Proposed Filtering System Research Articles

An adaptive delay-compensated filtering system and the application to path following control for unmanned surface vehicles

Given that the internal states of a system, such as position, velocity, acceleration, and other important factors, naturally obey the integral processes of a physical system in kinematics, this paper presents an adaptive noise filtering system that can reconstruct these system states at the kinematic level. This is done without using any prior knowledge of the statistical properties of measurement noises. In the proposed filtering system here, each noise-contaminated estimated state is filtered by an average filter to compensate for phase delay and amplitude distortion. Unlike existing model-based estimation methods, the dynamic equation is not explicitly used in the proposed method, and the uncertainties in the nonlinear dynamic equation can be isolated. Furthermore, this application is much more straightforward as there are no gains to be processed. To verify our proposed adaptive filtering system, it has been applied to a variable speed path-following control task for unmanned surface vehicles (USVs), where accurate system states must be known. In particular, this paper also proposes a state-constrained finite-time control framework to realize the path-following control objectives. The proposed controller here mainly consists of two parts, i.e., an online state-constrained polynomial planning function and an execution of an algebraic control law. Simulations and experiments have been conducted to validate the effectiveness and reliability of the proposed filtering system and the finite-time controller. The results show that the proposed filtering system considerably outperformed several of conventional observers such as the extended Kalman filter (EKF), the passive observer, as well as the high-order differentiator.

Non-Destructive Acoustical Rock Bolt Testing System with Intelligent Filtering in Salt Mine ‘Wieliczka’

This article presents the application of a self-excited acoustic SAS system for non-destructive testing (NDT) for roof-bolt housings in laboratory and real mine conditions. The proposed system with a filtering mechanism was applied to the J64-27 composite anchors. The conducted tests allowed successful confirmation of the usefulness of the system in the detection of rod defects, damage of the mechanism coupling the anchor to the rock mass and testing of the stress state of the anchor itself. The proposed filtering system allowed eliminating the effect of jump change of frequency in the limit cycle of self-excited system. The proposed method is a novel solution for safety diagnostics of bolt housings in mining applications.

A New Filtering System for Using a Consumer Depth Camera at Close Range.

Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which is an obstacle for real-time applications that do not rely on point cloud post-processing. In order to fill this gap, by analyzing the noise region based on position and shape, we proposed a composite filtering system for using consumer depth cameras at close range. The system consists of three main modules that are used to eliminate different types of noise areas. Taking the human hand depth image as an example, the proposed filtering system can eliminate most of the noise areas. All algorithms in the system are not based on window smoothing and are accelerated by the GPU. By using Kinect v2 and SR300, a large number of contrast experiments show that the system can get good results and has extremely high real-time performance, which can be used as a pre-step for real-time human-computer interaction, real-time 3D reconstruction, and further filtering.

The Comparison of Dry And Wet Capture (Washing With Drinking Water) to The Soot Particles Emissions From The Diesel Engine

The aim of the research is to reduce the emission of the soot patticles from diesel power generators through the design of the After Treatment system and to compare the dry and wet filtering process within the proposed filtering system. The filtration process is based on a new design of HydroCyclonic, Each process has the efficiency of capturing the soot particles for the maximum loading rate of the diesel engine from soot (1200 mg / m3) , for dry filteration achieved the efficiency of the capture of soot particles by about 8% using cyclone while the efficiency of soot capture within the wet filteration process was increased by washing with drinking water (mist) between [35 - 90%] for a loading rate of [0.025 - 0.6 L / m3], respectively.

A personalized mail re-filtering system based on the client

Email is an essential communication tool, but a large number of spam emails can seriously affect the work and life of users and can even cause property damage. Due to different interests and hobbies, there may be huge differences in the definition of spam by users; the realization of personalized spam filtering has become an important issue in the field of spam filtering. When emails are misjudged, the user has to manually modify it, which brings great inconvenience to the user experience. In order to effectively solve the above problems and realize the functions of personalized email filtering and automatic correction of mis-filtered emails, this paper combined with rules and statistical methods presents a personalized email re-filtering system based on the client (PRFC) and implements the automatic modification of the mis-filtered emails. A large part of existing spam filters do not consider the difference between class prior probability and class imbalance problem; they only filter the mail online. Firstly, the proposed filter system processes the mails entering the inbox and the garbage and then designs two mutually learned filters based on the multi-task learning principle to be used for the automatic modification of the mis-filtered emails in inbox and garbage. To ensure the performance of the filter based on the interests of users and data distribution of mails varying with time, a multi-window learning framework that combines important weights to effectively implement the dynamic adaptation of the filter was designed. Finally, our proposed filtering system on the TREC 2006c and 2007p data sets that gets a significant filtering efficiency was verified.

