Search

Acoustic manipulation is a non-contact process that applies acoustic waves to immobilize particles into a specific region for a variety of potential applications. This provides an alternative way to address air ventilation requirements where building systems are becoming smarter and more efficient. Development of such a process within confined spaces can incorporate microscopic interactions to filter aerosol-based particulate matter (PM). In real-engineering conditions, it is hard to filter sub-micron particles (0.25–1.0 μm) than super-micron particles (> 2.5 μm) by using low-grade filters. The objectives of this work are twofold. First, we propose a new acoustic-driven pre-filtering device (i.e., a U-shaped resonant acoustic chamber) that can improve the working efficiency of low-grade filters for capturing such particles. Second, the device can optimize spatial homogeneity to enhance the removal efficiency of airborne particles under lower sound intensity requirements. The U-shaped acoustic-driven device in the form of a resonant chamber allows PM to reside at the pressure node of a standing wave. Experimental studies are conducted to verify the present design. The results show that an overall filtration efficiency of up to 89% for 1.0-μm airborne particles can be achieved when the acoustic-driven device is coupled together with a low-grade MERV-6 coarse filter. As a standalone device, the acoustic effect works well for the sub-micron particles with a filtration efficiency of up to 61% under a lower sound pressure level (116 dB) than as previously reported in the literature. In the analysis, we also discuss the performance dependence on frequency, sound pressure level and flow rate in terms of particle size distribution. The relevance of this research is a major step towards engineering an acoustic-based pre-filtering technique for developing future innovative ventilation solutions.

This letter presents an eighth-order dual-mode waveguide filter, fabricated using the selective laser melting (SLM) technique. The filter employs four dimpled ellipsoid dual-mode resonators, operating at the fundamental TM101 mode. During printing, the filter can be mechanically self-supported without any internal supports. Compared to the filters employing tuning posts, the proposed filter is less sensitive to fabrication errors in terms of their impacts on passband return loss (RL), frequency shift, and bandwidth. The filter was demonstrated at WR-112 band (7–10 GHz), with a measured insertion loss of 0.25–0.4 dB and an RL >15 dB across the passband of 8–8.46 GHz.

Hybrid power quality conditioner (HPQC) has been utilised in a co‐phase electrified railway system with the aim of improving power quality indices. A C‐type filter is designed as an auxiliary device alongside the HPQC for high‐order harmonics mitigation in this paper. Alternatively, the C‐type filter compensates a selected portion of load reactive power, while the HPQC compensates for the left required power. Furthermore, the HPQC mitigates grid side unbalance and other harmonics in a co‐phase supplied system simultaneously. In addition, a design procedure of LCL filters is proposed to suppress high‐order harmonic contents induced by the HPQC converters to achieve lower grid‐side total harmonic distortion (THD). Moreover, a flexible partial compensation (PC) method considering the C‐type filter effect on the HPQC performance is adapted to maintain the grid‐side power quality indices in an acceptable range. The C‐type filter and PC method provide VA rating reduction of the HPQC without applying any complication to the compensation control system. Finally, simulation and experimental results are presented to validate the feasibility and functionality of the proposed configuration.

The parameters require for maintaining the crop's health and the integrity of the irrigation system has a relation with quality of irrigation water. In Micro irrigation appropriate filtration is very much important to prevent clogging. Filtration unit is one of the main components in micro irrigation system that too, Primary filter is more important to be installed along with the unit, when the water source is from open reservoir and other water sources. Though it is essential, the primary filter is not being installed by the farmers due to its high cost. Hence this study is an attempt to fabricate low-cost filter and to evaluate its performance with different filter media layer. A low-cost primary filter for drip irrigation system is fabricated. Different filter media layer like silex sand (fine silex, coarse silex), pebbles with different sizes are selected. The results revealed that the removal efficiency of filter using silex sand and pebbles were 71% in terms of turbidity, 78% in terms of total suspended solids removal at flow rate of 12 m3 /hr. This fabricated low-cost filter will be cost effective for small farm applications.

Wideband slot antenna gain enhancement using concentric square and circular loop metasurface reflector (SCLMR) is presented in this paper. In two steps, the design is realized. The analysis design of a FSS-based SCLMR unit cell is included in the first step. The SCLMR structure is made up of resonant metallic square-loop and inner circular loop structures. In terms of bandwidth, the unit cell structure can be considered as a stop-band filter (2.5 GHz-6.5 GHz). The design of a wideband slot antenna (29 mm × 24 mm × 1.57 mm) is included in the second step. EM simulation tool is used to analyze, design, optimize, and validate the proposed unit cell and slot antenna combination. CST microwave studio is utilized to design and analyze a slot antenna and a unit cell in this study. The computed result of an antenna with SCLMR indicates a 101.21 % operational impedance bandwidth (2.76 GHz to 8.41 GHz). With SCLMR, the antenna has a high gain of 8.75 dBi. The designed antenna structure provides a gain enhancement of 5.50 dBi while in comparison to the same antenna without SCLMR.

