Simple Filter Research Articles

A microstrip bandpass filter using coupled lines loaded by open stubs

A compact simple structure bandpass filter based on terminated three coupled line structures with high selectivity is introduced through odd and even-mode analysis. The proposed filter consists of three coupled lines loaded by open and short circuit stubs. The parallel-coupled lines structure is used to obtain high selectivity with transmission zeros near the passband. Meanwhile, simultaneously tuning on bandwidth and center frequency are introduced by coupled line. One type of bandpass filter based on terminated coupled line structure is theoretically analyzed, simulated, and measured. The stepped impedance structures at the input and output ports have been used to improve the attenuation level at the stopband region and create the multiple attenuation poles. The center frequency of 1.3 GHz, the fractional bandwidth (FBW) of about 65%, the low insertion loss of 0.25 dB, and the high return loss of 30 dB are obtained. Also, a flat group delay lower than 1.4 ns is achieved. Experimental and simulated results are presented, which are in good agreement.

Exploring Selective Fluorescence Turn-On Sensing of Caspase-3 with Molybdenum Disulfide Quenched Copper Nanoclusters: FRET Biosensor.

Sensing caspase-3 activity is essential for understanding the role of apoptosis in cancer dynamics, controlling therapeutic strategies, and improving patient care in cancer treatment. In this study, we demonstrate a highly sensitive recombinant human caspase-3 (rhC3) detection technique in biological fluids. This technique uses a copper nanocluster stabilized with bovine serum albumin (BSA-CuNCs) as a metal-based fluorescent biosensor, conjugated with anti-human caspase-3 (ahC3). To turn its fluorescence off, molybdenum disulfide nanosheets (MoS2 NSs) are added; this partnership is termed ahC3@BSA-CuNCs/MoS2 nanocouple. In the presence of rhC3, the energy transfer process is affected by strong ahC3/rhC3 interactions. When in close proximity, the rhC3 molecules cause detachment of the nanocluster from the MoS2 NS surface by attracting the ahC3 component of the nanocluster. This increases the distance between the nanocluster and quencher with a consequent restoration of intensity. As the concentration of rhC3 increases, the fluorescence intensity of the system also increases. A proportional response is seen in the concentration between 0.1 and 1.3 ng/mL with a very low limit of detection of 2.75 pg/mL and a quantification limit of 8.60 pg/mL. A simple filter paper strip was made to visually identify the presence of rhC3 under UV light.

SOSIALISASI DAN PELATIHAN PENYARING AIR SEDERHANA DALAM MEWUJUDKAN SISTEM AIR BERSIH DI DESA KARANG INDAH

The socialization and training of simple water filters in Karang Indah Village is an initiative that aims to improve community access to clean water. The program is designed to empower villagers with the knowledge and skills to build and operate simple water filters. The socialization conducted includes understanding the importance of clean water, the negative impacts of contaminated water, and the benefits of water filtration. Hands-on training focused on the construction and maintenance of water filters using local materials easily found in the village. Participatory and interactive methods were applied to ensure the active involvement of all levels of the community, including youth, housewives, and village leaders. Thus, Bojongmangu Village is expected to be self-sufficient in meeting its clean water needs, creating a healthier environment, and reducing the risk of water-related diseases. This program not only creates infrastructure, but also builds community capacity and awareness of the importance of clean and safe water sources.

A side-by-side comparison of variant function measurements using deep mutational scanning and base editing.

Variant annotation is a crucial objective in mammalian functional genomics. Deep Mutational Scanning (DMS) is a well-established method for annotating human gene variants, but CRISPR base editing (BE) is emerging as an alternative. However, questions remain about how well high-throughput base editing measurements can annotate variant function and the extent of downstream experimental validation required. This study presents the first direct comparison of DMS and BE in the same lab and cell line. Results indicate that focusing on the most likely edits and highest efficiency sgRNAs enhances the agreement between a "gold standard" DMS dataset and a BE screen. A simple filter for sgRNAs making single edits in their window could sufficiently annotate a large proportion of variants directly from sgRNA sequencing of large pools. When multi-edit guides are unavoidable, directly measuring the variants created in the pool, rather than sgRNA abundance, can recover high-quality variant annotation measurements in multiplexed pools. Taken together, our data show a surprising degree of correlation between base editor data and gold standard deep mutational scanning.