Convergent application for trace elimination of dynamic objects from accumulated lidar point clouds

In this paper, a convergent multimedia application for filtering traces of dynamic objects from accumulated point cloud data is presented. First, a fast ground segmentation algorithm is designed by dividing each frame data item into small groups. Each group is a vertical line limited by two points. The first point is orthogonally projected from a sensor’s position to the ground. The second one is a point in the outermost data circle. Two voxel maps are employed to save information on the previous and current frames. The position and occupancy status of each voxel are considered for detecting the voxels containing past data of moving objects. To increase detection accuracy, the trace data are sought in only the nonground group. Typically, verifying the intersection between the line segment and voxel is repeated numerous times, which is time-consuming. To increase the speed, a method is proposed that relies on the three-dimensional Bresenham’s line algorithm. Experiments were conducted, and the results showed the effectiveness of the proposed filtering system. In both static and moving sensors, the system immediately eliminated trace data and maintained other static data, while operating three times faster than the sensor rate.

Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.

A Highly Efficient Content Based Approach to Filter Pornography Websites

With the ever-growing Web, the Websites with objectionable contents like pornography, violence, racism, etc. have been augmented rapidly during recent years. Among the offensive contents, the pornography is the most harmful one affecting children safety and causing many destructive side effects. A content filter is one or more pieces of software that work together to prevent users from viewing material found on the Internet. This paper presents an efficient content based software system for detecting and filtering pornography images in Web pages. The proposed system runs online in the background of Internet Explorer (IE) for the purpose of restricting access to pornography Web pages. Skin and face detection techniques are the main components of the proposed system. Because the proposed filter works online, the authors propose two fasting techniques that can be used to speed up the filtering system. The results obtained using the proposed system is compared with four commercial filtering programs. The success rate of the proposed filtering system is better than the considered filtering programs.

A Serial-Parallel Panoramic Filter Bank as a Model of Frequency Decomposition of Complex Sounds in the Human Inner Ear

We consider that the outer hair cells of the inner ear together with the local structures of the basilar membrane, reticular lamina and tectorial membrane form the primary filters (PF) of the second order. Taking into account a delay in transmission of the excitation signal in the cochlea and the influence of the Reissner membrane, we design a signal filtering system consisting of the PF with the common PF of the neighboring channels. We assess the distribution of the central frequencies of the channels along the cochlea, optimal number of the PF constituting a channel, natural frequencies of the channels, damping factors and summation weights of the outputs of the PF. As an example, we present a filter bank comprising 20 Gaussian-type channels each consisting of five PF. The proposed filtering system can be useful for designing cochlear implants based on biological principles of signal processing in the cochlea.

A scalable intelligent non-content-based spam-filtering framework

Designing a spam-filtering system that can run efficiently on heavily burdened servers is particularly important to the widely used email service providers (ESPs) (e.g., Hotmail, Yahoo, and Gmail) who have to deal with millions of emails everyday. Two primary challenges these companies face in spam filtering are efficiency and scalability. This study is undertaken to develop an efficient and scalable spam-filtering framework for heavily burdened email servers. We propose an Intelligent Hybrid Spam-Filtering Framework (IHSFF) to detect spam by analyzing only email headers. This framework is especially suitable for giant email servers because of its efficiency and scalability. The proposed filtering system may be deployed alone or in conjunction with other filters. We extract five features from the email header, namely “originator field”, “destination field”, “X-Mailer field”, “sender server IP address” and “mail subject”. Email subjects are digitalized using an algorithm based on n-grams for better performance. Moreover, using real-world data from a well-known ESP in China, we employ various machine-learning algorithms to test the model. Experimental results show that the framework using the Random Forest algorithm achieves good accuracy, recall, precision, and F-measure. With the addition of MetaCost framework, the model works stably well and incurs small costs in various cost-sensitive scenarios.