Bandpass filtering

This paper presents a simultaneous high-Q impedance matching and bandpass filtering technique for low-noise amplifiers (LNA) for radio frequency integrated circuit (RFIC) phased array system at 15 GHz, over the bandwidth of 1 GHz. A non-uniform transmission line (NUTL) segment is used as a major building block, which provides feasible integration for multiple antennas. A feeding network for single element receiver is demonstrated. The simulated and measured results show sufficient impedance matching of input feeds with S11 < -10 dB over a relative bandwidth of 7% at 15 GHz.

Detecting the presence of alcohol in individuals poses a significant challenge due to the limitations of conventional devices that rely on odor, which is not always reliable. Electroencephalography (EEG), a widely-used technique for measuring brain activity, has emerged as a promising tool for evaluating subjects with alcoholism. The present study intends to use various types of linear and nonlinear analysis of the EEG signal to classify alcoholics and non-alcoholics and provide a direct comparison of the efficiency of each of the analysis methods. After EEG preprocessing, spectral analysis was done to calculate linear features. Then, some nonlinear features were calculated through fractal dimension, entropy analysis, Hurst exponent, Lempel-Ziv complexity, and detrended fluctuation analysis. Feature classification was done through KNN, Naïve Bayes, and AdaBoost classifiers. The suggested methods were assessed on a publicly available UCI alcoholic EEG database. Experimental results showed that linear and nonlinear features achieved an accuracy of 74.96% and 93.62%, respectively, for the EEG classification of alcoholics and non-alcoholics. Furthermore, Katz fractal dimension had a high accuracy of 95.74%, sensitivity of 98.82%, and specificity of 92.20% in distinguishing EEG signals of alcoholics and non-alcoholics. The findings showed that nonlinear features perform better than linear features for alcoholism detection. Therefore, it is recommended to use and investigate nonlinear signal processing methods in future studies for the detection of alcoholic EEG.

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.

In this manuscript, serial-shunt of square ring resonators with step-impedance open circuited stub resonators to produce a new on-off switchable bandpass to bandstop response in the same ultra-wideband microstrip filter structure is proposed. The closed ring of series-shunt square ring resonators with a combine stubs are introduced to excite the bandpass response while bandstop characteristic excited when gaps are embedded in the corners of the square ring resonators. The main advantage of this microstrip filter is its capability to switch from bandpass-to-bandstop operation using open-short gap, respectively. A microwave simulator is utilized to show the switchable case by replacing Skyworks radio frequency diodes (RF-PIN) instead of those gaps. The entire filter models have been simulated using the computer simulation technology (CST) Microwave Studio. The computed results for the proposed filters were compared with the measured results of the both prototype structures (bandpass- ...

This paper contributes to the literature by demonstrating the superiority of machine learning models like Neural Networks and Random Forests over traditional statistical methods in business analytics. It offers empirical evidence using metrics such as accuracy, ROC curves, and precision-recall, showcasing their applicability in diverse business contexts. By emphasizing parameter optimization, such as batch size and learning rate, it provides actionable insights for achieving high accuracy, up to 90%. The paper also addresses ethical considerations and model explainability, aligning ML advancements with responsible AI practices. Its focus on improving usability and efficiency bridges the gap between ML theory and real-world business applications.

This paper has investigated WLAN notched Ultra-Wide Band (UWB) Band Pass Filter with a wide upper stop band. The proposed design has folded half-wave line that has been incorporated in between slot line resonators. Designed UWB band pass filter has 3-dB cutoff frequencies at 3.2 GHz and 10.1 GHz with a notched band from 5.1 GHz to 5.4 GHz. Return loss and insertion loss in the first pass band have been greater than 12 dB and less than 1.5 dB. These have been greater than 25 dB and lesser than 1.8 dB in the second pass band. In the upper transition edge, an introduced filter has accomplished pointed roll-off with attenuation of 62 dB, wide stop band with the rejection of 20 dB till 12 GHz, and greater than 17 dB from 12 GHz to 18 GHz. Higher-order harmonics have been suppressed through quarter wavelength open stubs. The overall size of the filter is 27 x 9 x 0.8 mm3.

