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ARM performance, Algorithms In fact, there is no universal benchmark tool. Week40 (10/06) 網路文章 Why do we learn algorithms? Why do we need so many sorting algorithms? Why do we need to learn different sorting algorithms when the STL sort function is already available to us in C++? 網路資源 The Algorithms 不同程式語言演算法的實現. every-programmer-should-know Data Structure Visualizations 演算法視覺化 Week41 (10/13) 網路文章 GPU Processing Budget Approach to Game Development GPU budget是估算處理1 pixel的cycle cost, 單位是cycles/frame/pixel...

I have a build environment with make4.1 and JDK8 for Android N/O, but JDK6 and make3.81 are required by Android KitKat. Here is a way to create a clean environment for Android KK. Create a new environment in current Ubuntu 16.04 install schroot and debootstrap sudo apt install schroot debootstrap if xenial no exist, update to latest version of debootstrap ls -l /usr/share/debootstrap/scripts/xenial Configure new environment edit /etc/schroot/schroot.conf [Build_KK] description=ubuntu16.04 Android_KK type=directory directory=/srv/chroot/Build_KK users=oopsmonk groups=oopsmonk root-groups=root profile=default Adding Mount points edit /etc/schroot/default/fstab...

Jigsaw Puzzle, Hidden layers, New SoC, Algorithm basics, HTC Week35 (09/01) 第一次完成520片的拼圖, 花了4個晚上的時間, 大約12小時吧?! 下次記得顏色鮮明的圖會比較愜意些. Week36 (09/08) 網路文章 Credit firm Equifax says 143m Americans’ social security numbers exposed in hack 在新聞公開前高層就己賣掉市值1.8m的股票!! 網路資源 Google Developer Documentation Style Guide Google公佈了內部文件撰寫的格式及注意事項. Neural Networks and Deep Learning - Week 3 Tuning hidden layer size iterations: 5000 learning_rate: 1.2 activation: sigmoid Accuracy for 1 hidden units: 67.5 % Accuracy for 2 hidden units: 67.25 % Accuracy for 3 hidden units: 90....

Install caffe framework and run Faster R-CNN demo on Ubuntu 16.04. Test environment CPU: Intel(R) Core(TM) i3-4130 CPU @ 3.40GHz 4-Cores GPU: ASUSTeK GeForce GTX 1060 with 6GB Memory HD: WDC WD5000AAKX OS: Ubuntu 16.04 Test Flow Install software requirement Video pre-processing: get jpeg images from source video Image Labeling Use Faster R-CNN to genrate trained model Run Faster R-CNN demo Requirement Hardware: Good graphic card with large memory (6GB memory is okay, but it has problem in VGG traing....

以前球隊在回防時, 教練總會說"快跑! 別回頭, 到定點再休息". 關於人生, 喘息點在哪兒? 我想就在滴水穿石之間! Deep Learning <–> keep learning Week31 (08/04) 網路文章 A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN R-CNN: https://arxiv.org/abs/1311.2524 Visual Recognition就是從這裡爆發了… 透過Selective Search找到Region後丢給修改過的AlexNet學習, 最後再給Support Vector Machine (SVM) 這層CNN. 原本設計的R-CNN就只做這些: 用Selective Search找region 透過pre-trained AlexNet去算特徵後交給SVM看region裡的是什麼鬼東西 最後透過線性回歸(linear regression)畫出物件的座標 Fast R-CNN: https://arxiv.org/abs/1504.08083 Region proposals using Selective Search. RoI (Region of Interest) Pooling Combine All Models into One Network 將R-CNN的AlexNet, SVM, Regressor整合在一起變成單一個network Fast R-CNN instead used a single network to compute the extract image features (CNN), classify (SVM), and tighten bounding boxes (regressor)....

以前球隊在回防時, 教練總會說"快跑! 別回頭, 到定點再休息". 關於人生, 喘息點在哪兒? 我想就在滴水穿石之間! Week27 (07/07) 網路文章 Elon Musk: The world’s population is accelerating toward collapse and nobody cares 網路資源 Barbara Oakley: “Learning How to Learn” | Talks at Google Week28 (07/14) 網路資源 Numpy Quick Start MathJax basic tutorial and quick reference MathJax語法速查及範例 Week29 (07/21) 網路文章 Path Guide: A New Approach to Indoor Navigation 讀書心得 The Numpy Stack in Python 用簡短的方式介紹numpy, pandas, matplotlib, scipy. numpy: 在matrix, vector處理會比ptython built-in還快很多且方便....

以前球隊在回防時, 教練總會說"快跑! 別回頭, 到定點再休息". 關於人生, 喘息點在哪兒? 我想就在滴水穿石之間! Week22 (06/02) 讀書心得 OSDI: Memory Management Kernel space的memory management, 要考慮到的問題, Internal/External fregmentation, physical/logical contiguous, buddy system, slab allocator. User space的memory管理, page fault handler, Red-Black Tree / AVL Tree. 論文學習 FarmBeats: An IoT Platform for Data-Driven Agriculture PDF ,Slides FarmBeats這套系統提出了几個解決方法: 遠距傳輸, 以往的解法是用satellite或是手機訊號, 這裡是使用TVWS(TV White Space)以減少硬體成本, TVWS是利用數位電視未使用的頻寬做傳輸, 屬於低頻有傳輸距離長, 穿透力強的優點. 天氣感知系統, 透過OpenWeather API的資料來規劃電量的使用及電池的充電與否. 風力輔助系統, 收集農地的風向及風力數據, 透過飛行演算法(Flight Planning Algorithm)規劃出適當的路線, 減少逆風造成的額外耗電量. 本地影像及資料壓縮, 一般Drone的航空影像(Aerial Imagery)或sensors資料都是直接傳到雲端, 透過Farmbeats的gateway做中繼站並壓縮影像及sensors資料, 可支援1星期的在網路無法使用情況. Week23 (06/09) 網路文章 When should we use mutex and when should we use semaphore...