Filtering Capability Research Articles

Dielectrophoresis-Enhanced Microfluidic Device with Membrane Filter for Efficient Microparticle Concentration and Optical Detection

This paper presents a novel microfluidic device that integrates dielectrophoresis (DEP) forces with a membrane filter to concentrate and trap microparticles in a narrow region for enhanced optical analysis. The device combines the broad particle capture capability of a membrane filter with the precision of DEP to focus particles in regions optimized for optical measurements. The device features transparent indium tin oxide (ITO) top electrodes on a glass substrate and gold (Au) bottom electrodes patterned on a small area of the membrane filter, with spacers to control the gaps between the electrodes. This configuration enables precise particle concentration at a specific location and facilitates real-time optical detection. Experiments using 0.8 μm fluorescent polystyrene (PS) beads and Escherichia coli (E. coli) bacteria demonstrated effective particle trapping and concentration, with fluorescence intensity increasing proportionally to particle concentration. The application of DEP forces in a small region of the membrane filter resulted in a significant enhancement of fluorescence intensity, showcasing the effectiveness of the DEP-enhanced design for improving particle concentration and optical measurement sensitivity. The device also showed promising potential for bacterial detection, particularly with E. coli, by achieving a linear increase in fluorescence intensity with increasing bacterial concentration. These results highlight the device’s potential for precise and efficient microparticle concentration and detection.

A discovery platform to improve visibility of linked data across Australia.

BackgroundMetadata plays a crucial role in the health research infrastructure ecosystem. Despite the abundance of metadata for data collections in Australia, there is no simple way for researchers to search for standardised metadata about linked and linkable data collections across sectors or jurisdictions, making it an arduous and time-intensive task. MethodsThe project comprised three phases: an initial scoping exercise to understand the current state of metadata and best practice; a national consultation involving researchers data linkage staff and data custodians to develop a high-fidelity prototype; and a final build and implementation phase. The platform underwent several prototyping and testing cycles to refine the digital experience. ResultsKey expert interviews confirmed that there is a wealth of metadata available in Australia, but it is difficult for researchers to access and evaluate. Consultations with researchers identified opportunities to standardise metadata and provide a centralised platform to enhance the discoverability of linkable data collections for research. High value platform features included searching, browsing and filtering capabilities, variable list metadata, standardised formats, sample data, and frequently asked questions. The final design and functionality reflected user consultations and data custodian input on feasibility and resulted in the standardisation of metadata for linked data collections across sectors and jurisdictions. ConclusionThe Population Health Research Network developed and implemented a metadata platform to enable researchers to assess and evaluate the suitability of data collections for data linkage projects more readily. Improved metadata accessibility will save time and enhance the quality of linked data applications.

Development and research of filter models communication and control systems configured for the required set of parameters

Problem statement. A digital filter refers to a hardware or software implementation of a mathematical algorithm that converts a digital signal in a certain way. The classification of digital filters is usually based on the functional characteristics of digital filtering algorithms. Digital filters may have parameters that cannot be implemented in analog filters, do not require periodic monitoring and calibration, and one filter can process several input channels or signals, while the accuracy is limited only by the bit depth used. The implementation of digital signal processing units using programmable logic integrated circuits (FPGAs) will significantly simplify hardware costs and make the most effective use of these digital filter capabilities. Goal. Conducting research in the field of digital signal processing in order to obtain fundamentally new software and hardware architectures, developing an RTL model of a complex functional block (SF block) of a digital signal processing system, its verification both at the logical and physical level. Results. In the course of the scientific research, a software and hardware model of the filter was developed to adjust the parameters during operation, taking into account their architectural advantages and disadvantages. In order to verify and confirm the adequacy of the methodology developed in the course of the study, an SF model was created-a digital signal processing unit. This model has been implemented and physically verified on the basis of a debugging board using FPGAs. Practical significance. The technique presented in this paper will allow the development of software and hardware devices for digital signal processing, with the possibility of their configuration at the software level for the required set of processing parameters, which in turn will significantly save resources spent on development.

