorganizing the original measurements, the solvability of the addressed problem is cast into the feasibility of an optimal estimation issue for the hybrid Markovian jump system without delays. Then, an optimal sequential fusion filter is constructed by utilizing a dynamic model with finite jumps on basis of the arrival order of measurements from different sensors, guaranteeing estimation performances both at sampling instants and within the sampling intervals. Moreover, the desired estimator gains can be computed via solving a series of coupled differential Riccati equations with jumps and coupled differential Lyapunov equations. Further, a stationary sequential fusion filter is achieved with the guarantee of the exponentially mean‐square stability. Finally, two simulation examples are provided to verify the validity of the proposed algorithms.

Optimization of plasmonic nanofluid filters for spectral splitting photovoltaic/thermal systems

There remains a scarcity of studies on the utilization and optimization of core–shell plasmonic nanofluid filter although it shows potential in solar energy exploitation. To fill this research gap, this paper introduces an optimization methodology founded on merit function (MF) to optimize the Au@SiO2-based nanofluid filters for enhancing the photovoltaic/photothermal (PV/T) system performance. Initially, the MF model of the nanofluid filtered-PV/T system was constructed based on a reliable optical model and validated by referenced experimental results. Using these models, the effect of nanofluid parameters on the system performance and the mechanism of optimization procedure were investigated. Results indicated that modifying SiO2 shell thickness is a cost-effective means of tailoring the transmittance of the nanofluids compared to adjusting the Au core diameter. The optimization mechanism can be summarized as enhancing electrical efficiency or improving thermal efficiency to offset electricity losses. Furthermore, the MF of the Au@SiO2/EG nanofluid increased from 1.3737 to 1.3844 with optical thickness decreasing from 44 mm to 40 mm by mixing with Ag@SiO2 nanoparticles, indicating the cost-effectivity of blended nanofluid. The MF of the optimized blended nanofluids are 0.007–0.084 higher than those of the referenced nanofluids. This work offers a feasible pathway toward achieving high-performance nanofluid optical filters.

Simple Moment Generating Function Optimisation Technique to Design Optimum Electronic Filter for Underwater Wireless Optical Communication Receiver

This paper introduces a new simple moment-generating function (MGF) design modelling method to conclude an optimum filter to maximize the Q-factor and increase the link communication span. This approach mitigates the pulse temporal dispersion, particularly the underwater wireless optical communication (UWOC) systems. Hence, some form of equalizing filter design is highly desirable. The model solution environment includes a Double Gamma Function (DGF) water channel impulse response, intersymbol interference (ISI), stochastic Poisson process, and additive Gaussian thermal noise (AGTN). The optimal filters exhibit temporal profiles comparable to those derived by published works based on complex Chernoff Bound (CB) and Modified Chernoff Bound (MCB) methods. The results show the impact of the optimum filter at a signal level and optical receiver level utilizing Eye-Diagrams and BER vs. Q-Factor, respectively. The computation involves four different UWOC propagation channel models for Coastal and Harbor waters. One of the main conclusions indicates that the optimum filter manages the temporal dispersion due to the ISI impairment correctly. Also, the proposed optimum filter reduces eye-opening and the corresponding Q-Factor by less than 15% for a five-times increase in pulse width for the same transmitted optical power level.

Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

AbstractThe autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

Adaptive minimum noise amplitude deconvolution and its application for early fault diagnosis of rolling bearings

To address the challenge of detecting early faults in rolling bearings, where weak fault features are occasionally obscured by background noise, an innovative early fault diagnosis method based on adaptive minimum noise amplitude deconvolution (MNAD) is introduced. Initially, a correlation Gini index function is defined to estimate the fault period, while leveraging the iterative advantages of MNAD to progressively approach the true fault period. This resolution effectively overcomes the requirement of prior knowledge regarding the fault period, thus reducing the number of input parameters. Subsequently, a composite index, combining square envelope Gini index and square envelope entropy, is constructed as the fitness function. The sand cat swarm optimization algorithm is employed to adaptively determine the optimal noise ratio and filter length for deconvolution, ensuring the acquisition of the finest filtered signal. Ultimately, envelope spectrum analysis is conducted on the filtered signal to extract fault features and facilitate early fault diagnosis. The effectiveness of the proposed method is validated through simulated and experimental data, highlighting its superior feature extraction capabilities and robustness compared to original MNAD and other sophisticated blind deconvolution methods.

Grafite: Taming Adversarial Queries with Optimal Range Filters

Range filters allow checking whether a query range intersects a given set of keys with a chance of returning a false positive answer, thus generalising the functionality of Bloom filters from point to range queries. Existing practical range filters have addressed this problem heuristically, resulting in high false positive rates and query times when dealing with adversarial inputs, such as in the common scenario where queries are correlated with the keys. We introduce Grafite, a novel range filter that solves these issues with a simple design and clear theoretical guarantees that hold regardless of the input data and query distribution: given a fixed space budget of B bits per key, the query time is O(1), and the false positive probability is upper bounded by l/2B-2, where l is the query range size. Our experimental evaluation shows that Grafite is the only range filter to date to achieve robust and predictable false positive rates across all combinations of datasets, query workloads, and range sizes, while providing faster queries and construction times, and dominating all competitors in the case of correlated queries. As a further contribution, we introduce a very simple heuristic range filter whose performance on uncorrelated queries is very close to or better than the one achieved by the best heuristic range filters proposed in the literature so far.

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.