Set Of Filters Research Articles

SH2O: Efficient Data Access for Work-Sharing Databases

Interactive applications require processing tens to hundreds of concurrent analytical queries within tight time constraints. In such setups, where high concurrency causes contention, work-sharing databases are critical for improving scalability and for bounding the increase in response time. However, as such databases share data access using full scans and expensive shared filters, they suffer from a data-access bottleneck that jeopardizes interactivity. We present SH2O: a novel data-access operator that addresses the data-access bottleneck of work-sharing databases. SH2O is based on the idea that an access pattern based on judiciously selected multidimensional ranges can replace a set of shared filters. To exploit the idea in an efficient and scalable manner, SH2O uses a three-tier approach: i) it uses spatial indices to efficiently access the ranges without overfetching, ii) it uses an optimizer to choose which filters to replace such that it maximizes cost-benefit for index accesses, and iii) it exploits partitioning schemes and independently accesses each data partition to reduce the number of filters in the access pattern. Furthermore, we propose a tuning strategy that chooses a partitioning and indexing scheme that minimizes SH2O's cost for a target workload. Our evaluation shows a speedup of 1.8-22.2 for batches of hundreds of data-access-bound queries.

Multiple fault isolation method for micro thrusters of drag-free systems

This paper proposes a multiple fault isolation framework for micro thrusters of drag-free systems, based on the minimum number of isolation filters. Based on the consideration of fault isolatability, the original system is reasonably divided into two completely isolated subsystems, and then two sets of fault isolation filters are designed based on the principles of duality design for state feedback decoupling, which achieves residual decoupling, and therefore, can detect and isolate multiple faults occurred on the micro thrusters. In particular, this method has low computational burden (a feature of practical importance in reducing the usage of onboard resources) and short isolation time, making it suitable for on-board fast and precise fault isolation. The simulation results demonstrate the efficacy of the proposed method when utilized for a drag-free system with 12 field emission electric propulsion (FEEP) thrusters.

Effect of α-Al2O3 content on microstructures, mechanical properties and purification efficiency on molten steel of MgO-based filters

In this study, five sets of MgO-based ceramic filters were prepared using porous MgO powder and α-Al2O3 micro-powder as raw materials. The effect of α-Al2O3 content on the microstructures, mechanical properties and purification efficiency of MgO-based filters were investigated via SEM, EDS, and immersion test in molten steel, etc. The results indicate that the reactive sintering between α-Al2O3 and MgO promoted the formation and growth of neck connections of magnesium aluminate spinel between grains, increased the density of the filter skeleton, and decreased the pore size inside the skeleton. The higher density and smaller pore size of the skeleton, as well as the neck connection of spinel and crack deflection effect caused by magnesium aluminate spinel, comprehensively improved the strength and thermal shock resistance of MgO-based ceramic filters. Furthermore, the results from the immersion test in molten steel revealed that an appropriate amount of high-temperature liquid phase, smaller pore size, and in-situ generated spinel were beneficial for the filters to adsorb dissolved aluminum, dissolved oxygen, alumina inclusions, and secondary spinel inclusions in the steel. The filter with 20 wt.% α-Al2O3 had the best overall performance with a compressive strength of 2.35 MPa, a compressive strength after thermal shock test of 1.91 MPa and an efficiency of 77.3% in reducing the total oxygen content of the steel.

Extended Kalman filters for close-range navigation to noncooperative targets

This work presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations. All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behavior of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.