Implementation of Raspberry Pi As A2dp Module On The Quality of Song Reception of Humanoid Robot

This research discusses comparing the performance of Bluetooth communication on Raspberry Pi in controlling dance humanoid robots using external modules and direct communication. This research aims to understand the difference in performance between the two methods, by analyzing data transmission speed, signal stability, and communication latency that affect the quality of robot movement. In addition, this research also aims to evaluate the effectiveness of using simple filters such as low-pass filter circuits to improve the stability of Bluetooth communication on Raspberry Pi. It is hoped that this research can lead to a better understanding of the influence of both communication methods on the quality of dance humanoid robot movements.

Unbiased minimum-variance disturbance and state estimation for linear systems with both state and output disturbances

This paper addresses the problem of simultaneously estimating the states, the state disturbances and the output disturbances of a linear time variant system. The objective is to develop a simple but optimal filter in the unbiased minimum-variance sense without depending extra complex system decomposition transformation. The derived filter bases on the assumption that no prior knowledge about the dynamical evolution of these disturbances is available.

Extraction of crop canopy features and decision-making for variable spraying based on unmanned aerial vehicle LiDAR data

Implementation of precision agriculture aerial applications using unmanned aerial vehicles (UAVs) represents a pivotal technological aspect in precision agriculture. However, current UAV applications are still far from realizing true precision variable spraying. The use of UAVs to analyze and model crop growth, pest and disease conditions, and other pertinent information to enable targeted precision spraying poses a formidable challenge. Herein, a UAV variable-rate spray system with adaptive flow control based on crop canopy morphology was developed using a UAV equipped with a light detection and ranging (LiDAR) sensor to support precision spraying of cotton. Point cloud data captured by the LiDAR sensor initially undergo processing using the simple morphological filter (SMRF) algorithm and feature extraction function. Subsequently, cotton canopy volumes measured by the alpha shape algorithm, convex hull by slices shape algorithm, and voxel-based method were compared with manual measurements with coefficient of determination R2 values of 0.89, 0.78, and 0.82, respectively. The results of the validation experiments denote that the cotton canopy volume obtained using the alpha shape algorithm is in good agreement with that obtained using manual measurements; thus, this algorithm is employed for volume calculations in this design. Models were constructed to relate UAV flight parameters, cotton canopy volume, and spraying parameters to the duty cycle of variable control signals, which enabled the UAV to adjust the flow rate in real time to match the cotton canopy structure. In addition, a polygonal prescription map decoding algorithm based on the cotton canopy morphology was introduced. This algorithm is used for real-time decoding of prescription maps and extraction of flow, latitude, and longitude information. Finally, field experiments were conducted, and the results indicate that the UAV variable-rate spray system designed in this study exhibits biological efficacy (effective application thresholds provided by the pesticide manufacturer) comparable to that of the constant-rate spray system at a lower application rate. Furthermore, it exhibited superior spray deposition efficiency and coverage compared with the constant-rate spray system. Notably, the use of the variable-rate spray system in cotton crops resulted in a 43.37% reduction in spray volume while maintaining high application quality, providing an opportunity for pesticide and labor cost savings.