the first time in this article. By using a dual-mode ring resonator and novel parallel-coupled input/output feed structure, an ultra-wideband bandpass filter is realized. After optimization of the design and process, the filter is fabricated on liquid crystal polymer substrates using standard processing technology. Simulations and measurements are compared and discussed. The fabricated filter exhibits a return loss lever better than 20 dB, an insertion loss of 8 dB, and a 3-dB bandwidth of 25%, which agreed well with simulated results.

Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru&#39;s Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.

Many diseases are increasing day by day and it takes too much time to detect. In India after Covid-19 pandemic so many diseases have been spread their era. Like   Liver Disease, Lung cancer and Brain Stroke. They are among us and lethal diseases which need to predict earlier or in initial stage. Machine Learning (ML) is the subset of Artificial intelligent which can imitate like human intelligence and it can process the large information.  The classification or prediction of those diseases can be done by classifiers.  The disease prediction is the method which can predict future of Liver diseases, Lung Cancer and Brain Stroke possibilities based on the collection of historical dataset. In this paper we will use Hybrid Ensemble Classifier Model (HECM) which  is the combination of Supervised Classifiers like LightGBM, Random Forest, KNN used as Ensemble  Classifier then output given to Voting classifier for final output. Accuracy and time will be calculate

This paper presents a novel reconfigurable bandpass filter with three reconfigurable states used for C band wireless applications. The frequency reconfigurable is achieved using the combination of a different microstrip coupled resonator structure and switching device as PIN diodes. The open-loop filter structure provides three narrow band states at 5.1, 5.2, and 5.8 GHz. The frequency reconfiguration is obtained without compromising performances. The compact size of the proposed designed along with the targeted frequency bands at lower wireless local area network (WLAN) (5.1, 5.2 GHz), and worldwide interoperability for microwave access (WiMAX) (5.8 GHz) applications. The prototype is constructed on an RT duroid 5880 substrate and tested for validation in vector network analyzer (VNA). The designed filter provides excellent selectivity and good rejection at desired resonant frequencies.

A second-order dual-band BPF located at 1.8/5.2 GHz by using asymmetrical SLR is first proposed. Then, two different sets of half-wavelength resonators are introduced to produce another two passbands at 3.5 and 6.8 GHz, so that a second-order quadband BPF is successfully designed. The designed second-order dual-band BPF and quad-band BPF has compact sizes, good inband and out-of-band performance. All these merits make them attractive in modern multiband communication system.

In this article, the passband, and stopband of quarter wavelength stubs-based Band Pass Filter (BPF) are controlled by a straightforward and new method. This method depends on inserting an attenuation pole and tuning the passband by a Circular Slot Ring Resonator (CSRR) and rectangular slot beneath the BPF’s stubs. Hence, controlling the passband width without any further area usage is achieved. The stop-band rejection level and bandwidth are controlled by inserting Band Stop Filter (BSF) after the BPF such that the used rectangular slots beneath the stubs control the stop-band bandwidth. For verification, third-order Chebyshev BPF and Butterworth BSF filters are used. The proposed filter passband is chosen to cover the sub-6 GHz different 5G bands. The proposed BPF has an ultra-wide 3 dB passband of 2.97 GHz (2.35 GHz-5.32 GHz) and a 20 dB stopband of 4.59 GHz (6.18-10.77 GHz). The proposed BPF filter is fabricated, measured, and the results are in good agreement with their simulat...