Filter-Hilbert method for automatic correction of non-anechoic antenna measurements with embedded self-calibration mechanism

One of the most important steps in the process of antenna development involves measurements of its prototype. Far-field performance of radiators is normally characterized in strictly controlled environments such as anechoic chambers which can ensure certification-grade accuracy. Unfortunately, they are also characterized by high construction costs which might not be justified for low-budget research and/or teaching-related activities. Alternatively, the radiation characteristics can be obtained in non-anechoic test sites which neglect the expensive components such as shielding and/or absorbing materials. Although the noise from external radiation sources and multi-path interferences renders direct measurements in such conditions useless for drawing conclusions on antenna performance, the quality of responses can be substantially increased using appropriate post-processing. Unfortunately, the existing techniques are difficult to set-up and prone to failure which makes them of limited use for day-to-day measurements. In this work, a correction framework for non-anechoic measurements based on a set of Hilbert filters automatically adapted to propagation conditions within the test-site has been proposed. The presented method has been validated based on three example antennas and a total of 25 experiments covering 15 unique frequencies of interest. A benchmark of the method against the state-of-the-art correction techniques has also been performed.

Electron Temperature Measurements Using a Two-Filter Soft X-ray Array in VEST.

A multichannel soft X-ray (SXR) array has been developed to measure the electron temperature in the Versatile Experiment Spherical Torus (VEST). To estimate electron temperature using the two-filter method applied to SXR intensity, we designed a pinhole camera that has two photodiode arrays with different metallic filters. We also adopted a filter wheel and tested various filter parameters to find the optimal filter set. Through tests, the combination of aluminum and beryllium was found to be the most suitable for the current experimental conditions in VEST. The filtered SXR signals were acquired with a low-noise preamplifier, exhibiting sufficient signal-to-noise ratios for electron temperature estimation based on the intensity ratio of two signals obtained with different filters. The estimated electron temperature from the developed two-filter SXR array showed reasonably matched levels and consistent trends with Thomson scattering measurements. Error contribution from impurity line emission is also discussed.

Design of performance-weighted blended mode filters for underwater experiments

Normal mode signals are valuable in ocean acoustic tomography and source localization, assuming that they can be separated using spatial or temporal filtering. Common spatial mode filters include the matched filter (MF) [Ferris, JASA, 1972] and the pseudo-inverse (PI) filter [Tindle et al., JASA, 1978]. Choosing a filter and its parameters, e.g., the PI filter rank, requires knowledge that is not readily available. For instance, it is unlikely that the levels of ambient noise or interference from other modes is known a priori. To address this problem, Chakrabarti and Wage [IEEE Oceans, 2021, 2022] developed a Performance-Weighted Blended mode filtering algorithm that adapts quickly to changing conditions and achieves better performance than a single fixed mode filter. The PWB filter blends a set of PI filters of varying ranks, including a rank-1 PI filter (MF). A PI filter can suppress interference from neighboring modes but is more vulnerable to noise than the MF, which has the maximum white noise gain. This talk explores practical aspects of the PWB implementation, including how to select the set of filters to blend given realistic underwater noise conditions. We will show experimental and simulated data to illustrate performance in different environments. [Work supported by ONR.]

Improving Nighttime Driving-Scene Segmentation via Dual Image-Adaptive Learnable Filters

Semantic segmentation on driving-scene images is vital for autonomous driving. Although encouraging performance has been achieved on daytime images, the performance on nighttime images are less satisfactory due to the insufficient exposure and the lack of labeled data. To address these issues, we present an add-on module called dual image-adaptive learnable filters (DIAL-Filters) to improve the semantic segmentation in nighttime driving conditions, aiming at exploiting the intrinsic features of driving-scene images under different illuminations. DIAL-Filters consist of two parts, including an image-adaptive processing module (IAPM) and a learnable guided filter (LGF). With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner. Specifically, the IAPM module consists of a small convolutional neural network with a set of differentiable image filters, where each image can be adaptively enhanced for better segmentation with respect to the different illuminations. The LGF is employed to enhance the output of segmentation network to get the final segmentation result. The DIAL-Filters are light-weight and efficient and they can be readily applied for both daytime and nighttime images. Our experiments show that DAIL-Filters can significantly improve the supervised segmentation performance on ACDC_Night and NightCity datasets, while it demonstrates the state-of-the-art performance on unsupervised nighttime semantic segmentation on Dark Zurich and Nighttime Driving testbeds. Codes and models are available at https://github.com/wenyyu/IA-Seg.

