A Novel Sample Based Quadrature Phase Shift Keying Demodulator

This paper presents a new practical QPSK receiver that uses digitized samples of incoming QPSK analog signal to determine the phase of the QPSK symbol. The proposed technique is more robust to phase noise and consumes up to 89.6% less power for signal detection in demodulation operation. On the contrary, the conventional QPSK demodulation process where it uses coherent detection technique requires the exact incoming signal frequency; thus, any variation in the frequency of the local oscillator or incoming signal will cause phase noise. A software simulation of the proposed design was successfully carried out using MATLAB Simulink software platform. In the conventional system, at least 10 dB signal to noise ratio (SNR) is required to achieve the bit error rate (BER) of 10−6, whereas, in the proposed technique, the same BER value can be achieved with only 5 dB SNR. Since some of the power consuming elements such as voltage control oscillator (VCO), mixer, and low pass filter (LPF) are no longer needed, the proposed QPSK demodulator will consume almost 68.8% to 99.6% less operational power compared to conventional QPSK demodulator

convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images has been used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among all images of the dataset, 3896 and 977 images are applied to train and test the convoHER2 model, respectively. As all the images are in high resolution, we resize them so that we can feed them in our convoHER2 model. The cancerous samples images are classified into four classes based on the stage of the cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future

Synthesis, Characterization and Biological Activity of Oxovanadium(IV) Complexes Containing α-Amino Acid Schiff Bases and 5,6-Dimethyl-1,10-phenanthroline Ligands

Five oxovanadium(IV) complexes of the type [VO(L)(DPhen)], containing Schiff base derived from α-amino acid, [where L = 3-hydroxybenzaldehyde-α-alanine (hb-Ala), 3-hydroxybenzaldehyde-DL-phenylalanine (hb-Phe), 3-hydroxybenzaldehyde-leucine (hb-Leu), 3-hydroxybenzaldehyde-glycine (hb-Gly) and 3-hydroxybenzaldehyde-DL-methionine (hb-Met) & DPhen = 5,6-Dimethyl-1,10-phenanthroline] have been synthesized and characterized by some physicochemical properties, molar conductance, magnetic susceptibilities measurements, elemental analysis, UV-Visible, FT-IR and EIS-MS spectral studies. The molar conductance values evidenced the non-electrolytic nature of the complexes. The magnetic moment values of the complexes are in accordance with the d1 electronic configuration of the VIVO2+ moiety and indicates the paramagnetic behavior of the complexes. IR spectral data indicates the coordination of tridentate amino acid Schiff base ligands to the vanadyl (VO2+) ion through O, N, O-donor. ESI-MS spectral study confirmed the proposed structure of the complexes. All the analytical data suggested that all the complexes possess to have distorted octahedral geometry. The complexes were screened for their antibacterial activity against four human pathogenic bacteria; two Gram positive Escherichia coli & Pseudomonas aeruginosa and two Gram negative Staphylococcus aureus & Bacillus cereus with Kanamycin (K-30) standard. The result shows that all the complexes have moderate to strong potential antibacterial activity against all the pathogenic bacteria. This work is licensed under a Creative Commons Attribution 4.0 International License

Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography

The objective of the present study was to investigate the time to fatigue and compare the fatiguing condition among the three heads of the triceps brachii muscle using surface electromyography during an isometric contraction of a controlled forceful hand grip task with full elbow extension. Eighteen healthy subjects concurrently performed a single 90 s isometric contraction of a controlled forceful hand grip task and full elbow extension. Surface electromyographic signals from the lateral, long and medial heads of the triceps brachii muscle were recorded during the task for each subject. The changes in muscle activity among the three heads of triceps brachii were measured by the root mean square values for every 5 s period throughout the total contraction period. The root mean square values were then analysed to determine the fatiguing condition for the heads of triceps brachii muscle. Muscle fatigue in the long, lateral, and medial heads of the triceps brachii started at 40 s, 50 s, and 65 s during the prolonged contraction, respectively. The highest fatiguing rate was observed in the long head (slope = -2.863), followed by the medial head (slope = -2.412) and the lateral head (slope = -1.877) of the triceps brachii muscle. The results of the present study concurs with previous findings that the three heads of the triceps brachii muscle do not work as a single unit, and the fiber type/composition is different among the three heads

A Literature Review On NoSQL Database For Big Data Processing

Abstract Objective:Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural issues and the real-time data mining techniques to extract the estimated valuable information.Methods:We searched the Springer Link and IEEE Xplore online databases for articles published in English language during the last seven years (between January 2011 and December 2017).We specifically searched for two keywords (“NoSQL” and “Big Data”) to find the articles.The inclusion criteria were articles on the use of performance comparison on valuable information processing in the field of Big Data through NoSQL databases.Results:In the 18 selected articles,this review identified 8 articles which provided various suitable recommendations on NoSQL databases for specific area focus on the value chain of Big Data,5 articles described the performance comparison of different NoSQL databases, 2 articles presented the background of basics characteristics data model for NoSQL,1 article denoted the storage in respect of cloud computing and 2 articles focused the transactions of NoSQL.Conclusion:In this literature,we presented the NoSQL databases for Big Data processing including its transactional and structural issues. Additionally, we highlight research directions and challenges in relation to Big Data processing. Therefore,we believe that the information contained in this review will incredible support and guide the progress of the Big Data processing

Cationic nickel metal-organic frameworks for adsorption of negatively charged dye molecules

Industrial dye effluents with low biodegradability are highly toxic and carcinogenic on both human and aquatic lives, thus they are detrimental to the biodiversity of environment. Herein, this data set presents the potential of cationic Nickel based MOFs in the adsorption of charged and neutral dye molecules. Data set include a concise description of experimental conditions for the synthesis of imidazolium ligands, 1,3-bis(4-carboxyphenyl)imidazolium chloride (H2L+Cl-) and 1,3-bis(3,5-dicarboxyphenyl)imidazolium chloride (H4L+Cl-), and MOFs. The data show that the two Nickel MOFs, 1 and 2, synthesized from imidazolium ligands are cationic frameworks. The adsorption and analysis data show that the cationic MOFs exhibit efficient adsorptive removal capacity for positively charged dyes, adsorbing up to 81.08% and 98.65% of Methyl orange and Congo red, respectively

Fuzzy Logic Controller Design for Intelligent Drilling System

An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlikethe existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation

Fuzzy Logic-Based Improved Ventilation System For The Pharmaceutical Industry

Indoor air quality in pharmaceutical industry plays a vital role in the production and storing of medicine. Stable indoor environment including favorable temperature,humidity,air flow and number of microorganisms requires consistent monitoring.This paper aimed to develop a fuzzy logic-based intelligent ventilation system to control the indoor air quality in pharmaceutical sites.Specifically,in the proposed fuzzy inference system,the ventilation system can control the air flow and quality in accordance with the indoor temperature,humidity,air flow and microorganisms in the air.The MATLAB® fuzzy logic toolbox was used to simulate the performance of the fuzzy inference system.The results show that the efficiency of the system can be improved by manipulating the input-output parameters according to the user’s demands.Compared with conventional heating, ventilation and air-conditioning (HVAC) systems,the proposed ventilation system has the additional feature of the existence of microorganisms,which is a crucial criterion of indoor air quality in pharmaceutical laboratories

Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method