This work presents a new negative interresonator coupling structure that can be applied to circular waveguide resonators, such as substrate-integrated waveguide (SIW) resonators and metallic cavity resonators utilizing the TM011 mode. The negative coupling is obtained by using the fact that the magnetic field in the resonator circulates in the opposite directions in the upper and lower half parts. As it can be entirely enclosed within a filter structure, it does not produce radiation loss. For verifying the proposed negative coupling structure, we have designed and fabricated a fourth-order bandpsss filter having a pair of transmission zeros using SIW resonators. In addition, the proposed negative coupling structure can be applied to the metallic waveguide cavity resonator filters. Hence, we also have designed, fabricated, and measured a fourth-order metallic cavity bandpass filter having a pair of transmission zeros by adopting the proposed negative coupling structure.

This letter presents our study on a compact and low-loss E-band bandpass filter (BPF) based on multilayer groove gap waveguide (GGW). Different from other reported planar GGW filters and vertically stacked substrate integrated waveguide (SIW) filters, the proposed filter realizes a cross-coupled quadruplet topology by introducing a pair of unique circular capacitive holes across layers, innovative ridges (capacitive iris), and conventional inductive irises on the same layer, which reduces the insertion loss (IL) while having a smaller size. By tuning the dimensions of the coupling structures and cavities, the filter is optimized to exhibit a superior performance in simulation. The designed filter is fabricated using CNC machining and verified in experiment. The measured results agree with the simulated ones quite well, showing an average IL of 0.45 dB and an 8% relative 3-dB bandwidth at a center frequency of 73.65 GHz. Therefore, the proposed multilayer GGW structure opens a new avenue in designing high-performance filters in millimeter-wave bands.

Approximate Differentiators have been extensively used in the estimation of joint velocity measurements of industrial manipulators. In this letter, we exploit key differences between the continuous-time model of the first-order Approximate Differentiator, commonly known as the Dirty-derivatives Filter (DF), and its discrete-time model. We show that the discrete-time filter shares the characteristics of an exponentially weighted moving average; in particular, the filter smooths the derivative of its input. We numerically and experimentally demonstrate how the filter estimation performance follows a convex trend in function of the filter bandwidth. We further demonstrate how the bandwidth at which the filter achieves “optimal” performance varies with the frequency of the filter’s input. Inspired by the latter, we propose an Approximate Differentiator with Varying Bandwidth (ADVB) where the filter bandwidth varies based on the magnitude of the position tracking error. We illustrate the superiority of the proposed ADVB over various differentiation schemes both numerically and experimentally.

In microgrids with droop-controlled inverters, the supervisory controller must the tune the droop gains in real time to maintain stability as the system topology or the number of inverters change. Stability could be compromised due to delays in the dispatch of supervisory control inputs or due to communication link failures. This article proposes a filter deballasting control (FDC) method for maintaining stability in the interim period until the supervisory inputs become available. FDC employs a quasi-open-loop control structure to leverage the inductance of the LC filter as a stability ballast, which under normal operation does not contribute to the dynamic behavior of the system. To further improve the stability, a lead-lag compensator is implemented to compensate the lagging dynamics of the deballasted system. The proposed method does not affect the steady-state active and reactive power share of the inverters, enabling a smooth transition between the conventional and FDC operating modes. The effectiveness of the proposed control strategy is verified through theoretical and experimental means.

Multi-party set reconciliation is a key component in distributed and networking systems. It naturally contains two dimensions, i.e., set representation and reconciliation protocol. However, existing sketch data structures are insufficient to satisfy the new needs brought by the multi-party scenario simultaneously, including space-efficiency, mergeability, and completeness. The current reconciliation protocols, on the other hand, fail to achieve the global optimization of communication cost. To this end, in this article, we propose the marked cuckoo filter (MCF), a data structure for representing set members. Grounded on MCF, we implement the MCFsyn protocol to reconcile multiple sets. MCFsyn aggregates and distributes sets information represented by MCFs along with an underlying minimum spanning tree among the participants. The participants then identify the different elements by traversing the overall MCF which contains the information of all elements in the union set. For the identified missing elements, MCFsyn helps the participants to choose the optimal senders to fetch with the minimum communication cost. Comprehensive evaluations indicate that MCFsyn significantly outperforms existing alternatives in terms of both reconciliation accuracy and communication cost.

Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events that are unwanted. We propose HashHeat, a hashing-based spatiotemporal BA filter for DVS. It is the first spatiotemporal filter that doesn't scale with the DVS output size and doesn't store the 32-bits timestamps. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's global performance and local performance respectively, where we introduce a novel metric for evaluating global performance. The experimental results show that HashHeat achieves similar global performance as baseline filters, but increases the signal to noise ratio by about 1.5x to nearly 5x compared with other baseline filters concerning the local performance. The hardware implementation enables HashHeat to output a labeled event every 10ns and meets the real-time requirement. And it can reduce the storage cost by 128x to 256x compared with baseline filters.

Many conventional quality prediction models are directly developed based on the easy-to-measure variables, and thus the local information within individual unit may be buried by information of other units. In this study, a cascaded regression network (RegNet) is proposed to solve the aforementioned issue. Specifically, the features which are adopted to develop RegNet model are extracted in two steps, including variable-wise and unit-wise feature extractions. In variable-wise feature extraction, several adjacent variables and their corresponding time lags are integrated using convolutional filter. By this means, both local correlation and temporal information within each unit can be preserved. In the unit-wise feature extraction, the local information of each unit is adopted to further explore the global correlation between different operation units. Based on the obtained global features, a fully connected layer is designed to calculate the regression weight of the quality prediction model. It is noted that the architecture of RegNet can be readily generalized to many existing methods by replacing the convolutional filter and fully connected layer. The performance of the proposed method is illustrated using a simulated process and two real industrial processes, and the experimental results show that it can provide reliable prediction results for industrial applications.

A three-phase three-level converter with an LCL filter has been used in the industry because of its low switching harmonics. However, it inevitably suffers from dc neutral-point imbalance, which may unnecessarily stress semiconductors and deteriorate power quality. The imbalance will, nevertheless, naturally rebalance, but it usually takes a long time. A common rectification is, thus, to add an active or a passive booster to speed up the balancing. An active booster requires sensitive sensors and control modification, which will increase the system cost and complexity. A passive booster is, therefore, preferred owing to its exemption of monitoring and control efforts. The existing three-phase passive booster, however, contains several extra separate LC components, which are normally not functioning during the balanced conditions. They should, hence, be reduced. One approach is to dual use the existing LCL (or other high-order) filter for both harmonic filtering and dc-link balancing. Topologically, it requires only the addition of one extra capacitor along the return common-mode path. Using newly formulated criterions related to sizing, harmonic, and modulation selections, the balancing performance of the resulting capacitive balancer has been evaluated comprehensively. Finally, an experimental prototype is built for testing the balancing mechanism under different operating conditions.

This letter describes the design of a right-hand circularly polarized planar filtenna (filter + antenna) that can reconfigure its operation between two center frequencies in the Ka-band. The filtenna structure is composed of a four-element array radiator and a reconfigurable bandpass filter, occupying an area of 31 × 14 mm. The circularly polarized array elements are fed by a sequential power divider for an enhanced polarization purity. The filter is based on a ring resonator loaded with a variable open stub. The length of the stub extends through a p-i-n diode switch, resulting in a change of the loaded reactance into the ring, and consequently tuning the filter's operating center frequency from 30.3 to 29 GHz. The proposed low-loss filtenna structure exhibits a reconfigurable fractional bandwidth up to 7.74%, a peak realized gain that can reach up to 11.5 dBic, an axial ratio of less than 1 dB, and a sidelobe level that can go as low as −12 dB. The prototype filtenna is fabricated and tested, where the measured results verify the simulated data. A figure of merit is proposed to highlight the superior performance of the presented Ka-band structure in terms of the polarization, size, reconfiguration ability, and radiation characteristics as proven by the comparison with other works in the literature.

Parasitic motion corrupts the measurement of all non-contact sensor systems. It thus hinders deployment of these sensors for such real-world applications where it is impossible to keep the sensor perfectly stationary. In this paper, an adaptive algorithm is presented to remove parasitic vibrations corrupting the measurements of a self-mixing (SM) interferometric laser sensor. Previously, this was achieved by coupling a solid state accelerometer (SSA) with the SM sensor followed by either a pre-calibration based design or a time-domain adaptive filter based system. The proposed method is based on adaptive spectral filtering where filter coefficients are derived based on the parasitic vibrations present in the retrieved target motion. Importantly, it neither requires any pre-calibration procedure nor has any dependence on parameters such as filter-order, convergence criteria, or step-size (required in case of time-domain adaptive filters), thus making the proposed scheme a suitable choice for mass production of SSA-SM sensor systems. The proposed algorithm provides improved mean RMS error of 5.37 nm as opposed to 19.1 nm for least mean square (LMS) filter, 20.22 nm for recursive least square (RLS) filter, and 24.7 nm for pre-calibration based system. This method’s performance has also been characterized and an embedded, real-time, hardware design is also presented. At the end, hardware system results (on experimental data-set) and sensor bounds/bandwidth are also quantified.

