Brain computer interface (BCI) requires an online and real‐time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.

An Inspection is a study of methods and techniques that can be suitably employed in practical applications. In this paper, a new activity is proposed and analysis framework to facilitate the inspection of a cylindrical object using coordinate conversion technique along with pattern matching which is again verified by machine vision technique in which maximum efficiency can be achieved. By using LABVIEW software and vision builder software the quality of an object in terms of linearity can be investigated by using different image processing techniques such as image compression, image restoration etc. along with the various image processing functions like special filters and classifiers to compute the optimum results. Using smart camera in the inspection system the static as well as the dynamic object is captured in fraction of seconds without any blurs; as a result the optimum image quality without any distortion is obtained for better analysis. The proposed system is very precise, accurate and flexible with reasonable development cost compared to other model. With the aid of an Industrial robotic system with simulation software the object is replaced immediately when the same is rejected by the machine vision model. Apart from this, the proposed model can be implemented for any type of manufacturing unit.

Due to the advancement in recent technologies and emergence of Internet of Things (IoT), Smart Home Automation (SHA) plays a vital role in today’s lifestyle. Many users are showing their keen interest to move towards this era of digital transformation in order to take the potential benefits of such intelligent and smart application. Traditional smart home systems have several limitations e.g. connectivity, coverage area, device dependability, platform supportability etc. which makes the system less sustainable. In this paper, the proposed technique introduces a novel scheme to operate the devices in an intelligent manner. The scheme implements a system named as “Speech Technology enabled Smart Home (STeSH)”, which supports voice assisted home automation with extreme security and range-free localization. The idea of the proposed technique is to integrate the power of IoT with classical home automation by using an advanced speech technology service. The entire processing of Speech Enabled Services (SES) is applied with this proposed technique and the detailed analysis is presented with proper experimental proof.