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液晶电视机Halrie-32Q5A-1组装机VS.TP5962.81维修详细过程，图纸，存储器程序备份： 型号：Halrie-32Q5A-1 主板号：VS.TP5962.81 芯片：TSUMV59XU-Z1 屏号：HV320WHB-N80 存储器：W25Q32FVSIG 故障：偶尔不能开机 详细描叙：https://blog.csdn.net/zyyujq/article/details/122183620

解压后可用，资源全名：Firefox Web Installer 81.0b2.exe

解压后可用，资源全名：Firefox Web Installer 81.0b8.exe

龙岩市物联网应用平台建设方案[81页].docx

dlib人脸特征库分类器，81个点 包含使用代码，通过摄像头识别人脸 import cv2 import dlib from skimage import io import numpy as np # 使用特征提取器get_frontal_face_detector detector = dlib.get_frontal_face_detector() # dlib的68点模型，使用作者训练好的特征预测器 predictor = dlib.shape_predictor("shape_predictor_81_face_landmarks.dat") cap=cv2.VideoCapture(0) while True: ret,img=cap.read() dets = detector(img, 1) for k, d in enumerate(dets): print("第", k+1, "个人脸d的坐标：", "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom()) width = d.right() - d.left() heigth = d.bottom() - d.top() print('人脸面积为：',(width*heigth)) # 利用预测器预测 #shape = predictor(img, d) cv2.rectangle(img,(d.left(),d.top()),(d.right(),d.bottom()),(0,255,0),1) shape = predictor(img, d) landmarks = np.matrix([[p.x, p.y] for p in shape.parts()]) for num in range(shape.num_parts): cv2.circle(img, (shape.parts()[num].x, shape.parts()[num].y), 3, (0,255,0), -1) #cv2.putText(img, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) cv2.imshow("img",img) if cv2.waitKey(1) & 0xFF == ord('q'): break

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解决php输出pdf中文乱码所需的资源，ttf2pt1.exe + fpdf1.81 + chinese.php + ttf2pt1-chinese-3.4.0。实测可用

2020 Flash Memory Summit 闪存峰会演讲PPT汇总，共81份。包括但不限于一下议题： Tiering_Across_Higher_Endurance_Intel_Optane_ Media_and_Low_Endurance_NAND_Flash_Media Big_Memory_Impact_on_Animation Closing_the_Memory_and_Storage_Divide Software_Defined_Memory_Combining_Persistent_Memory_and_DRAM New_Ways_to_Improve_SSD_Mgt_Performance_Optimizing_SSD_Performance_with_AI_Real_WorldWorkloads DASE_A_SMART_NEW_ARCHITECTURE_FOR_CLOUD_SCALE_STORAGE Flash_Reliability_at_Scale Flash_through_the_Hyperscale_Lens Handling_Slow_Disks_in_Heterogeneous_SSD_Deployments Using_PM_to_Accelerate_Tweet_Search_with_Apache_Lucene_at_Twitter_Experimental_Results_of_this_Investigation Hyperscale_Panel 3D_Xpoint_Optane_Media_Memory_Markets_Bits_Revenue_Costs Media_Aware_Smart_Storage_Engine Using_3DXpoint_Persistent_Memory_Effectively 3D_Xpoint_in_2025 Bringing_NVMe_TCP_Up_to_Speed High-Performance_RoCETCP_Solutions_for_End-to-end_NVMe-oFCommunication NVMe_and_NVMe-oF_Progress_Over_The_Last_Five_Years_and_Preparing_for_the_Next_Five_Years NVMe_Base_Specification_2_0_Preview NVMe_Technology_in_the_Real_World_NVMe_over_Fabrics_in_the_Enterprise NVMe_Technology_In_The_Real_World The_State_of_NVMe_Interoperability NVMe_Technology_in_Cloud_Applications Enterprise_Flash_Storage_Annual_Update Annual_Update_on_Emerging_Memories_2020 How_Data_Centers_Can_Profit_from_New_Memory_Technologies Resistive_Ram_ReRAM_RRAM_Today 3D_NAND_Current_Future_and_Beyond Emerging_Memories_Use_Grows_Driving_Capital_Equipment_Demand Emerging_Non-Volatile_Memory_The_Challenging_Journey_to_Mass_Adoption Introduction_to_Big_Memory Top_Ten_Things_You_Need_to_Know_about_Big_Memory_Management_Today Using_Persistent_Memory_and_Software_Defined_Architectures_to_Optimize_AIML_and_Analytics_workloads Accelerating_Performance_with_NVMe-oF NVMe_Technology_in_Cloud_Applications Presentation_SNIA_An_Introduction_to_the_Storage_Networking_Industry_Association Ses_Market_Directions_for_Persistent_Memory New_Ways_to_Improve_SSD_Management_and_Performance

安装好串口复用Eltima.Software.Serial.Splitter.v3.5.2.81 后.打开任务管理器.结束Serial.Splitter进程 覆盖到安装文件夹