Let's trace Linux Lernel with KGDB @ COSCUP 2021Jian-Hong Pan
https://coscup.org/2021/en/session/39M73K
https://www.youtube.com/watch?v=L_Gyvdl_d_k
Engineers have plenty of debug tools for user space programs development, code tracing, debugging and analyzing. Except “printk”, do we have any other debug tools for Linux kernel development? The “KGDB” mentioned in Linux kernel document provides another possibility.
Will share how to experiment with the KGDB in a virtual machine. And, use GDB + OpenOCD + JTAG + Raspberry Pi in the real environment as the demo in this talk.
開發 user space 軟體時,工程師們有方便的 debug 工具進行查找、分析、除錯。但在 Linux kernel 的開發,除了 printk 外,還可以有哪些工具可以使用呢?從 Linux kernel document 可以看到 KGDB 相關的資訊,提供了在 kernel 除錯時的另一個可能性。
本次將分享,從建立最簡單環境的虛擬機機開始,到實際使用 GDB + OpenOCD + JTAG + Raspberry Pi 當作展示範例。
Estimating instruction-level throughput (for example, predicting the cycle counts) is critical for many applications that rely on tightly calculated and accurate timing bounds. In this talk, we will present a new throughput analysis tool, MCA Daemon (MCAD). It is built on top of LLVM MCA and combines the advantages of both static and dynamic throughput analyses, providing a powerful, fast, and easy-to-use tool that scales up with large-scale programs in the real world.
Let's trace Linux Lernel with KGDB @ COSCUP 2021Jian-Hong Pan
https://coscup.org/2021/en/session/39M73K
https://www.youtube.com/watch?v=L_Gyvdl_d_k
Engineers have plenty of debug tools for user space programs development, code tracing, debugging and analyzing. Except “printk”, do we have any other debug tools for Linux kernel development? The “KGDB” mentioned in Linux kernel document provides another possibility.
Will share how to experiment with the KGDB in a virtual machine. And, use GDB + OpenOCD + JTAG + Raspberry Pi in the real environment as the demo in this talk.
開發 user space 軟體時,工程師們有方便的 debug 工具進行查找、分析、除錯。但在 Linux kernel 的開發,除了 printk 外,還可以有哪些工具可以使用呢?從 Linux kernel document 可以看到 KGDB 相關的資訊,提供了在 kernel 除錯時的另一個可能性。
本次將分享,從建立最簡單環境的虛擬機機開始,到實際使用 GDB + OpenOCD + JTAG + Raspberry Pi 當作展示範例。
Estimating instruction-level throughput (for example, predicting the cycle counts) is critical for many applications that rely on tightly calculated and accurate timing bounds. In this talk, we will present a new throughput analysis tool, MCA Daemon (MCAD). It is built on top of LLVM MCA and combines the advantages of both static and dynamic throughput analyses, providing a powerful, fast, and easy-to-use tool that scales up with large-scale programs in the real world.
RoadAR - free mobile application, targeted on drivers. This app will gather video and GPS data from smartphones, analyze it and build smart maps for drivers. The app will recognize traffic signs, roads itself, road surface quality, billboards information, traffic jams etc. We are going to become a platform for geo-targeted audio advertising of local businesses. Also we will sell information from this database for our b2b and b2g customers.
Now we have an android app called RoadAR, which recognizes road signs, puts them into database (with GPS position) and warns drivers when they are breaking limits or approaching a bump or a pothole at high speed. See www.roadar.ru for details.
【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。
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.