Improvement in the DOA estimation of broadband signals usingpartially IIR beamformers

An ideal broadband beamformer requires an increased number of taps for each filter, which in turn increases the complexity of the design. It is shown that for angles near the broadside of the antenna the beamforming is more accurate compared to the angles near the endfire, and it is advantageous to use recursive filters combined with a computed number of zeros for angles near the endfire. The proposed method is used for estimating the arrival angle of the incoming signals and displays uniform accuracy for all angles     

A Novel UWB Pulse Shape Modulation System

In this paper novel modified Hermite polynomial functions for use in impulse radio (ultra-wideband) communications are proposed. With these functions pulse shapes which are orthogonal and have nearly constant pulse width regardless of the pulse order are generated. These properties hold under the effects of differentiation. An M-ary communication system is constructed using these pulse shapes. A Matlab model for generating the pulses is designed and the effect of timing jitter on the performance of the system is investigated by computer simulation.     

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

Glyphosate Adsorption from Water Using Hierarchically Porous Metal–Organic Frameworks

The selective removal of one ligand in mixed-ligand MOFs upon thermolysis provides a powerful strategy to introduce additional mesopores without affecting the overall MOF structure. By varying the initial ligand ratio, MOFs of the MIL-125-Ti family with two distinct hierarchical pore architectures are synthesized, resembling either large cavities or branching fractures. The performance of the resulting hierarchically porous MOFs is evaluated toward the adsorptive removal of glyphosate (N-(phosphonomethyl)glycine) from water, and the adsorption kinetics and mechanism are examined. Due to their strong affinity for phosphoric groups, the numerous Ti–OH groups resulting from the selective ligand removal act as natural anchor points for effective glyphosate uptake. The relationships between contact duration, glyphosate concentration, and adsorbent dosage are investigated, and the impact of these parameters on the effectiveness of glyphosate removal from contaminated water samples is examined. The introduction of additional mesopores has increased the adsorption capacities by nearly 3 times with record values exceeding 440.9 mg g⁻¹, which ranks these MOFs among the best-reported adsorbents.     

Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model

Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

An Overview on SARS‑CoV‑2(COVID‑19)and Other Human Coronaviruses and Their Detection Capability via Amplification Assay,Chemical Sensing,Biosensing,Immunosensing,and Clinical Assays

A novel coronavirus of zoonotic origin(SARSCoV-2)has recently been recognized in patients with acute respiratory disease.COVID-19 causative agent is structurally and genetically similar to SARS and bat SARS-like coronaviruses.The drastic increase in the number of coronavirus and its genome sequence have given us an unprecedented opportunity to perform bioinformatics and genomics analysis on this class of viruses.Clinical tests like PCR and ELISA for rapid detection of this virus are urgently needed for early identification of infected patients.However,these techniques are expensive and not readily available for point-of-care(POC)applications.Currently,lack of any rapid,available,and reliable POC detection method gives rise to the progression of COVID-19 as a horrible global problem.To solve the negative features of clinical investigation,we provide a brief introduction of the general features of coronaviruses and describe various amplification assays,sensing,biosensing,immunosensing,and aptasensing for the determination of various groups of coronaviruses applied as a template for the detection of SARS-CoV-2.All sensing and biosensing techniques developed for the determination of various classes of coronaviruses are useful to recognize the newly immerged coronavirus,i.e.,SARS-CoV-2.Also,the introduction of sensing and biosensing methods sheds light on the way of designing a proper screening system to detect the virus at the early stage of infection to tranquilize the speed and vastity of spreading.Among other approaches investigated among molecular approaches and PCR or recognition of viral diseases,LAMP-based methods and LFAs are of great importance for their numerous benefits,which can be helpful to design a universal platform for detection of future emerging pathogenic viruses.