In the classical form, the Poisson Multi-Bernoulli Mixture (PMBM) filter uses a PMBM density to describe target birth, surviving, and death, which does not model the appearance of spawned targets. Although such a model can handle target birth, surviving, and death well, its performance may degrade when target spawning arises. The reason for this is that the original PMBM filter treats the spawned targets as birth targets, ignoring the surviving targets’ information. In this paper, we propose a Kullback-Leibler Divergence (KLD) minimization based derivation for the PMBM prediction step, including target spawning, in which the spawned targets are modeled using a Poisson Point Process (PPP). Furthermore, to improve the computational efficiency, three approximations are used to implement the proposed algorithm, such as the Variational Multi-Bernoulli (VMB) filter, the Measurement-Oriented marginal MeMBer/Poisson (MOMB/P) filter, and the Track-Oriented marginal MeMBer/Poisson (TOMB/P) filter. Finally, simulation results demonstrate the validity of the proposed filter by using the spawning model in these three approximations.

Sigma–delta ( $\Sigma \Delta$ ) digital-to-analog converters (DACs) are commonly used to realize programmable dc voltage generators in built-in self-test (BIST) schemes, largely on account of their high linearity. Unfortunately, such $\Sigma \Delta $ -DACs require large amounts of digital memory and a reconstruction filter with a large silicon area footprint. In this article, a segmented DAC architecture is proposed. The proposed architecture is realized using two sub-DACs, where both are $\Sigma \Delta $ -based. This DAC provides two advantages: filter footprint size reduction and memory savings, thereby allowing for a simpler BIST solution. The benefits of using the segmented $\Sigma \Delta $ -DAC are shown using two experimental prototypes. The first is an IC prototype design using the TSMC 65-nm CMOS technology. It will be shown that the IC achieves 12 bits of resolution from 1020 memory elements, whereas an unsegmented $\Sigma \Delta $ -DAC uses 4095 elements to obtain the same resolution. This is about a 75% savings in memory elements. The segmented prototype occupies 0.5 mm2 of silicon area, whereas the unsegmented design occupies 0.77 mm2 for the same resolution. A 35% silicon area savings compared with its unsegmented counterpart. A second prototype implements the segmented $\Sigma \Delta $ -DAC architecture using discrete components. The discrete prototype achieves 16 bits of resolution from 1020 memory elements, whereas the unsegmented counterpart uses 65 535 bits for the same resolution. This is a 98% saving in memory elements.

We present a nonlinear geometric approach to fault detection and isolation (FDI) in a grid-connected inverter system. Open-switch faults in inverter power transistors together with faults in grid voltages and source current sensors are detected and isolated in the presence of disturbance. The detection process is based on dynamic model of an inverter, including a dc-bus capacitor and an output $RL$ filter. The proposed FDI system does not rely on balanced or zero-sum conditions in the grid point and is functional under nonsinusoidal voltages and currents.

In this letter, a combined approach based on circuit equivalent and field analysis is employed to design a waveguide low-pass filter. According to the field analysis and out-of-band transmission poles responses, the transmission pole spacing distribution method is proposed to obtain a wide stopband. The simulation indicates that this method can provide a well-performed harmonic suppression. Different from the large-scale parameter optimization method for the wide stopband, this technique has the advantage of consuming relatively small computation resources. Finally, the X-band waveguide low-pass filter is fabricated and tested. Measurements show that the stopband suppression can exceed the fourth-order harmonic of the fundamental mode with more than 56-dB suppression levels.

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. The considered system model involves a multiple antenna base station (BS) of a cellular system serving multiple single antenna users on the downlink. Furthermore, the BS simultaneously introduces sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. A novel framework based on constrained optimization problems is proposed for the joint design of the transmit waveform and the radar receive filter such that different constraints related to the power amplifiers and the radar waveform are satisfied. In contrast to the existing approaches in the DFRC systems' literature, the proposed approach does not require the knowledge of a predetermined radar beampattern in order to optimize the performance of the radar part through its approximation. Instead, a beampattern is generated by maximizing the radar receive signal-to-interference ratio (SINR) thus, enabling a more flexible design. Moreover, the radar receive filter processing and its optimization is considered for the first time on DFRC systems, enabling the effective exploitation of the available degrees of freedom in the radar receive array. Efficient algorithmic solutions with guaranteed convergence are developed for the defined constrained nonconvex optimization problems. The effectiveness of the proposed solutions is verified via numerical results.