Inverse-Covariance-Intersection-Based Distributed Estimation and Application in Wireless Sensor Network

In a sensor network, estimation performance may degrade when unknown correlations among local estimates are not addressed carefully. This article presents a novel distributed estimation algorithm based on inverse covariance intersection (ICI) for effectively solving the cross-correlation and dynamic state estimation problems in a wireless sensor network. The intermediate results of a set of consensus filters running in parallel are utilized to realize a global fusion of estimates, which involves all agents' local estimates and improves the estimation accuracy. Meanwhile, the global consistency of the proposed algorithm can be guaranteed theoretically since the fusion process considers all local estimates jointly and in a unified way. Furthermore, besides the asymptotic performance, the boundedness and consistency of the fused estimate can be achieved under finite iterations, which demonstrates its robustness and potential in practical applications. A cooperative target tracking problem illustrates the performance of the proposed algorithm.

Spatial sampling versus acquisition time of room impulse responses for low-frequency sound zones

Sound zone methods aim to create multiple listening areas within a room, allowing independent playback of different audio content. Achieving this requires a loudspeaker array, multiple microphones per sound zone, and a set of control filters, which design involves the room impulse responses (RIRs) between loudspeakers and microphones. Under certain acoustic conditions, acquiring these RIRs is possible using very short acquisition times. At lower frequencies, longer wavelengths enable spatial sampling of the sound zones with fewer points, reducing the number of microphone positions required and amount of data to process. This study exploits this characteristic to render and evaluate low-frequency sound zones in two real rooms, with few spatial sampling points. We assess the performance by objectively evaluating multiple spatial sampling configurations and RIR acquisition times. Furthermore, we evaluate the system's ability to control sound beyond the sampled regions by assessing the sound field over wider areas. For sound zones roughly the size of a human head, satisfactory performance is achieved in low reverberation conditions with at least two microphones per ear, even for RIR acquisition times as short as 150 μs. Conversely, the performance degradation in highly reflective environments cannot be compensated using additional microphones nor longer acquisition times.

Voice matching in sung duos: Is it related to spectrum envelopes?

Choir singers report anecdotally that two voices can be perceived as a good match for each other, or not. Could “matching voices” be explained by the spectrum envelopes? Thirteen singers sang a duo in unison or canon with an adjacent prerecorded reference singer, in a moderately reverberant room. Singers controlled the stimulus timbre, using variable filters in medium and high frequency bands. They were asked to adjust the filters, while singing, for “best” and “worst” perceived matching. The singers then performed the song again, but with the filters automatically set to their chosen (dis-)preferences. The Self-to-Other ratio as a function of frequency [SOR(f)] at the ipsilateral ear of the participant was estimated from multiple microphone signals to predict separately the long-time average spectra of Self and Other. Most participants rated the sound with extreme filter settings at ±15 dB as the “worst” match, while “best” matches were fairly evenly distributed. Some but not all participants preferred the spectra to be complementary. However, at low frequencies, SOR(f) was about +10 dB, and very irregular but rarely negative at medium and high frequencies; so how an adjacent singer can be heard at all will require further investigation.

Remote Detection and Quantification of Methane Emissions Based on Hyperspectral Data Analysis

Measurement of methane emissions from leaks occurring on the territorially extensive network of transmission gas grid is a topical issue and highly desirable from the point of view of safety and reducing methane emissions into the atmosphere. Remote detection of methane is a problem whose technical solution is based on several types of optoelectronic devices, e.g. thermal imaging cameras with sets of optical filters, spectroradiometers, laser systems of the DIAL (DIfferential Absorption Lidar) type. On the other hand, the quantification of emission magnitudes is in most cases realized by spectoradiometric systems. This paper will present a method for analyzing hyperspectral data from an imaging Fourier infrared spectroradiometer. Measurements will be made on a purpose-built bench simulating methane emissions from a transmission network. Data obtained from ground level under different atmospheric conditions will be presented, together with the results of their analysis for different methane emissions.