Three-Phase Transformerless PV Inverter With Reconfigurable LCL Filter and Reactive Power Capability

Three-Phase Transformerless PV Inverter With Reconfigurable <i>LCL</i> Filter and Reactive Power Capability

The Profile of Mask Thickness on Acute Rhinitis During Anatomy Practicum: A Study Among Medical Students at Universitas Ciputra

Introduction. Rhinitis is an inflammatory condition that affects the linings of the nasal mucosa. The symptoms of rhinitis include irritation of the nasal passages, eyes, and throat, leading to sneezing, rhinorrhea, nasal congestion, and watery eyes. Acute rhinitis is often linked to viral respiratory infections or exposure to environmental allergens, including formaldehyde, commonly used as formalin in anatomy practicums to preserve cadavers in medical schools. Medical students use masks in anatomy practicums, including N95, medical/surgical, and fabric masks, which vary in efficacy and filtering capabilities. To reduce risks for medical students exposed to formaldehyde, minimizing the likelihood of acute rhinitis is crucial. This research aims to contribute as a comprehensive guideline for understanding the relationship between mask types and acute rhinitis. Methods. A cross-sectional approach with random sampling was employed to collect data from 32 out of 49 Universitas Ciputra medical students participating in the anatomy practicum. 2 respondents with existing allergies are excluded, resulting in a final sample size of 30 respondents. Results. 11 respondents who wore N95 masks affirmed that the masks prevented acute rhinitis. This finding was supported by the minimal occurrence of acute rhinitis symptoms in N95 mask users, contrasting with fabric mask users who displayed the highest symptom frequency. Discussion. The ability of N95 masks in reducing the occurrence of acute rhinitis was evident due to its higher filtration efficacy, but there were drawbacks including comfort issues. Conclusion: To ensure safety and prevent acute rhinitis during anatomy practicums, medical students are recommended to wear N95 masks.

Design of Channel Drop Filters Based on Photonic Crystal with a Dielectric Column with Large Radius inside Ring Resonator

Photonic crystal channel drop filters (CDFs) play a vital role in optical communication owing to their ability to drop the desired channel. However, it remains challenging to achieve high-efficiency CDFs. Here, we demonstrate a highly efficient three-channel CDF with both high transmission and high quality (Q) factor based on a novel ring resonator that is in the middle of two waveguides. A dielectric column with a large radius replaces the homogeneously distributed dielectric columns inside the ring cavity to modulate the coupling ratio with a straight waveguide, thereby enhancing the transmission and Q factor. The transmission and Q factor of the single-cavity filter are 99.7% and 12,798.4, respectively. The mean value of the three-channel filter based on the basic unit can reach up to 94.6% and 10,617, respectively, and a crosstalk between −30.16 and −50.61 dB is obtained. The proposed CDFs provide efficient filter capability, which reveals great potential in integrated optoelectronics and optical communication.

Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis

The number of satellites in orbit around Earth is increasing rapidly, with the risk of collision rising accordingly. Trends of the global population of satellites need to be analyzed to test the viability and impact of proposed rules and laws affecting the satellite population and collision avoidance strategies. This requires large scale simulations of satellites that are propagated on long timescales to compute the large amounts of actionable close encounters (called conjunctions), which could lead to collisions. Rigorously checking for conjunctions by computing future states of orbits is computationally expensive due to the large amount of objects involved and conjunction filters are thus used to remove non-conjuncting orbit pairs from the list of possible conjunctions. In this work, we explore the possibility of machine learning (ML) based conjunction filters using several algorithms such as eXtreme Gradient Boosting, TabNet and (physics-informed) neural networks and deep operator networks. To show the viability and the potential of ML based filters, these algorithms are trained to predict the future state of orbits. For the physics-informed approaches, multiple partial differential equations are set up using the Kepler equation as a basis. The empirical results demonstrate that physics-informed deep operator networks are capable of predicting the future state of orbits using these equations (RMSE: 0.136) and outperform eXtreme Gradient Boosting (RMSE: 0.568) and TabNet (RMSE: 0.459). We also propose a filter based on the trained deep operator network which is shown to outperforms the filter capability of the commonly used perigee-apogee test and the orbit path filter on a synthetic dataset, while being on average 3.2 times faster to compute than a rigorous conjunction check.

Blind Edge-Retention Indicator for Assessing the Quality of Filtered (Pol)SAR Images Based on a Ratio Gradient Operator and Confidence Interval Estimation

Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator.

