Filter Improvement Research Articles

Efficient Log Parsing and Visualization System for Large-Scale Logs: A Data-Centric Approach Using Custom Data Structures

In the realm of debugging and system optimization, testers frequently deal with large-scale logs from systems such as 5G communication networks. Handling, filtering, and visualizing these logs efficiently are crucial for understanding system performance and identifying issues. This paper presents an efficient log parsing and visualization system that leverages custom data structures to handle logs more effectively, with significant improvements in filtering and querying after initial parsing. The system provides multiple ways to filter logs, including by parent attributes, supports inline scripting to manipulate data tables, and offers various visualization techniques. The tool is optimized for fast filtering and querying, compared to other approaches, and will be available as an open-source project to the broader testing and debugging community.

A Clock-Phase Reuse Technique for Discrete-Time Bandpass Filters

In this article, we apply a new clock-phase reuse technique to a discrete-time infinite impulse response (IIR) complex-signaling bandpass filter (BPF). This leads to a deep improvement in filtering, especially the stopband rejection, while maintaining the area, sampling frequency, and the number of clock phases and their pulsewidths. Fabricated in 28-nm CMOS, the proposed BPF is highly tuneable and is capable of achieving a 70-dB stopband rejection at 50-MHz offset with 25% duty-cycle clocks while consuming 1.65 mW. The achieved in/out-of-band third-order intermodulation intercept point (IIP3) is +2.5 dB and +17.3 dBm, respectively, and the input-referred noise (IRN) is 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {nV}}/{\sqrt {\mathrm{ Hz}}}$ </tex-math></inline-formula> .

Discovering Image Manipulation History by Pairwise Relation and Forensics Tools

Given a potentially manipulated probe image, provenance analysis aims to find all images derived from the probe (offsprings) and all images from which the probe is derived (ancestors) in a large dataset (provenance filtering), and reconstruct the manipulation history with the retrieved images (provenance graph building). In this paper, we address two major challenges in provenance analysis, retrieving the source image of the small regions that are spliced into the probe image, and, detecting source images within the search results. For the former challenge, we propose to detect spliced regions by pairwise image comparison and only use local features extracted from the spliced region to perform an additional search. This removes the influence of the background and greatly improves the recall. For the latter, we propose to learn a pairwise ancestor-offspring detector and use it jointly with a holistic image manipulation detector to identify the source image. The proposed provenance analysis system has performed remarkably in evaluations using comprehensive provenance datasets. It's the winning solution for NIST Media Forensics Challenge (MFC) in 2018, 2019 and 2020. In MFC 2019, our provenance results achieved a 12% improvement in filtering and a 20% gain in oracle provenance graphs building over the alternative methods. In the real-world Reddit dataset, the edge overlap between our reconstructed provenance graphs and the ground-truth graphs is 5 times better than the state-of-the-art system.

EEG signal improvement with cascaded filter based on OWA operator

Noise reduction methods have a great impact on the performance of all EEG signal processing systems. The work involves the reduction in impulsive interferences. It is impossible to suppress effectively such a noise using linear filtering approach. The work presents the properties of a cascaded filter based on the ordered weighted aggregation (OWA) operator and its application to improve the electroencephalogram (EEG) signal. The OWA operator is a class of mean-like aggregation operators. By introducing a nonlinear sorting process and by assigning appropriate values of weights to the sorted samples, the improvement in filtering in the impulsive environment is achieved. The structure of the proposed cascade consists of two layers. The first layer contains two OWA filters that share a certain number of signal samples. The second layer averages the outputs of the first layer. The algorithm for the rise of filtering efficiency has been introduced. The performance of the new method has been experimentally compared with the traditional methods of impulsive noise reduction, using synthetic as well as real signals from the CHB-MIT database. The obtained results demonstrate that in the field of impulsive noise suppression and preprocessing, the proposed cascaded OWA filter brings about a significant improvement in the signal-to-noise ratio.

Measurement of high temperature full-field strain up to 2000 °C using digital image correlation

Understanding the deformation and strain at elevated temperature is a critical factor for the stability of aerodynamic shape, and an important consideration for the thermal protection system design. However, accurate measurement of deformation and strain at high temperatures is a challenge. Here, we present a measurement study for full-field strain mapping up to 2000 °C using digital image correlation (DIC) method, which mainly depends on the quality of speckle patterns on the specimen surface. In our study, the strain values are analyzed by DIC method while specimens are heated using a large electric current. Improvements in filtering and speckling allow the measured temperatures using this method to reach 2000 °C. We confirmed the validity of this method by comparison of measured Young’s modulus values with reference data for Inconel 718 Ni-based superalloy and graphite at different temperatures. Additionally, the full-field strain and Young’s modulus were demonstrated for a carbon fiber-reinforced carbon (C/C) composite uniaxial tensile specimen at 2000 °C.

High accuracy displacement measuring system using strain gauge based displacement sensor and direct sequence spread spectrum techniques in data acquisition system

The present article portrays a design and implementation of microcontroller based system for displacement measurement using strain gauge based displacement sensor. A displacement transducer may be capacitive or resistive, is an uncontrollable device. Improvement in filtering increases the speed of measurement and enhances the measurement accuracy. In this paper a new algorithm and architecture is proposed as filter for the displacement measuring system with strain gauge based displacement sensor. We verify and implement the novel algorithm with this displacement transducer in the measuring of displacement successfully.

Multi-Target Tracking Based on Multi-Bernoulli Filter with Amplitude for Unknown Clutter Rate.

Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice. In this paper, an improved multi-Bernoulli filter based on random finite sets for multi-target Bayesian tracking accommodating non-linear dynamic and measurement models, as well as unknown clutter rate, is proposed for radar sensors. The proposed filter incorporates the amplitude information into the state and measurement spaces to improve discrimination between actual targets and clutters, while adaptively generating the new-born object random finite sets using the measurements to eliminate reliance on prior random finite sets. A sequential Monte-Carlo implementation of the proposed filter is presented, and simulations are used to demonstrate the proposed filter’s improvements in estimation accuracy of the target number and corresponding multi-target states, as well as the clutter rate.

High accuracy mass measuring system using capacitive mass sensor

In the area of mass production, the products are weighted using load cell based weighting system. A load cell may be capacitive or resistive, is an uncontrollable weighing device. Improvement in filtering increases the speed of weighing and enhances the measurement accuracy. In this paper a new algorithm and architecture is proposed as weight filter for the weighing system with capacitive load cell. We verify and implement the novel algorithm in the measuring products successfully.

Characterization of Steady-State Current Ripple in Interleaved Power Converters Under Inductance Mismatches

Interleaved power converters are used in high-current applications due to their inherent reduction of semiconductors stress and total ripple. Ripple reduction is accomplished by a correct phase shifting, and the filtering improvement explained by the increase in the ripple frequency. However, these benefits are wasted, among other reasons, by the mismatch of the phase inductor value. As a consequence, differences in the ripple amplitude among phases are produced, leading to a total current ripple significantly greater than the ideal case, the loss of the cancellation points and the generation of the switching frequency component and its harmonics. The works dealing with this subject matter have focused on particular cases, such as a given number of phases, a specific converter topology, or a particular case of inductance mismatch, disregarding a general analytical approach. This paper proposes an analytical method to characterize the total ripple in steady state as a function of the duty cycle and the number of phases under any condition on inductance mismatch. Experimental results validate the proposed method.

Navigation algorithm for WSN mobile node on MH particle filtering improvement

A new approach is developed to localise and navigate Wireless Sensor Network (WSN) mobile node with Received Signal Strength Indicator (RSSI) signal, the approach is economical, convenient and reliable in noisy environment. An efficient Metropolis–Hasting (MH) particle filter algorithm was proposed to process the signal for ensures the monotonic increasing relationship between RSSI value and the distance between nodes. The coordinate space quantised with RSSI value was selected to describe the state and position of robots to avoid model error. The navigation system consists of several beacon nodes, and each of them is a distributed measurement and control unites. The outputs from every beacon nodes are collected by the navigation control centre, and the final outputs for mobile robots are calculated based on data fusion. Therefore, the real–time performance of this navigation system is enhanced. Furthermore, this system could adapt dynamic or unknown scenarios due to the coordinate of beacon node is not required before navigation. The simulation and experimental results show the effectiveness of this navigation algorithm.

Extending the physics studied by ECE on ITER

The Electron Cyclotron Emission (ECE) diagnostic provides essential information for plasma operation and for establishing performance characteristics in ITER. Recently, the design of the ITER ECE diagnostic has been taken through the conceptual design review and now entering the detailed design phase (1, 2). The baseline ECE system on ITER permits measurements of both the X- and O-mode radiation in the frequency range from 70 GHz up to 1 THz along two lines-of-sight, perpendicular and oblique at about 10 degrees, in the equatorial port. The system as planned meets the ITER measurement requirements. Nevertheless, there are several other mm-wave diagnostics in ITER, such as HFS, LFS and plasma position reflectometry, as well as Collective Thomson scattering system, whose transmission lines allow, in principle, additional measurements of parts of the ECE spectrum with upgrades of their back-ends, improvements in filtering and/or additional receivers. A discussion of whether and how supposedly to enable such ECE measurements is given here.

A decision theoretic approach to combining information filtering

PurposeThe purpose of this paper is to present an extension to a framework based on the information structure (IS) model for combining information filtering (IF) results. The main goal of the framework is to combine the results of the different IF systems so as to maximise the expected payoff (EP) to the user. In this paper we compare three different approaches to tuning the relevance thresholds of individual IF systems that are being combined in order to maximise the EP to the user. In the first approach we set the same threshold for each of the IF systems. In the second approach the threshold of each IF system is tuned independently to maximise its own EP (“local optimisation”). In the third approach the thresholds of the IF systems are jointly tuned to maximise the EP of the combined system (“global optimisation”).Design/methodology/approachAn empirical evaluation is conducted to examine the performance of each approach using two IF systems based on somewhat different filtering algorithms (TFIDF, OKAPI). Experiments are run using the TREC3, TREC6, and TREC7 test collections.FindingsThe experiments reveal that, as expected, the third approach always outperforms the first and the second, and that for some user profiles, the difference is significant. However, operational goals argue against global optimisation, and the costs of meeting these operational goals are discussed.Research limitations/implicationsOne limitation is the assumption of independence of the IF systems: in real life systems usually use similar algorithms, so dependency might occur. The approach also tends to be examined with the assumption of dependency between systems.Practical implicationsThe main practical implications of this study lie in the empirical proof that combination of filtering systems improves filtering results and the finding about the optimal combination methods for the different user profiles. Many filtering applications exist (e.g. spam filters, news personalisation systems, etc.) that can benefit from these findings.Originality/valueThe study presents and compares the contribution of three different combination methods of filtering systems to the improvement of filtering results It empirically shows the benefits of each method and draws important conclusions about the combination of filtering systems.