Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
The procedure of electroencephalography (EEG) is a famous process in neuroscience that is utilized for extracting the activity of brain signals related to voluntary and involuntary actions. The systems of BCI are capable of making the persons control any external device remotely utilizing the signals of the brain with no neurophysical interference. Many techniques used for isolating noise from EEG signals like filters and blind source separation. The main problem is Artifacts (noise) such as Electrocardiography (ECG), electromyography (EMG), electrooculography (EOG) and power line (LN). Added to EEG signals and affect the performance of the system. Separating these signals into individual components to delete the artifacts is complicated. In this paper on the combination between stone-blind source separation and bandpass filter has been implemented bandpass filter was used and an algorithm based on a stone-blind source separation algorithm is presented for extracting the features from EEG based recorded brain signals. These features are extracted for Motor imagery; the function is the index finger motions to left or right. The proposed system also verified based on two Classifiers. The obtained results the precision rate were (77% by Hoeffding Tree Classifier) while the precision rate was (58% by Random forest)
In this paper, we first define a new kind of mappings involving a finite family of accretive operators via resolvents. We show some properties of such mappings in Banach spaces. Then we introduce a new iteration method using these mappings to find the common zeros of two finite families of accretive operators. Our next move is to prove the some strong convergence theorems under appropriate conditions in real reflexive strictly convex Banach spaces having uniformly Gateaux differentiable norm. Finally, we give some applications of our main results