In field-oriented vector control of permanent magnet synchronous machine (PMSM) drives, the dead time will cause the 5th and 7th current harmonics in the stationary reference frame. They are mapped to the negative and positive 6th harmonic current in the synchronous reference frame (SynRF), resulting in torque ripple and deterioration of control performance. To solve this problem, a new dead-time compensation method is proposed in this article. First, a multiple complex coefficient filter is modified with a low-pass filter being paralleled to extract positive and negative sequence components of 6th harmonic from dq -axes currents. Then, based on the extracted harmonics, a voltage feedback path with variable gains is designed in SynRF to eliminate the dead-time effect. Finally, the proposed method is implemented in a prototyped PMSM drive to verify its effectiveness. Both the simulations and experiments indicate that the proposed method can significantly weaken the dead-time effect with no additional hardware. Besides, it is also proved robust to the variable machine parameters.

A novel High-order extended Strong tracking filter (H-STF) is proposed for a class of nonlinear systems. All high-order polynomial terms in the state model are regarded as implicit variables; the original state model is equivalently formulated into a pseudo-linear form; the dynamic relationship between each implicit variable and all variables is modeled; original state model is rewritten into an augmented linear model; the nonlinear measurement model can be rewritten into linear form; taking into account the problems of modeling errors and state mutations that may be caused by the introduction of implicit variables, a high-order extended strong tracking filter is designed. Examples are presented to demonstrate the effectiveness of the new algorithm.

This article proposes an active electromagnetic interference (EMI) filter (AEF) realized by a customized integrated circuit (IC) to reduce conducted emission noises. A fully transformer-isolated feedforward current-sense current-compensation structure is selected as a base topology of the AEF. To achieve the high reliability of the proposed AEF by using low-voltage IC devices, the injection transformer is added to protect IC devices from high-voltage hazards. The operation of the proposed AEF is theoretically demonstrated, and practical design guidelines for the IC part and nearby components are developed. The designed IC was fabricated by a 0.18 μ m bipolar-CMOS-DMOS process. The fabricated IC is implemented in a compact printed circuit board for application to a three-phase inverter motor system. The performance, stability, and surge immunity of the AEF are experimentally demonstrated. A designed 1stage AEF achieves better noise-attenuation performance than 2stage passive EMI filters (PEFs) over 6 dB at 150 kHz without critical resonance peak at the high-frequency range. Weak points of the conventional PEFs in both low and high frequencies are effectively improved by using the proposed 1stage AEF. In addition, the surge immunity of the designed IC within the AEF is validated under numerous 5 kV surge tests.

The Sentry bioconvertible IVC filter (Boston Scientific, MA, USA) contains a bioabsorbable filament which hydrolyses after 60 days, allowing the arms of the filter to spring open, retract into the vessel wall and endothelialise, leaving an unobstructed IVC lumen. It is a novel treatment option for patients at transient risk of pulmonary emboli with a contraindication to anticoagulation. The device provides similar protection to other currently available devices against pulmonary emboli with minimal complications. It represents an effective alternative to retrievable filters, the removal of which is variously not attempted, not possible or associated with high complication rates. We review the literature which underpins the development of the bioconvertible filter. We describe our first deployment of the filter in an 85-year-old female with gastric malignancy (who subsequently underwent a subtotal gastrectomy) with a history of anaemia and previous pulmonary emboli. The availability of a bioconvertible filter constitutes a further step forward in the management of patients with potential or active thromboembolic disease.

In this article, topology optimization was used to design a magnetic core structure on a $\pi$ -type filter circuit. The newly designed magnetic core canceled the noise generated by conducted and induced currents, and as a result, showed dramatically improved electromagnetic interference (EMI) filter performance. Topology optimization was executed using three-dimensional electromagnetic field analysis to capture the magnetic coupling effect. The designed magnetic core was fabricated by machined milling and its performance was evaluated by measuring the noise reduction effect of a simple EMI filter equipped with the core. The EMI filter equipped with our optimized magnetic core showed about 30 dB higher performance than a filter with conventional ring-shaped cores.These results show that the performance of an EMI filter can be improved by optimizing the structure of its magnetic core.

The outbreak of the SARS CoV-2, referred to as COVID-19, was initially reported in 2019 and has swiftly spread around the world. The identification of COVID-19 cases is one of the key factors to inhibit the spread of the virus. While there are multiple ways to diagnose COVID-19, these techniques are often expensive, time-consuming, or not readily available. Detection of COVID-19 using a radiological examination of Chest X-Rays provides a more viable, rapid, and efficient solution as it is easily available in most countries. The paper outlines a method that employs an unsupervised convolutional filter learning using Convolutional Autoencoder (CAE) followed by applying it to COVID-19 classification as a downstream task. This shows that the proposed technique provides state-of-the-art results with an average accuracy of 99.7%, AUC of 99.7%, specificity of 99.8%, sensitivity of 99.6%, and F1-score of 99.6%. We release the data and code for this work to aid further research.

