Antecedents of the attitude toward the athlete celebrities' human brand extensions

PurposeThe present study aims to examine how consumers evaluate the extended human brands of athlete celebrities beyond their unique brand personality associated with sports. Athlete celebrities' unique image in sports is used as a human brand, and attitude toward the athlete brand extensions is investigated when the athlete's name is included in a new non-sport brand. The concepts of brand extensions were employed to develop the ideas of human brand extensions.Design/methodology/approachIn total, 198 participants answered online survey questions before and after being informed of athlete brand extensions. Partial least squares structural equation modeling is utilized to test the hypotheses.FindingsThe survey results indicated that athlete–product fit and image transfer positively influenced attitude toward the extension. In addition, attitude toward the athlete brand extensions was significantly influenced by consumers' pre-existing attitude toward the celebrity; however, not by celebrity's expertise.Originality/valueThe research findings imply that some brand extension concepts are applicable to human brands to understand the effectiveness of athlete brand extensions for non-sport products.

Face Transferring on Webcam images using StyleGAN

In image processing technology, face transfer is broadly used for privacy protection, picture enhancement, and entertainment applications. Face transfer is the domain that maps one image into another image and extracts several features of the face from one person to morph that face to another person. This face transfer will carry the facial expressions also. This is also called face morph, face swap, etc. Here we propose StyleGAN technology using face transfer with the image to get high quality. In this StyleGAN contribute the bilinear interpolation and affine transformation. Bilinear interpolation is to remove the noise and increase the quality of images. Affine transformation is to supply the images with 2d warping to improve the image quantity. To upgrade the quality of the images with face transfer is adopted to increase the accuracy of the image quality after image transfer.

An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer

Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.

Scope and Key Areas of Medical Image Processing

Over the past century, scientific advances in diagnostic devices have offered new potential for noninvasive diagnoses and entrenched computed tomography as a critical component of today's health services. The multidisciplinary field of health image analysis is one of the key areas of innovation that represents these achievements. This area of rapid growth deals with a wide range of operations that support the whole data flow in current health monitoring systems (from raw data capture through digital image transfer). These technologies now have better spatial and luminance resolutions, as well as quicker collection periods, resulting in a large volume of high critical image files that must be appropriately processed and evaluated in order to provide reliable diagnostics findings. This article examines the core kinds of clinical image analysis, as well as the background of various imaging technologies and the major difficulties and developments in the field.

Experimental demonstration of multiple dimensional coding decoding for image transfer with controllable vortex arrays

Vortex beams carrying orbital angular momentum (OAM), which featuring helical phase front, have been regarded as an alternative spatial degree of freedom for optical mode coding and multiplexing. For most reported OAM-based mode coding schemes, data information is only encoded by different OAM mode states. In this paper, we introduce a novel design technique to construct vortex array phase grating (VAPGs) for the flexible generation of vortex arrays, and employ the proposed VAPGs to realize multi-dimensional space/mode/amplitude coding/decoding. By designing VAPGs with different parameters and loading them on to a single spatial light modulator (SLM), we successfully generate vortex array with different mode states and relative power in the experiments. Moreover, a 10-bit multi-dimensional space/mode/amplitude data coding/decoding scheme for image transfer in free-space link with a zero bit-error-rate is experimentally demonstrated, which confirm the feasibility of our proposed VAPG-based coding/decoding scheme.