A multifunctional fluorescence sensor for DNT vapor and Fe3+ based on a conjugated polymer

Due to the molecular wire effect, the fluorescent conjugated polymer (FCP) is always one of the focus of the sensor field. However, the complicated synthesis of FCP usually hinders their further practical application. Meanwhile, the multifunctional fluorescence sensor, which can distinguish multi-analyte only by one sensing material, presents high potential prospect in the real application. Here, a relatively simple benzimidazole-based FCP P1 via Suzuki polymerization was synthesized and further used as a multifunctional fluorescence sensor: P1 can not only efficiently detect Fe3+ in solution with a limit of detection (LOD) of 60 nM, but also monitor 2, 4-dinitrotoluene (DNT) vapor with a low LOD of 3.3 ppb. Furthermore, the simple P1 filter paper sensor was prepared and enable visual detection of Fe3+ and DNT with high performances of portability, flexibility and practicability.

A Complex-Valued Stationary Kalman Filter for Positive and Negative Sequence Estimation in DER Systems

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the positive sequence voltage. However, positive and negative sequences are conventionally defined in a steady state and are not directly observed from the instantaneous voltage and current measurements at the DER unit’s point of connection. In this article, an estimation algorithm for sequence separation based on the Kalman filter is proposed. Furthermore, the proposed filter uses a complex vector representation of the asymmetric three-phase signals in synchronous coordinates to allow for the implementation of the Kalman filter in its stationary form, resulting in a simple dynamic filter able to estimate positive and negative sequences even during transient operation. The proposed stationary complex Kalman filter performs better than state-of-the-art techniques like DSOGI and very similarly to other Kalman filter implementations found in the literature but at a fraction of its computational cost (23.5%).

Application of Appropriate Technology for Making Organic NPK Fertilizer by Utilizing Palm Oil Mill Waste in Bekiung Village

Bekiung is one of the villages in Langkat Regency. Farmers in this village generally cultivate a lot of oil palm plants. The waste is quite large and causes environmental pollution problems. On the other hand, experience problems in the form of scarcity and expensive synthetic fertilizers, subsidized fertilizers that are no longer available, and less fertile soil. This is due to the lack of knowledge and skills of farmers, limited advice and infrastructure in waste processing. The aim of this activity is to increase farmers' knowledge and skills in processing palm oil waste into organic NPK fertilizer, open farmers' insight into the importance of organic fertilizer as a diversification of synthetic fertilizer which is beneficial for soil properties and increase the independence of partners in waste processing. The solution offered to overcome this problem is the introduction of appropriate technology and processing palm oil factory waste into organic NPK fertilizer. The methods used in this activity are socialization and direct practice in the field. The results obtained were the introduction and delivery of technology transfer equipment in the form of a hummer mill, conveyor and simple filter, training and assistance in making organic NPK fertilizer up to packaging. The conclusion of this activity is that it is a beneficial activity for partners because it increases the understanding and independence of partners in processing palm oil mill waste into organic NPK fertilizer which can overcome the problem of the availability of scarce and expensive fertilizer.

Instability Compensation of Recording Interferometer in Phase-Sensitive OTDR.

In the paper, a new method of phase measurement error suppression in a phase-sensitive optical time domain reflectometer is proposed and experimentally proved. The main causes of phase measurement errors are identified and considered, such as the influence of the recording interferometer instabilities and laser wavelength instability, which can cause inaccuracies in phase unwrapping. The use of a Mach-Zender interferometer made by 3 × 3 fiber couplers is proposed and tested to provide insensitivity to the recording interferometer and laser source instabilities. It is shown that using all three available photodetectors of the interferometer, instead of just one pair, achieves significantly better accuracy in the phase unwrapping. A novel compensation scheme for accurate phase measurements in a phase-sensitive optical time domain reflectometer is proposed, and a comparison of the measurement signals with or without such compensation is shown and discussed. The proposed method, using three photodetectors, allows for very good compensation of the phase measurement errors arising from common-mode noise from the interferometer and laser source, providing a significant improvement in signal detection. In addition, the method allows the tracking of slow temperature changes in the monitored fiber/object, which is not obtainable when using a simple low-pass filter for phase unwrapping error reduction, as is customary in several systems of this kind.