Security and protection of information is an ever-evolving process in the field of information security. One of the major tools of protection is the Intrusion Detection Systems (IDS). For so many years, IDS have been developed for use in computer networks, they have been widely used to detect a range of network attacks; but one of its major drawbacks is that attackers, with the evolution of time and technology make it harder for IDS systems to cope. A sub-branch of IDS-Intrusion Alert Analysis was introduced into the research system to combat these problems and help support IDS by analyzing the alert triggered by the IDS. Intrusion Alert analysis has served as a good support for IDS systems for many years but also has its own short comings which are the amount of the voluminous number of alerts produced by IDS systems. From years of research, it has been observed that majority of the alerts produced are undesirables such as duplicates, false alerts, etc., leading to huge amounts of alerts causing alert flooding. This research proposed the reduction alert by targeting these undesirable alerts through the integration of supervised and unsupervised algorithms and approach. The research first selects significant features by comparing two feature ranking techniques this targets duplicates, low priority and irrelevant alert. To achieve further reduction, the research proposed the integration of supervised and unsupervised algorithms to filter out false alerts. Based on this, an effective model was gotten which achieved 94.02% reduction rate of alerts. Making use of the dataset ISCX 2012, experiments were conducted and the model with the highest reduction rate was chosen. The model was evaluated against other experimental results and benchmarked against a related work, it also improved on the said related work.

Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that selected from each filter feature selection. The filter feature selection that implemented in this research are Information Gain (IG), Chi-Squares, ReliefF and Fisher Score. Genetic Algorithm (GA) is used to select the best subset from the ensemble result of the filter feature selection. In this research, Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes classification techniques were used to diagnose the CKD. The features subset that selected are different and specialised for each classifier. By implementing the proposed method irrelevant features through filter feature selection able to reduce the burden and computational cost for the genetic algorithm. Then, the genetic algorithm able to perform better and select the best subset that able to improve the performance of the classifier with less attributes. The proposed genetic algorithm union filter feature selections improve the performance of the classification algorithm. The accuracy of RF, XGBoost, KNN and SVM can achieve to 100% and NB can achieve to 99.17%. The proposed method successfully improves the performance of the classifier by using less features as compared to other previous work.

The world of Wireless Technology has been improving consistently due to its high speed and reliability. Some of the demands that need to be fulfilled include larger network capacity, improved data rates and so on. This paper focuses on the design and simulation of the Transmitter, which is a key component in an RF Transceiver Module. It is built by cascading an Up Conversion Mixer, a Bandpass Filter and a Power Amplifier as its most fundamental units. The Cadence Virtuoso Tool was used to construct this Transmitter block in 180nm node technology. The IF signal to the Mixer is 100 MHz and the LO signal is 2.3GHz. The 2.4GHz amplified RF output is taken from the Power Amplifier. A 1dB compression point of -25.91 dBm and an IIP3 of 10.14 dBm is observed for the proposed design. Future work involves testing its integrity with an RF Receiver module that has appropriate modulation schemes in place and converting it into a physical layout-based IC.

This paper deals with a motion and force control scheme for underwater robots, each of which is equipped with a manipulator. Several motion and force controllers for this type of underwater robot have been developed. Most of them were designed under the assumption that the control capability of each robot body is the same as that of each manipulator. However, it has been pointed out in the literature that for a real underwater robot, its robot body control is more challenging than its manipulator control, because the robot body has much larger inertia, and many more inaccurate position sensors and actuators than the manipulator. Therefore, even if an advanced control law with good performance is implemented in the vehicle controller of each control system, its original control performance may not be achieved. In this paper, we develop a motion and force controller for the manipulator under the condition that the robot body is independently controlled by a motion controller with poor performance. Its features are (1) to be designed in consideration of the dynamics of the robot body including its actuators (i.e., marine thrusters), (2) to ensure the stability properties in the presence of bounded disturbances, and (3) to include a linear force error filter, which can facilitate the stability analysis in comparison with existing nonlinear force error filters. Furthermore, the theoretical results were supported by those obtained in numerical simulations.

This paper explores the design and hardware efficient realization of low complexity filter bank-based reconfigurable channelizers suitable for wireless receivers. The reconfigurability is achieved by deriving a set of non-uniform filter banks with different sampling factor combinations from a low complexity partial cosine modulated uniform filter bank, by combining different number of its neighbouring channels. The hardware complexity comparison of the filter bank channelizer designed by the proposed approach, with the existing works proves that this approach requires less number of multipliers in its realization to extract the same set of communication standards. To reduce the power and complexity of the multipliers needed for the hardware realization of the filter coefficients, an approximation algorithm, which increases the zeros in the binary represented filter coefficients is employed. The synthesis results show that the algorithm offers a considerable reduction in the hardware and power consumptions of the filter bank.