Design and Manufacturing of Filter Sets for Dual-Color Synchronous Gene Sequencing

Gene sequencing is an important means for modern life sciences research and clinical diagnosis. In recent years, dual-color synchronous gene sequencing has developed rapidly due to its excellent properties such as high throughput, fast speed, miniaturization, and low cost. In this paper, a type of dual-color synchronous gene sequencing optical path structure, which is based on the fluorescence-detection principle, was designed. FAM and TAMRA were selected as the fluorophores, which corresponded to excitation light sources with 488 nm laser and 543 nm laser, respectively. Combining the optical system and fluorophores, we designed the parameters of the filter sets, including the details of design methods for dual-bandpass filters and dual-notch dichromic mirrors. Moreover, these filters were successfully manufactured by ion-assisted deposition and magnetron-sputtering methods with extreme precision. The satisfying application effect was demonstrated when these products were applied in customer-desktop medium-throughput gene sequencers.

FORECAST: A flexible software to forward model cosmological hydrodynamical simulations mimicking real observations

Context. Comparing theoretical predictions to real data is crucial to properly formulate galaxy formation theories. However, this is usually done naively considering the direct output of simulations and quantities inferred from observations, which can lead to severe inconsistencies. Aims. We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images, and thus providing a robust foundation for accurate comparison with observational data. With customizable options for filters, field-of-view size, and survey parameters, it allows users to tailor the synthetic images to their specific requirements. Methods. FORECAST constructs a light cone centered on the observer’s position exploiting the output snapshots of a simulation and computes the observed flux of each simulated stellar element, modeled as a single stellar population, in any chosen set of passband filters, including k correction, intergalactic medium absorption, and dust attenuation. These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. This allows simulated galaxies to be obtained with realistic morphologies and star formation histories. Results. As a first application, we present a set of images obtained exploiting the ILLUSTRISTNG simulation, emulating the GOODS-South field as observed for the CANDELS survey. We produced images of ~200 sq. arcmin, in 13 bands (eight Hubble Space Telescope optical and near-infrared bands from ACS B435 to WFC3 H160, the VLT HAWK-I Ks band, and the four IRAC filters from Spitzer), with depths consistent with the real data. We analyzed the images with the same processing pipeline adopted for real data in CANDELS and ASTRODEEP publications, and we compared the results against both the input data used to create the images and the real data, generally finding good agreement with both, with some interesting exceptions which we discuss. As part of this work, we have released the FORECAST code and two datasets. The first is the CANDELS dataset analyzed in this study, and the second dataset emulates the JWST CEERS survey images in ten filters (eight NIRCam and two MIRI) in a field of view of 200 sq. arcmin between z = 0–20. Conclusions. FORECAST is a flexible tool: it creates images that can then be processed and analyzed using standard photometric algorithms, allowing for a consistent comparison among observations and models, and for a direct estimation of the biases introduced by such techniques.

Modelling of Lake Waves to Simulate Environmental Disturbance to a Scale Ship Model

Abstract In the development of ship motion control systems, software simulations or scale model experiments in pools or open water are very often carried out in the verification and testing stages. This paper describes the process of building a software wave simulator based on data gathered on the Silm Lake near Iława, Poland, where scale ship models are used for research and training. The basis of the simulator structure is a set of shaping filters fed with Gaussian white noise. These filters are built in the form of transfer functions generating irregular wave signals for different input wind forces. To enable simulation of a wide range of wind speeds, nonlinear interpolation is used. The lake wave simulation method presented in this paper fills a gap in current research, and enables accurate modelling of characteristic environmental disturbances on a small lake for motion control experiments of scale model ships.

Use of a funneled sheath for embolic protection during removal of thrombosed Simon Nitinol filters.