In this manuscript, serial-shunt of square ring resonators with step-impedance open circuited stub resonators to produce a new on-off switchable bandpass to bandstop response in the same ultra-wideband microstrip filter structure is proposed. The closed ring of series-shunt square ring resonators with a combine stubs are introduced to excite the bandpass response while bandstop characteristic excited when gaps are embedded in the corners of the square ring resonators. The main advantage of this microstrip filter is its capability to switch from bandpass-to-bandstop operation using open-short gap, respectively. A microwave simulator is utilized to show the switchable case by replacing Skyworks radio frequency diodes (RF-PIN) instead of those gaps. The entire filter models have been simulated using the computer simulation technology (CST) Microwave Studio. The computed results for the proposed filters were compared with the measured results of the both prototype structures (bandpass- ...

Alcohol is a serious toxic substance that alters brain function by interfering with neuron processes in the central nervous system, leading to mental and behavioural disorders. Alcoholism has serious pathological effects on the liver, immune system, brain, and heart. Alcoholism diagnosis is critical not only because of the disease’s impact on individuals and society but also because of the financial cost to the national health system since many people suffer from it around the world. Electroencephalogram (EEG) signals of normal and alcohol can be classified automatically to diagnose these illnesses, such that this work contains a simple and very fast prediction system. The method uses a bandpass filter to remove all unused signal frequencies, it can be showed in correlation matrices. Therefore, the use of machine learning (ML) algorithms in this work make it possible to quickly generate prediction models with better accuracy values, in addition to the improvement of the algorithmic parameters of classifier and bandpass filter using major optimizers like Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), the elaborate model precision values have been increased from 63.9% to 99.95% without the extraction of EEG signal characteristics. To minimize the number of the electrodes and remained good accuracy values, this work uses the Extra-Trees (ET) algorithm, such as with only four electrodes the accuracy value remains higher of 99%. A comparison with other techniques was performed aiming to validate our approach, and it shows great efficiency, simplicity, and instantly.

— A new approach to design a dual-band RLC circuits is proposed. In this approach, a single-band circuit is designed and then converted to a dual-band circuit by substituting its inductors and capacitors for a proper parallel or series LC resonator. The validity of the proposed approach is verified by designing, implementing and measuring a dual-band RLC power divider at frequencies 10 and 20 MHz as well as a dual-band tank circuit. The proposed approach can be used for designing arbitrary dual-band circuits such as narrowband bandpass filters, bandstop filters, power dividers and combiners, phase shifters etc.

In this manuscript, serial-shunt of square ring resonators with step-impedance open circuited stub resonators to produce a new on-off switchable bandpass to bandstop response in the same ultra-wideband microstrip filter structure is proposed. The closed ring of series-shunt square ring resonators with a combine stubs are introduced to excite the bandpass response while bandstop characteristic excited when gaps are embedded in the corners of the square ring resonators. The main advantage of this microstrip filter is its capability to switch from bandpass-to-bandstop operation using open-short gap, respectively. A microwave simulator is utilized to show the switchable case by replacing Skyworks radio frequency diodes (RF-PIN) instead of those gaps. The entire filter models have been simulated using the computer simulation technology (CST) Microwave Studio. The computed results for the proposed filters were compared with the measured results of the both prototype structures (bandpass- and bandstop-filter). The codes also showed good agreement between them. Other advantages include being small in size, and low in effective cost.

Digital filtering is considered to be an important operation in reconstruction and visualization of information, besides amounting to increase in computational efficiency. The Finite Impulse Response based digital filter bank is better responsive in respect of above specified advantages. In this paper, we propose a novel approach in digital bandpass filter bank through Modified Kaiser window based Multirate technique as specified in (1)(2). A remarkable spectral output is achieved by way of increase in peak response, better selectivity, more stopband attenuation, better frequency response and computational efficiency as compared to the existing methods(3)(4)(5). The simulation results are exhibited on account of satisfactory performance and comparison is drawn to verify the advantages in the proposed method. This type of filter bank is more suitable in typical hearing aid applications in order to arrive significant merits for improving quality of output.