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""This is the first step-by-step guide to creating cutting edge print ads, covering everything from how advertising works, how brand-building methodologies are changing, how to get an idea, and how copy and art should be crafted." "It demystifies the advertising creative process, with page after page of practical, inspiring and often controversial advice from such masters as David Abbott, Bob Barrie, Nick Cohen, Tim Delaney, Neil French, Gary Goldsmith, John Hegarty, Lionel Hunt, Bob Isherwood, Bill Oberlander, Indra Sinha, and dozens more." "Over 200 print ads and case histories reveal the creative processes at work in Abbott Mead Vickers, Bartle Bogle Hegarty, Fallon McElligott, Goodby Silverstein, Howell Henry Chaldecott Lury, Leagas Delaney, Mad Dogs &​ Englishmen, Saatchi &​ Saatchi, and other world famous agencies in the US, UK, Asia and Australia."--BOOK JACKET."

"Segmentasi semantik adalah sebuah task pada bidang computer vision yang dewasa ini menjadi semakin penting. Segmentasi semantik sendiri dapat dipakai untuk memisahkan satu benda dengan benda yang lainnya, baik pada dua dimensi maupun tiga dimensi. Segmentasi semantik tiga dimensi umumnya mengutilisasikan sebuah point cloud yang dapat diambil menggunakan sensor Light Detection and Ranging (LIDAR). Sejak 2020, Apple menyertakan sensor LIDAR pada beberapa model iPhone. Hal tersebut memungkinkan orang awam untuk merekonstruksi berbagai objek dan keadaan di sekitarnya. Berdasarkan hal tersebut, dapat dirumuskan sebuah aplikasi yang dapat membantu penggunanya untuk melakukan scan terhadap benda rumah tangga untuk mengetahui panjang, lebar, tinggi, dan volume melalui kombinasi dari segmentasi semantik dan beberapa metode lainnya. Dibandingkan juga performa beberapa model yang menjadi kandidat integrasi dengan aplikasi tersebut, yaitu Dynamic Graph Convolutional Neural Network (DGCNN), Kernel Point Convolutional Neural Network (KPConv), Point Transformer, dan Point Transformer dengan Contrast Boundary Learning (CBL). Hasil pengujian menujukkan bahwa Point Transformer dengan CBL memiliki Intersection over Union yang paling baik. Didapatkan juga bahwa DGCNN adalah model yang paling baik untuk diimplementasikan sepenuhnya pada iPhone untuk edge computing.

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Semantic segmentation is a computer vision task that has become increasingly important in recent years. Semantic segmentation can be utilized to separate one object from another in a two dimensional or three dimensional environment. Semantic segmentation normally utilizes a point cloud that can be obtained using a Light Detection and Ranging (LIDAR) sensor. As of 2020, Apple has packaged a built-in LIDAR sensor on a few iPhone models. This allows everyday users to reconstruct all sorts of objects around them. Owing to that

fact, there can be formulized an application that helps its users to find the length, width, height, and volume of an object through a combination of semantic segmentation along with a few other methods. We also compared the performance of different models as candidates to be integrated into the application, which are Dynamic Graph Convolutional Neural Network (DGCNN), Kernel Point Convolutional Neural Network (KPConv), Point Transformer, and Point Transformer with Contrast Boundary Learning (CBL). We found that Point Transformer with CBL has the best Intersection over Union result. We also found that DGCNN is the best model to be fully implemented on an iPhone for edge computing."

"This volume describes the latest developments in protective clothing against nearly any kind of threat for both military and civilians. It deals with protection through the use of nanotechnology, interactive clothing and biotechnological processes. Factors such as comfort and ballistics are also considered in the book, and several practical examples are discussed. All papers are written by leading experts in their respective fields. "