Design of Miniaturized 90-Degree Hybrid Coupler with Wide Rejection Band Using Neural Network.

In this paper, a 3 dB 90-degree hybrid coupler with size reduction and harmonics rejection was designed. In the proposed coupler structure, four simple low-pass filters (LPFs) were applied. An artificial neural network (ANN) was used to determine the dimensions of the applied LPFs based on EM simulation data. The applied ANN model could also provide the desired LPF parameters, including the cut-off frequency (fc), bandwidth (BW), and insertion loss (IL). Designing an applied LPF involves complex mathematical calculations and simulations to optimize parameters. However, by utilizing neural networks, the design process can be significantly streamlined and automated. Neural networks have the ability to learn complex patterns and relationships within data, making them well suited for optimizing the performance of applied components. The proposed 90-degree hybrid coupler works correctly at 1800 MHz and has a small size of 16.6 mm × 15.15 mm, which provides a 73% size reduction compared to a normal 1800 MHz coupler. The designed coupler not only decreases the circuit size but also provides a wide rejection band from 4.8 GHz to 11.2 GHz, which suppresses the second to sixth harmonics. The insertion loss parameter of this 90-degree hybrid coupler is less than 0.1 dB at the working frequency, which shows the superior performance of the proposed coupler.

Novel Illumination-invariant Face Recognition Approach via Reflectance-luminance and Local Matching Model with Weighted Voting System

In this study, a novel approach has been introduced for face recognition that is unaffected by changes in illumination. This method is based on the reflectance-luminance model and incorporates local matching using a weighted voting technique to eliminate any artifacts present in the retina images. A total of 37 different linear and nonlinear filters were tested, including high pass and low pass filters, to achieve the reflectance component of the image, which remains invariant to changes in illumination. Among these filters, the maximum filter, which is a simple filter with low computational complexity, yielded the best results in extracting the illumination invariants. It was observed that the illumination invariants obtained through this method outperformed other methods such as QI, SQI, and image enhancement techniques in terms of recognition accuracy. Importantly, the proposed method does not require any prior knowledge about the facial shape or illumination conditions and can be applied to individual images independently. Unlike many existing methods, this approach does not rely on multiple images during the training stage and does not require any parameter selection to generate the illumination invariants. To further enhance the robustness of illumination, a weighted voting system was introduced. Certain regions of the image, which may adversely affect the recognition outcome due to poor illumination, occlusion, noise, or lack of distinctive information, were identified using predefined factors such as grayscale mean, image entropy, and mutual information. The proposed method was also compared to other face recognition methods in the presence of occlusions, and it demonstrated promising results outperforming existing methods. The Python algorithm successfully detects obstructed faces, genders, and ages in videos with a face matching accuracy between 80.9% to 96.9% based on proximity.

IR detection module with integrated real-time FIR filter implemented in FPGA

Infrared detectors are usually characterized by 1/f noise when operating with biasing. This type of noise significantly reduces detection capabilities for low-level and slow signals. There are a few methods to reduce the influence of 1/f noise, like filtering or chopper stabilization with lock-in. Using the first one, a simple 1st-order analog low-pass filter built-in amplifier usually cuts off 1/f noise fluctuations at low frequencies. In comparison, the stabilization technique modulates the signal transposing to a higher frequency with no 1/f noise and then demodulates it back (lock-in amplifiers). However, the flexible tuned device, which can work precisely at low frequencies, is especially desirable in some applications, e.g., optical spectroscopy or interferometry. The paper describes a proof-of-concept of an IR detection module with an adjustable digital filter taking advantage of finite impulse response type. It is based on the high-resolution analog-to-digital converter, field-programmable gate array, and digital-to-analog converter. A microcontroller with an implemented user interface ensures control of such a prepared filtering path. The module is a separate component with the possibility of customization and can be used in experiments or applications in which the reduction of noises and unexpected interferences is needed.

