A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia communication.Versatile Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)videos.This article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video communication.The advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon limit.Turbo codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel fashion.Constituent RSC encoders may be operating on the same or different architectures and code rates.The proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video communication.Moreover,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−4.The Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

Noise Reduction in Industry Based on Virtual Instrumentation

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction.In the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G deployment.This study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a dataset.The experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft.We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency.Using virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested algorithms.Laser welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+ICA.Press workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

Augmented Node Placement Model in t-WSN Through Multiobjective Approach

In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based region.The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position.In this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing range.An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem efficiently.Multiobjective problems are intended to solve different objectives of a problem simultaneously.Bio-inspired algorithms address the NP-hard problem most effectively in recent years.In NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance relation.In the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the algorithm.Performance of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

Integrated interferometers’system for in situ real-time optical signal modulation

Improving the functionality of an optical sensor on a prefabricated platform relies heavily on an optical signal conditioning method that actively modulates optical signals.In this work,we present a method for active modulation of an optical sensor response that uses fiber modal interferometers integrated in parallel.Over a broad frequency range of 1 Hz to 1 kHz,the interferometers’technology allows for adjustable amplification,attenuation,and filtering of dynamic signals.The suggested method is also used to enhance the real-time response of an optical fluid flowmeter.In order to keep tabs on different physical fields,the suggested approach promotes the creation of self-conditioning sensing systems.

Liquid crystal immunosensors for the selective detection of Escherichia coli with a fast analysis tool

The consumption of contaminated food may cause serious illnesses,and traditional methods to detect Escherichia coli are still associated with long waiting times and high costs given the necessity to transport samples to specialized laboratories.There is a need to develop new technologies that allow cheap,fast,and direct monitoring at the site of interest.Thus,in this work,we developed optical immunosensors for the selective detection of E.coli,based on liquid crystal technology,whose molecules can align in different manners depending on the boundary conditions (such as substrates) as well as the environment that they experience.Each glass substrate was functionalized with anti-E.coli antibody using cysteamine as an intermediate,and a vertical alignment was imposed on the liquid crystal molecules by using DMOAP during functionalization.The presence of bacteria disrupts the alignment of the liquid crystal molecules,changing the intensity of light emerging between cross polarizers,measured using a polarized optical microscope and a monochromator.It was possible to detect E.coli in suspensions in the concentration range from 2.8 cells/mL to 2.8×10^(9) cells∕mL.Selectivity was also evaluated,and the sensors were used to analyze contaminated water samples.A prototype was developed to allow faster,in-situ,and easier analysis avoiding bulky instruments.