In this paper, aspects of obtaining white colour hues for displays/monitors and lighting by using three- and four-components LED systems are discussed. Photometric equipment developed by us for multichannel LEDs control is used in an experimental study to verify theoretical calculations. Three-component RGB and four-component RGBW LED systems, which utilise the same RGB light sources and two white LEDs with warm and cold hues, are investigated. Results of testing of luminous efficacy of such systems at different values of light intensity and comparison of the corresponding circadian action factor as the value of impact of summarized RGB and RGBW white light on human circadian rhythms are presented. It is demonstrated that the four-component RGBW LED systems are more preferable for lighting and displays than the three-components RGB LED systems, because of significant higher luminous efficacy and slightly lower circadian factor over the entire range of correlated colour temperature from 2500K to 7000K studied.

Speech analysis is the modelling and estimating of the different speech characteristics that would provide the importance on each set of criteria established on the real time applications. One such analytic section in enhancement process on speeches would improve the need of speech enhancement. This paper compares the performance analysis of our proposed Fast Hybrid Euclidean Direction Search (FHEDS) algorithm with other adaptive algorithms such as NHP and FEDS algorithm. These algorithms have been tested for their adaptive noise cancellation of speech signal corrupted by different noises such as Babble, Factory, Destroy Engine, Car, Fire Engine and Train Noises. Ensuring the design criteria with current design limits of the database and its analysis have been encapsulated with each phase of design with Noise model, improving the better performance aspects. The relative factors for comparisons have been tabulated with each set of the noise and clear speech data with proposed filter operation. The proposed model effectively reduces the noise for achieving better speech enhancement. The proposed model achieves high Signal-to-Noise Ratio (SNR) when compared to traditional models.

The increasing demand for bandwidth by mobile users in wireless communication becomes a challenging issue to the research community. Several theories and models have been proposed to mitigate this issue. The most effective and commonly used approach to resolve the demand shortage of bandwidth is the massive Multi-Input and Multi-Output (MIMO) approach in which the number of transmitting and receiving antennas is placed at the base station (BS) to fulfill the issue of bandwidth. However, this technique suffers from various issues in estimating the channel due to interference, beamforming, and pilot contamination. In this paper, a novel channel estimation technique is being proposed using Code Shifting Keying symbols as pilot signals (CSK-PS) to minimize the pilot contamination. These signals are used as reference signals and the received signal is detected. The presented approach reduces the interference (pilot contamination) and improves the channel estimation in massive MIMO networks by using the modified expected propagation estimation method (MEPE). The presented approach is validated using mat-lab.

Technology is getting smarter day by day and facilitating every part of human life from automatic alarming, automatic temperature, and personalised choice prediction and behaviour recognition. Such technological advancements are using different machine learning techniques for artificial intelligence. Face recognition is also one of the techniques to develop futuristic artificial intelligence-based technology used to get devices equipped with personalised features and security. Face recognition is also used for keeping information of facial data of employees of any company citizens of any country to get tracked and control over crimes in unfair incidents. For making face recognition more reliable and faster, several techniques are evolving every day. One of the fastest and most dependable face recognitions is CNN based face recognition. This work is designed based on the multiple convolutional module-based CNN equipped with batch normalisation and linear rectified unit for normalising and optimising features with minibatch. Faces in CNN’s fully connected layer are classified using the SoftMax classifier. The ORL and Yale face datasets are used for training. The average accuracy achieved is 94.74% for ORL and 96.60% for Yale Datasets. The convolutional neural network training was done for different training percentages, e.g., 66%, 67%, 68%, 69%, 70%, and 80%. The experimental outcomes exhibited that the defined approach had enhanced the face recognition performance.

This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

This article considers the peculiarities of using circular orthogonal polarization basis for measuring the parameters of an electromagnetic wave. In particular, the angle of inclination of the major axis of the polarization ellipse and the ellipticity coefficient are among measuring parameters. The main expressions for calculation of field parameters in circular and linear orthogonal polarization basis are developed and analyzed. The advantages of using the ring as a measuring antenna in comparison with symmetrical vibrators of the turnstile antenna are substantiated. The expressions obtained in the article for calculating the measurement errors of polarization parameters in a linear orthogonal polarization basis illustrate the multifactorial dependence of the measurement accuracy on the angular and amplitude parameters. In contrast to the linear polarization basis, in case of circular basis, the inclination angle of the polarization ellipse axis can be found by direct measurements of the phase shift, and the accuracy of measuring the ellipticity coefficient is affected only by the error of measuring the ratio of voltage amplitudes, which are proportional to the modules of the field strength vectors of the left and right directions of the circular polarization rotation. This provides better potential accuracy of measurement for the electromagnetic wave parameters when using circular polarization antennas and, correspondingly, more reasonable analysis in the circular orthogonal polarization basis.