Wideband bandpass filter with ultra-wide stopband based on cross-shaped parallel coupled lines

A broadband out-of-band rejection capability and multiple transmission zeros of wideband bandpass filter are proposed and validated in this article, which employ quadruple anti-parallel coupled-line structures to form a novel cross-shaped resonator planar topology. The centered frequency of the single wideband bandpass filter designed for verification is located at 2.4 GHz (f 0) with a fractional bandwidth of 50%. The interaction of crossed multiple anti-coupled lines with a lowpass-loaded fan constitutes the wideband with nine transmission zeros in the 5 f 0 frequency range for improved suppression extending to 26.5 GHz (10.7 f 0) with a suppression level of 12.5 dB. Moreover, the designed prototype filter is implemented and tested with a measured minimum insertion loss of 0.9 dB. Good agreement between the measured and simulated results is attained to successfully validate the correctness of the proposed design approach.

Comparative evaluation of Z source / quasi Z-source direct and indirect matrix converters for PMG based WECS

The present era sees the dominance of wind energy conversion systems as a leading technology in renewable energy. Various technologies have been devised to harness wind energy, with variable speed systems emerging as particularly advantageous. Consequently, these systems are designed to operate at specified speeds using power electronic interfaces, maximizing power extraction from the wind. The integration of Permanent Magnet Generators (PMGs), directly linked to the wind turbine lacking a gearbox, allows operation at minimum speeds. To stabilize the continuously fluctuating output voltage of the PMG, power electronic converters are extensively utilized in Wind Energy Conversion Systems (WECS), ensuring a consistent voltage and frequency output. The voltage gain of Traditional matrix converters (MCs) is limited to less than 0.866. In turn, the Z-source (ZS) and discontinuous QZSDMC, as well as ZS IMCs, topologies have achieved voltage gains exceeding 0.866. However, the former provides complicated commutation issues from DMC, while the latter incorporates a Z source network in the DC link, resulting in a non-all-silicon solution. A novel continuous QZSDMC and QZSIMC are suggested, wherein a qZS network incorporates the grid-side filtering function. This design eliminates the need for an additional input filter in the proposed system. Three newly introduced topologies of ZSDMC, QZSDMC and QZSIMC are thoroughly compared across various parameters including voltage gain, current and voltage ripples, inductor current and capacitor voltage stresses, filtering capabilities, input current, and output voltage Total Harmonic Distortion (THD), finally efficiency. The paper presents their control and modulation methods to achieve the desired performance levels. Experimental comparisons validate the theoretical analysis and demonstrate the potential of the proposed QZSDMC as a promising topology.

Inventory Management System

Inventory management systems cater to the demands of businesses by providing efficient inventory operations via user-friendly interfaces for supplier administration, product categorization, inventory tracking, and transaction recording. This web application facilitates online stock and inventory management for a particular business or retailer. The project makes it simple for the management of the company to keep track of, record, and maintain its goods inventory. The system will have a modular architecture to enable future improvements and changes, with a focus on scalability, security, and user-friendliness. It will also include strong search and filtering capabilities, role-based access control, and support for many transaction types. It guarantees dependability and efficiency, includes extensive testing and documentation, and finally gives companies a strong instrument to streamline inventory procedures and enhance overall operations. The Inventory Management System will include cutting-edge capabilities such inventory Management Systems, with features like automated stock replenishment, real-time inventory synchronization across multiple locations or warehouses, and integration, represent a substantial investment in raising a company's competitiveness through cost savings, improved customer satisfaction, and optimized inventory processes

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors.

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

A wideband end‐fire stepped slot antenna with gain enhancement and filtering capability

AbstractA wideband end‐fire stepped slot antenna with gain enhancement and filtering capability is proposed. On one hand, a parasitic dipole serving as a director is introduced in the stepped slot to improve the radiation gain of the slot antenna without increasing its physical size. It is found that a gain enhancement of about 2 dB can be achieved after adopting such approach. On the other hand, an absorptive branch possessing a nearly complementary frequency response with regard to the one of the slot antenna is in‐parallel loaded at the microstrip feedline. It allows the partial dissipation of the out‐of‐band/non‐radiated RF‐signal power of the antenna to attain both an RF‐quasi‐absorptive behaviour and filtering capability. It is found that after loading the dissipative branch, the out‐of‐band radiation efficiency of the slot antenna is reduced to less than 20%. The measured results of a fabricated proof‐of‐concept antenna prototype show a reasonable agreement with the simulated ones and verify the RF performance merits of the proposed stepped slot antenna, such as wide RF operating band covering the 3.39−4.73‐GHz range, end‐fire radiation with a front‐to‐back ratio (FBR) above 10 dB, flat radiation gain, and co‐integrated filtering functionality with RF‐quasi‐absorptive capability.