The inverse filters are commonly used in the fields of signal processing, communication, instrumentation, and control system for the recovery of a distorted signal. In this paper, an operational transresistance amplifier (OTRA)-based third-order multi-function inverse filter has been proposed. The proposed method uses two OTRAs and suitable combinations of admittances to realize the frequency response characteristics of inverse low-pass filter, inverse band-pass filter, inverse high-pass filter, and inverse band-reject filter. The proposed circuits have been verified by PSPICE simulations with the CMOS model of OTRA using 0.5 μm CMOS technology. The proposed third-order multi-function inverse filters give a frequency roll-off in the range of 49.79 dB/decade to 55.38 dB/decade in the transition band which gives an improvement of almost 25 to 38% in roll-off in comparison with the existing literature. The experimental verification of the proposed structure is also included.

In this survey, we present single-photon states of electromagnetic fields, discuss discrete measurements of a single-photon field, show how a linear quantum system responds to a single-photon input, investigate how a coherent feedback network can be used to manipulate the temporal pulse shape of a single-photon state, present single-photon filter and master equations, and finally discuss the generation of Schrodinger cat states by means of photon addition and subtraction.

To make a better discrimination between target and false alarm, amplitude information (AI) of radar signal has been incorporated into many target tracking algorithms, such as probabilistic data association, multiple hypothesis tracking and probability density hypothesis (PHD). In this paper, we present the cubature integration based Gaussian mixture implementation of the PHD filter with AI, namely the GMPHD-AI filter. Since the utilization of multiple sensors is more effective than using only one sensor, we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion. The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario.

The objective of the current study is to numerically predict the collection efficiency, particulate matter (PM) loading, pressure drop, and flow characteristics in partially damaged or unplugged filters. Five different PM size classes with mean particle diameters ranging from 25 to 300 nm are considered for loading the filter. These PM classes are transported in the computational domain as scalars and collected in the filter through Brownian diffusion and interception mechanisms. Four different partially damaged filters with varying damaged cross-sections in the outlet face of the filter are considered. Five different exhaust gas flow rates from 50 to 300 kg/h with a transient soot loading condition are considered, which leads to different temporal and spatial soot cake evolutions as well as a different total soot mass in the filter. As expected, plugged filter sections have higher filtration efficiencies which increases monotonically with soot accumulation. For damaged segment, however, efficiency is nonmonotonic and depends strongly on particle size, loading, and prevailing flow conditions. The collection efficiency of damaged segments is less than 40% at all flow rates and damaged cross-sections considered in this study. The overall filter efficiency is evaluated as the sum of flow averaged filtration efficiencies of both plugged and unplugged sections. The overall filter efficiency is less than 100% and this efficiency decreases from filter-1 to filter-4 as the damaged filter section contributes to the efficiency loss. Spatial and temporal evolution of soot cake is different in damaged and undamaged sections. Strong nonuniform soot distribution is observed in the partially damaged filters in both the axial and radial direction. One objective of the current study is to analyze the overall filter collection efficiency with respect to the percentage of damage, which as a result will assist in the roadworthiness studies, on-board diagnostic studies, or periodic technical inspection of the filters or damaged filters.

Premise selection is a task that selects likely useful premises from a large-scale premise set, which is one of the challenges for automated theorem proving. Nowadays, using graph neural networks to encode logical formulae becomes more and more appealing, as graph representations demonstrate the ability to preserve the semantic and syntactic information of logical formulae. However, graph neural networks in the prior works iteratively update each node’s embedding via information aggregated solely from its neighbors and always ignore the types of nodes. Besides, evidence shows that sharing and exchanging some information between formula pairs can ensure neural-based models more robust in measuring graph relevance. Unluckily, previous graph neural networks generate final graph embeddings independently in the premise selection task. To overcome these shortages, we propose a novel graph neural network, called ARCG-NN, for embedding the first-order logical formulae. The embedding model firstly takes the node types into full consideration and utilizes an attention mechanism based on newly proposed node types to compute weights in the message aggregation. Except for the message from the local neighborhood, ARCG-NN also dynamically exchange the cross-graph information of particular nodes along with the propagation rounds. Besides, an extra gate function on node types is used to filter out irrelevant information in the graph aggregation phase. To train, validate, and test our approach, we build balanced and unbalanced datasets based on the MPTP2078 benchmark. The experimental results demonstrate that the proposed graph neural architecture achieves state-of-the-art classification accuracy on the first-order premise selection task and helps the automated theorem prover E prove more problems in the test dataset.