Abstract 91: Comprehensive 27-marker standard panel for immune monitoring of pre-clinical tumor mouse models using spectral analyzer technology

Abstract Within the last decade, major technological advances in flow cytometry and (single cell) RNA-sequencing have deepened our understanding of complex anti-tumoral immune responses. This allows a comprehensive immune monitoring of novel therapies in pre-clinical models. Conventional flow cytometry is reaching its technical limitation. The emitted fluorescence signal of the target population stained with antibodies is evaluated with simple bandpass filters, which leads to spectral overlap and thus the number of parameters that can be analyzed simultaneously is limited. The spectral analyzer technology combines both methods, providing great flexibility in evaluating different immune cell populations. This provides the opportunity to obtain target information by measuring the entire fluorescence spectrum measuring each wavelength individually and thus evaluating the true signal of each fluorochrome unaffected by autofluorescence or spillover. Our standard all-in-one flow cytometry panel uses the full capacity of a conventional BD Fortessa flow cytometer and enables the differentiation of important immune cell populations in tumors, such as T cells (CD4+, CD8+, regulatory T cells), B and NK cells as well as macrophages (M1/M2), MDSCs (granulocytes and monocytes) and dendritic cells. However, it would be of great advantage to gain further insights into activation and effector functions of the immune cells using markers like CD44 & CD62L, CD69, PD-1, Lag-3, Tim-3 and PDL-1, CD80, CD86, CD40 and Ki-67. The Sony ID7000 spectral analyzer enables the simultaneous assessment of 27 markers to evaluate immune phenotypes, with the ability to include up to 4 individual target antibodies. Flow cytometry data from various tumor models are presented, demonstrating the enormous utility and potential of the spectral analyzer, especially with limited and precious tumor sample material, by evaluating different immune cell populations simultaneously without reaching its technical limits. The spectral analyzer technology enables us to mechanistically investigate differences in tumor therapies such as PD-1 treatment or other immune cell modulators not only phenotypically but also with regard to activation and differentiation of immune cell populations. Citation Format: Jonas F. Hummel, Philipp Metzger, Roland Sonntag, Dennis Borrero-Wolff, Cynthia Obodozie, Holger Weber. Comprehensive 27-marker standard panel for immune monitoring of pre-clinical tumor mouse models using spectral analyzer technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 91.

A simple and efficient filter feature selection method via document-term matrix unitization

Text processing tasks commonly grapple with the challenge of high dimensionality. One of the most effective solutions to this challenge is to preprocess text data through feature selection methods. Feature selection can select the most advantageous features for subsequent operations (e.g., classification) from the native feature space of the text. This process effectively trims the feature space’s dimensionality, enhancing subsequent operations’ efficiency and accuracy. This paper proposes a straightforward and efficient filter feature selection method based on document-term matrix unitization (DTMU) for text processing. Diverging from previous filter feature selection methods that concentrate on scoring criteria definition, our method achieves more optimal feature selection by unitizing each column of the document-term matrix. This approach mitigates feature-to-feature influence and reinforces the role of the weighting proportion within the features. Subsequently, our scoring criterion subtracts the sum of weights for negative samples from positive samples and takes the absolute value. We conduct numerical experiments to compare DTMU with four advanced filter feature selection methods: max–min ratio metric, proportional rough feature selector, least loss, and relative discrimination criterion, along with two classical filter feature selection methods: Chi-square and information gain. The experiments are performed on four ten-thousand-dimensional feature space datasets: book, dvd, music, movie and two thousand-dimensional feature space datasets: imdb, amazon_cells, sourced from Amazon product reviews and movie reviews. Experimental findings demonstrate that DTMU selects more advantageous features for subsequent operations and achieves a higher dimensionality reduction rate than those of the other six methods used for comparison. Moreover, DTMU exhibits robust generalization capabilities across various classifiers and dimensional datasets. Notably, the average CPU time for a single run of DTMU is measured at 1.455 s.