Dual Channel Residual Learning for Denoising Path Tracing

In this paper, we present a denoising method for path tracing using residual learning with convolutional neural networks (CNNs). Noisy artifacts in path tracing are inherited from insufficient sampling, which often generates over- or under-exposed values when integrating the limited bright or dark samples in a pixel. In this paper, we introduce a dual channel residual learning CNNs which separates the over and under-exposed signals in order to provide an efficient denoising filter for the path tracing rendering. Furthermore, we present an advanced CNN comprised of variable-sized kernels in each convolutional layer. Our CNN detects features in different scales providing an adaptive denoising filter capability which is optimal for extracting various contextual details in a complex scene. The experiments demonstrate that our method generates better visual quality than other compared approaches across various rendering effects.

Highly stable single-longitudinal-mode erbium-doped fiber laser using dual-ring compound cavity filter and saturable absorber

Single-longitudinal-mode (SLM) fiber lasers have the advantages of stable output, long coherence length, and low noise. However, several fiber lasers still maintain multiple longitudinal mode outputs. Thus, a highly stable SLM erbium-doped fiber laser (EDFL) was proposed and investigated. The dual-ring compound cavity (DRCC) filter, composed of two Type-Ⅱ fiber rings, and a 1.5 m unpumped erbium-doped fiber (EDF) was used as a mode-selection filter. The mode-selection capability of the DRCC filter and unpumped EDF was investigated and discussed theoretically and experimentally. The proposed EDFL could generate stable SLM output with an optical signal-to-noise ratio of ∼ 64 dB over 60 min. The maximum wavelength drift was not observed on an optical spectrum analyzer and the maximum peak power fluctuation was kept at a low level of 0.16 dB. The output power variations with different pump powers, the relative intensity noise, the relaxation oscillation, and the linewidth at different integration times were investigated. Furthermore, the performance of the proposed EDFL acted as a strain sensor and temperature sensor with sensitivities of 1.0 pm/με and 11.4 pm/℃ were also discussed.

Magnetic integration of a symmetrical LCL filter in the grid‐tied inverter with reduced EMI noise

AbstractHarmonics and electromagnetic interference (EMI) pose serious threats to the safety and efficiency of grid‐tied inverters. Although the inductor‐capacitor‐inductor (LCL) filter offers commendable harmonic suppression, it does not sufficiently mitigate leakage current and EMI caused by high‐frequency pulse‐width modulation (PWM). This paper examines the use of a symmetrical LCL filter to reduce the AC side leakage current in grid‐tied inverters. The symmetrical structure bolsters the conducted EMI suppression capability of the LCL filter. A magnetic integration scheme for symmetric LCL filters is introduced, and through thoughtful structural design, it achieves filter inductance integration on the EIE magnetic core. An experimental platform, equipped with a high‐frequency SiC‐MOSFET voltage source inverter with a 600 W output, is utilized. The LCL filter, symmetric LCL filter, and magnetic integrated symmetric LCL filter are contrasted, proving the effectiveness and feasibility of the proposed scheme. Furthermore, the experimental results demonstrate that the proposed magnetic integration scheme significantly reduces the filter's volume and weight, thereby enhancing the power density of the grid‐tied inverter system.

Multiple ion isolation and accumulation events for selective chemical noise reduction and dynamic range enhancement in MALDI imaging mass spectrometry.

Abundant chemical noise in MALDI imaging mass spectrometry experiments can impede the detection of less abundant compounds of interest. This chemical noise commonly originates from the MALDI matrix as well as other endogenous compounds present in high concentrations and/or with high ionization efficiencies. MALDI imaging mass spectrometry of biological tissues measures numerous biomolecular compounds that exist in a wide range of concentrations in vivo. When ion trapping instruments are used, highly abundant ions can dominate the charge capacity and lead to space charge effects that hinder the dynamic range and detection of lowly abundant compounds of interest. Gas-phase fractionation has been previously utilized in mass spectrometry to isolate and enrich target analytes. Herein, we have characterized the use of multiple continuous accumulations of selected ions (Multi CASI) to reduce the abundance of chemical noise and diminish the effects of space charge in MALDI imaging mass spectrometry experiments. Multi CASI utilizes the mass-resolving capability of a quadrupole mass filter to perform multiple sequential ion isolation events prior to a single mass analysis of the combined ion population. Multi CASI was used to improve metabolite and lipid detection in the MALDI imaging mass spectrometry analysis of rat brain tissue.