Inferior vena cava (IVC) filters should be removed when no longer needed, given their association with complications such as thrombosis of the IVC and lower extremities, fracture, migration, and growth into adjacent structures. While this is generally straightforward in the setting of retrievable filters, permanent filters present more of a challenge. In fact, many operators will not attempt to do so for fear of intraprocedural complications, among them, filter fracture and fragment embolization. Despite this, leaving the filters in situ places patients at risk of the complications described above. Here, the authors illustrate a novel technique for retrieving permanent filters using a funneled sheath to protect against embolization.

Measuring the Temperature of Starspots from Multi-filter Photometry

Using simultaneous multi-filter observations during the transit of an exoplanet around a K dwarf star, we determine the temperature of a starspot through modeling the radius and position with wavelength-dependent spot contrasts. We model the spot using the starspot modeling program STarSPot (STSP), which uses the transiting companion as a knife-edge probe of the stellar surface. The contrast of the spot, i.e., the ratio of the integrated flux of a darker spot region to the star's photosphere, is calculated for a range of filters and spot temperatures. We demonstrate this technique using simulated data of HAT-P-11, a K dwarf (T eff = 4780 K) with well-modeled starspot properties for which we obtained simultaneous multi-filter transits using Las Cumbres Observatory's MuSCAT3 instrument on the 2m telescope at Haleakala Observatory, which allows for simultaneous, multi-filter, diffuser-assisted high-precision photometry. We determine the average (i.e., a combination of penumbra and umbra) spot temperature for HAT-P-11's spot complexes is 4500 K ± 100 K using this technique. We also find for our set of filters that comparing the SDSS and filters maximizes the signal difference caused by a large spot in the transit. Thus, this technique allows for the determination of the average spot temperature using only one spot occultation in transit and can provide simultaneous information on the spot temperature and spot properties.

TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction.

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.

Filter-Based Disturbance Observer and Adaptive Control for Euler–Lagrange Systems With Application to a Quadrotor UAV

In this paper, a filter-based control scheme is developed for Euler-Lagrange systems with unknown parameter and external disturbance. Two controllers are proposed under the filtering framework, namely the adaptive controller and the disturbance observer-based controller, respectively. A set of low-pass filters are employed in the control design to derive useful filter outputs, which can provide the design freedom for the parameter estimator or the disturbance observer. For the adaptive controller, a certainty equivalent adaptive law is designed in such way that dominates the cross term between the estimation error and the filtered regression matrix to achieve better performance compared with classical estimators. For the disturbance observer-based controller, a high-gain disturbance observer is constructed for the lumped uncertainty with an arbitrary order for users to determine. Experimental results are presented to verify the effectiveness of the proposed schemes on a quadrotor UAV in several cases.

Frequency-encoded two-photon excited fluorescence microscopy.

Two-photon excited fluorescence (2PEF) microscopy is the most popular non-linear imaging method of biomedical samples. State-of-the art 2PEF microscopes use multiple detectors and spectral filter sets to discriminate different fluorophores based on their distinct emission behavior (emission discrimination). One drawback of 2PEF is that fluorescence photons outside the filter transmission range are inherently lost, thereby reducing the imaging efficiency and speed. Furthermore, emission discrimination of different fluorophores may fail if their emission profiles are too similar. Here, we present an alternative 2PEF method that discriminates fluorophores based on their excitation spectra (excitation discrimination). For excitation we use two lasers of different wavelengths (ω1, ω2) resulting in excitation energies at 2ω1, 2ω2, and the mixing energy ω1+ω2. Both lasers are frequency encoded (FE) by an intensity modulation at distinct frequencies while all 2PEF emission is collected on a single detector. The signal is fed into a lock-in-amplifier and demodulated at various frequencies simultaneously. A customized nonnegative matrix factorization (NNMF) then generates fluorescence images that are free of cross talk. Combining FE-2PEF with multiple detectors has the potential to enable the simultaneous imaging of an unprecedented number of fluorophores.