AppsFlyer - Mobile Advertising Analytics - Japanese AppsFlyer
Founded in 2011, AppsFlyer is a leading mobile advertising measurement platform that enables app marketers, ad agencies and brands to optimize their marketing spend by measuring their campaigns across more than 900 integrated mobile advertising network partners. As an official Facebook & Twitter Mobile Measurement Partner, AppsFlyer is a one stop shop for any mobile advertiser providing unbiased attribution, mobile campaign analytics, in-app user engagement, lifetime value analysis, ROI and retargeting.
A single real-time dashboard, powered by AppsFlyer’s NativeTrackTM, gives users all the tools they need to achieve the biggest return on their marketing dollars, smart deeplinking and more. The platform currently measures more than $900 million in annual mobile ad spend and more than 250 million installs every month. Clients include Samsung, Baidu, Foursquare, Hipmunk, Mail.Ru and YPlan.
For more information, visit www.appsflyer.com
AppsFlyer - Mobile Advertising Analytics - Japanese AppsFlyer
Founded in 2011, AppsFlyer is a leading mobile advertising measurement platform that enables app marketers, ad agencies and brands to optimize their marketing spend by measuring their campaigns across more than 900 integrated mobile advertising network partners. As an official Facebook & Twitter Mobile Measurement Partner, AppsFlyer is a one stop shop for any mobile advertiser providing unbiased attribution, mobile campaign analytics, in-app user engagement, lifetime value analysis, ROI and retargeting.
A single real-time dashboard, powered by AppsFlyer’s NativeTrackTM, gives users all the tools they need to achieve the biggest return on their marketing dollars, smart deeplinking and more. The platform currently measures more than $900 million in annual mobile ad spend and more than 250 million installs every month. Clients include Samsung, Baidu, Foursquare, Hipmunk, Mail.Ru and YPlan.
For more information, visit www.appsflyer.com
How I Achieved $15,000 Daily Sales on Google Play | Android App MonetizationKatsuaki Sato
Katsuaki Sato describes his studies of app monetization and promotion on Google Play. Learn from the master of Anrdoid app marketing in Tokyo Japan. Katsuaki's company Metaps is a leader in Android app promotion, monetization and retention.
セル生産方式におけるロボットの活用には様々な問題があるが，その一つとして 3 体以上の物体の組み立てが挙げられる．一般に，複数物体を同時に組み立てる際は，対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる．ただし，この方法ではロボットアームや治具を部品数と同じ数だけ必要とし，部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる．この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により，治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた．すなわち，環境中の非把持対象物のロバスト性を考慮して，組み立て作業条件を検討している．本研究ではこの方策に基づいて，複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする．このとき，対象物のロバスト性を考慮することで，仮組状態の複数物体を同時に扱う手法を提案する．作業対象としてパイプジョイントの組み立てを挙げ，簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す．さらに，作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する．
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL：https://arxiv.org/pdf/2404.19174
出典：Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要：リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat（Accelerated Features）」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