The paper describes a case study with the target moving at unknown radial velocity. Exemplified by the frequency modulation signal, the study presents the analysis of specific features of channel multiplexing for the target range migration compensator at the compression filter output within pulse burst periods. Channel multiplexing allows to reduces losses in case of long-term signal integration. The solution effectiveness is estimated and a method is proposed for compensator channel multiplexing ensuring protection against unwanted signal expansion.

Gao, et al. (2015) gave a simple algorithm to compute Grobner bases named GVW. It can beused to compute Grobner bases for both ideals and syzygies at the same time, and the latter plays an important role in free resolutions in homological algebra. In GVW algorithms the authors need to compute all the J-pairs firstly and then use GVW criterion (which refers the criterions used in GVW) to determine which one is useless or which one the authors should do top-reduction. In this paper, based on the study of relations between J-pairs, the authors propose the concept of factor. This concept allows the authors to filter the useless J-pairs in a rather convenient way. Moreover, by using this concept, the authors may easily determine which two pairs’ J-pair need not to be computed. Besides, the Grobner basis which the authors obtained is relatively simpler than the one in GVW.

This paper proposed a 5.6 kW 11.7 kW/kg four-phase interleaved buck converter (FPIBC) for the unmanned aerial vehicle (UAV) low-voltage power system. The UAV mainly concerns the lightweight strategy and the power density of the DC-DC converter. We proposed a comprehensive lightweight strategy with the characteristics of reduced inductor current ripples, small size of filter inductors and capacitors, and high output power. The output current ripples are reduced by using interleaved modulation strategy and integrated CLC filters cascaded to the input side and output side respectively. In addition, the aluminum based printed circuit board (PCB) is used with SMD power transistors to obtain better heat dissipation capability without fans. With abovementioned methods, the designed DC-DC converter has a power of 5.6 kW @ 0.48 kg, the power-to-weight ratio is improved to be 11.7 kW/kg, and the power density is 3 W/cm3. The efficiency of this DC-DC converter is up to 95.8%.

Motivation Medical image fusion has developed into an important technology, which can effectively merge the significant information of multiple source images into one image. Fused images with abundant and complementary information are desirable, which contributes to clinical diagnosis and surgical planning. Results In this article, the concept of the skewness of pixel intensity (SPI) and a novel adaptive co-occurrence filter (ACOF)-based image decomposition optimization model are proposed to improve the quality of fused images. Experimental results demonstrate that the proposed method outperforms 22 state-of-the-art medical image fusion methods in terms of five objective indices and subjective evaluation, and it has higher computational efficiency. Availability and implementation First, the concept of SPI is applied to the co-occurrence filter to design ACOF. The initial base layers of source images are obtained using ACOF, which relies on the contents of images rather than fixed scale. Then, the widely used iterative filter framework is replaced with an optimization model to ensure that the base layer and detail layer are sufficiently separated and the image decomposition has higher computational efficiency. The optimization function is constructed based on the characteristics of the ideal base layer. Finally, the fused images are generated by designed fusion rules and linear addition. The code and data can be downloaded at https://github.com/zhunui/acof. Supplementary information Supplementary data are available at Bioinformatics online.

PurposeThis study aims to argue that opportunism is central to management thought and illustrate its evolution into a central element of the entrepreneurship theory. The authors show that many criticisms of opportunism tend to conflate the concept with other theoretic traditions.Design/methodology/approachThe authors trace foundational works by Taylor, Mayo, Fayol, Barnard, Follett and Simon to limit opportunism under the guise of promoting cooperation in organizations.FindingsOpportunism is conceptualized in transaction cost economics as one of the most controversial concepts in management. While modern management is based on handling opportunism, it is bad for practice, as it ignores innovation, and damages trust and goodwill among organizational members. These interventions serve as a knowledge filter, damaging organizational entrepreneurship.Originality/valueBy tracing the roots of opportunism in early management thought, the authors clarify ethical and entrepreneurial issues of mutual obligations in organizations. The authors also place workplace conflict to be a more coherent framework that better reflects the core concept of opportunism.

In recent years, occupational stress mining has become a widely exciting issue in the research field. The primary purpose of this study is to analyze filter feature selection methods for the efficient occupational stress classification model. We propose and examine seven different techniques of filter feature selection such as Chi-Square, Information Gain, Information Gain Ratio, Correlation, Principal Component Analysis, and Relief. The resultant selected features are then used with popular classifiers like Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Gradient Boosted Trees (GBT) for detection of occupational stress in the insurance sector. A survey-based psychological primary occupational stress data set is used to evaluate the relative performance of these methods. This study effectively demonstrated the significance of filter feature selection methods and explained how accurately they could help classify stress levels. This study showed that the Correlation-based feature selection with the SVM classifier obtained the best performance compared to other filter feature selection methods and classification models.

Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) is available. Consequently, the cost of collision recognition could be reduced dramatically.