9,599 research outputs found
Literature Review of The Store Windows Display Influences on Consumers Attractiveness Through the Layout Design
Windows display is one of the important aspect in retail design in order to increase consumer product awareness. The store windows design had been varied especially with the creative visual merchandiser in managing a visual approach of store windows display. Flat type store windows display often used in Bandung Retail, and has characteristic of its own retail brand image to improve attractiveness. Layout design of a store windows display has an important role for the influence of consumer behavior especially in shopping attitudes. This research identify the effects of WAKAI and MANGO store windows display at Trans Studio Mall on consumer attractiveness through the implementation of layout design principles
Keywords Store Windows, Retail, Attractiveness, Layout design
Chemical Synthesis of Nano-Sized particles of Lead Oxide and their Characterization Studies
The quantum dots of semiconductor display novel and interesting phenomena
that have not been in the bulk material. The color tunability is one of the
most attractive characteristics in II-VI semiconductor nanoparticles such as
CdS, ZnS, CdSe, ZnSe and PbO. In this work, the semiconductor lead oxide
nanoparticles are prepared by chemical method. The average particle size,
specific surface area, crystallinity index are estimated from XRD analysis. The
structural, functional groups and optical characters are analyzed with using of
SEM, FTIR and UV- Visible techniques. The optical band gap value has also been
determined.Comment: 8 pages, 5 figures, 2 table
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
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