Grafite: Taming Adversarial Queries with Optimal Range Filters

Range filters allow checking whether a query range intersects a given set of keys with a chance of returning a false positive answer, thus generalising the functionality of Bloom filters from point to range queries. Existing practical range filters have addressed this problem heuristically, resulting in high false positive rates and query times when dealing with adversarial inputs, such as in the common scenario where queries are correlated with the keys. We introduce Grafite, a novel range filter that solves these issues with a simple design and clear theoretical guarantees that hold regardless of the input data and query distribution: given a fixed space budget of B bits per key, the query time is O(1), and the false positive probability is upper bounded by l/2B-2, where l is the query range size. Our experimental evaluation shows that Grafite is the only range filter to date to achieve robust and predictable false positive rates across all combinations of datasets, query workloads, and range sizes, while providing faster queries and construction times, and dominating all competitors in the case of correlated queries. As a further contribution, we introduce a very simple heuristic range filter whose performance on uncorrelated queries is very close to or better than the one achieved by the best heuristic range filters proposed in the literature so far.

A Comparison of 5 Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of 5 Different Vital Signs: A Retrospective Analysis.

Research on electronic health record physiological data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized different artifact detection algorithms, including machine learning, may be necessary to provide optimal performance for various vital signs and clinical contexts. In a retrospective single center study, intraoperative OR and ICU electronic health record datasets including heart rate, oxygen saturation, blood pressure, temperature, and capnometry were included. All records were screened for artifacts by at least two human experts. Classical artifact detection methods (cutoff, multiples of standard deviation (z-value), interquartile range, and local outlier factor) and a supervised learning model implementing long short-term memory neural networks were tested for each vital sign against the human expert reference dataset. For each artifact detection algorithm, sensitivity and specificity were calculated. A total of 106 (53 operating room and 53 ICU) patients were randomly selected, resulting in 392,808 data points. Human experts annotated 5,167 (1.3%) data points as artifacts. The artifact detection algorithms demonstrated large variations in performance. The specificity was above 90% for all detection methods and all vital signs. The neural network showed significantly higher sensitivities than the classic methods for: heart rate (ICU: 33.6%, 95% CI: 33.1-44.6), systolic invasive blood pressure (both in the OR (62.2%, 95% CI: 57.5-71.9) and ICU (60.7%, 95% CI: 57.3-71.8), and temperature in the OR (76.1%, 95% CI: 63.6-89.7). The confidence intervals for specificity overlapped for all methods. Generally, sensitivity was low, with only the z-value for oxygen saturation in the operating room reaching 88.9%. All other sensitivities were less than 80%. No single artifact detection method consistently performed well across different vital signs and clinical settings. Neural networks may be a promising artifact detection method for specific vital signs.

Model robustness for feedback stabilization of open quantum systems

This paper builds upon and expands the findings presented in Liang et al. (2021). Our focus lies in scenarios where initial states and model parameters remain unknown. In contrast to prior research, we consider multiple measurement channels, addressing the challenge of global exponential stabilization towards a chosen target subspace. Under quantum non-demolition conditions, our analysis establishes essential tools for achieving feedback stabilization. Furthermore, we explore the utilization of a simplified filter as a viable alternative to full state estimator. While the effectiveness of this simplified filter was numerically examined in Cardona et al. (2020), our paper offers a comprehensive proof of its applicability in feedback stabilization. Furthermore, our analysis may serve as an inspiration for demonstrating the validation of the robust feedback stabilization through generic approximate filters.

A simple filter Lorenz electronic circuit

In this work, an electronic circuit of the Lorenz system is developed. The electronic circuit proposed is one of the easiest to implement. We changed the x-state equation of Lorenz‘s system with a low-pass filter to an RC circuit with the same cutoff frequency. Corron‘s electronic circuit is used as the basis for electronic design. Simulation results support this proposal.