Adapting virus filtration to continuous processing: Effects of product and process variability on filtration performance.

Virus filtration (VF) is an important unit operation in the manufacture of biotherapeutics that provides robust removal of potential virus contaminants. Small virus removal can be impacted by the low operating pressures and potential depressurization events that are often associated with continuous operations where increased operational flexibility for higher loading at low flux and low pressure is required. In this study, we evaluated the impact of low flux (7 LMH) and pressure interruptions on minute virus of mice (MVM) removal. We used long-term filtrations conducted to a target throughput of 1000 L/m2 with four different monoclonal antibodies on small-scale hollow fiber virus filters with a hydrophilic modified polyvinylidene fluoride membrane. These conditions are certainly challenging for any VF operation and ensuring robust viral clearance under such conditions is critical to the design and implementation of continuous VF. Planova BioEX filters effectively removed MVM at 4 log or greater when run continuously for up to 6 days. Interestingly, pressure increases associated with filter fouling over the duration of long-term filtrations were shown to be reflective of load material variability and could be remediated by implementation of an inline prefilter. Pressure interruptions had minimal impact on overall MVM logarithmic reduction value. Effective virus removal was achieved with pressure increases being largely product-specific, which demonstrates the capability of the virus filter to remove virus independent of pressure increases that are expected to occur with increased protein load.

0.5-V 281-nW Versatile Mixed-Mode Filter Using Multiple-Input/Output Differential Difference Transconductance Amplifiers.

This paper presents a new low-voltage versatile mixed-mode filter which uses a multiple-input/output differential difference transconductance amplifier (MIMO-DDTA). The multiple-input of the DDTA is realized using a multiple-input bulk-driven MOS transistor (MI-BD-MOST) technique to maintain a single differential pair, thereby achieving simple structure with minimal power consumption. In a single topology, the proposed filter can provide five standard filtering functions (low-pass, high-pass, band-pass, band-stop, and all-pass) in four modes: voltage (VM), current (CM), transadmittance (TAM), and transimpedance (TIM). This provides the full capability of a mixed-mode filter (i.e., twenty filter functions). Moreover, the VM filter offers high-input and low-output impedances and the CM filter offers high-output impedance; therefore, no buffer circuit is needed. The natural frequency of all filtering functions can be electronically controlled by a setting current. The voltage supply is 0.5 V and for a 4 nA setting current, the power consumption of the filter was 281 nW. The filter is suitable for low-frequency biomedical and sensor applications that require extremely low supply voltages and nano-watt power consumption. For the VM low-pass filter, the dynamic range was 58.23 dB @ 1% total harmonic distortion. The proposed filter was designed and simulated in the Cadence Virtuoso System Design Platform using the 0.18 µm TSMC CMOS technology.

A Flexible Aptameric Graphene Field‐Effect Nanosensor Capable of Automatic Liquid Collection/Filtering for Cytokine Storm Biomarker Monitoring in Undiluted Sweat

AbstractA wearable sensor capable of directly detecting cytokines in external secretions external to the human body is pivotal for gauging the intensity of the immune response and circumventing inflammatory storms, a significant clinical concern. The detection of low‐concentration cytokines is often hampered due to interference from impurities in secretions. Addressing this challenge, a wearable detection system is developed by integrating a Janus membrane with water auto‐collection/filtering capabilities, and aptamer‐modified graphene field‐effect transistor sensors, enabling the detection of low‐concentration cytokines in undiluted sweat samples. The Janus membrane can automatically filter external secretions and transport them to the sensor surface, effectively eliminating impurity interference and enhancing the sensor's detection capabilities. The device can sensitively detect cytokines (such as TNF‐α, a marker for inflammation and cancer) in undiluted sweat, with a detection range spanning 0.5 to 500 pM, and a detection limit reaching 0.31 pM. The ultra‐flexibility of the sensor is also verified, maintaining stable electrical properties even after enduring up to 50 extreme deformations. These findings indicate that the wearable detection system has the potential to realize low‐concentration cytokines monitoring in the sweat of inflammatory patients, to assist the healthcare system in controlling the body's immune status.