解析Qualcomm MSM7627平台Android Boot流程：Arm11启动详解

Android
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"本文档深入探讨了Android系统的启动过程，特别是在高通MSM7627平台上的具体实现，重点聚焦于Arm11端的AppsBoot bootloader。作者林耕书以Android 4系统为背景，从Arm9启动Arm11的详细流程出发，首先回顾了前文对Arm9启动的介绍，然后详细解析了两个关键函数：oemsbl_load_aarm_bootloader和oemsbl_create_aarm_partition_tbl。 在oemsbl_load_aarm_bootloader函数中，文档展示了如何将AppsBoot bootloader的MBN（Mobile Bootloader Network）文件appsboot.mbn加载到RAM中，特别提到了它被定位在0x00000000地址，这与技术规格中的描述相吻合。作者通过口头讲解和图表辅助，帮助读者理解这一过程。 接下来，oemsbl_create_aarm_partition_tbl函数的作用在于在共享内存中创建一个分区表，用于管理不同分区的数据存储和访问。尽管具体实现未在文中详述，但这个步骤是引导Arm11启动的重要环节，因为它配置了系统启动时所需的各种分区信息。 在整个流程中，作者强调了通过这些函数之间的交互，逐步引导Arm11进入运行状态，确保了从低级硬件抽象层（如Arm9）向高级应用执行环境（如Arm11的AppsBoot bootloader）的平稳过渡。此外，文档还可能包含对项目杂谈的部分，可能涉及到开发者的经验和注意事项，以供读者参考。 这篇文档深入剖析了Android系统启动过程中在高通MSM7627平台上的Arm11 bootloader的关键细节，为深入理解Android内核启动机制提供了宝贵的教学资料。"

究浅意深-Android-高通-流程合集

2015-12-17 上传
在Android系统中，高通平台扮演着至关重要的角色，尤其在设备启动流程中。这个"究浅意深-Android-流程合集..."究浅意深-Android-流程合集.pdf"这份文档很可能是对这些流程的详尽阐述，包含了丰富的实践经验和技术细节。

#!/bin/bash #date=$(date +%Y%m%d-%H:%M:%S) date=$(date +%Y%m%d) loadbindir="A66.ZXT.ZT1028.MT8766.32+3.EU.13.0.V001.$date" # load bin file to current build dir cd $PWD rm -rf ./$loadbindir mkdir $loadbindir cd t0/out/target/product/tb8766p1_64_bsp/merged cp preloader_tb8766p1_64_bsp.bin logo.bin md1img.img spmfw.img sspm.img lk.img boot.img dtbo.img tee.img vbmeta.img vbmeta_system.img vbmeta_vendor.img super.img userdata.img MT6761_Android_scatter.txt ../../../../../../$loadbindir find -name "APDB_MT6761_S01__W2*" |xargs -I{} cp {} ../../../../../../$loadbindir find ../../../../../../s0/vendor/mediatek/proprietary/modem/A66_B2B4B5_B2B4B5B7B12B13B14B17B66B71/ -name "MDDB.META_MT6761_S00_MOLY_LR12A_R3*" |xargs -I{} cp {} ../../../../../../$loadbindir帮我解释一下这段shell脚本越详细越好

2023-06-06 上传
8. `cp preloader_tb8766p1_64_bsp.bin logo.bin md1img.img spmfw.img sspm.img lk.img boot.img dtbo.img tee.img vbmeta.img vbmeta_system.img vbmeta_vendor.img super.img userdata.img MT6761_Android_...

转成matlab： npair = nbk[idx].shape[0] rk = (nbk[idx] == k).nonzero()[0] Lk = sp.sparse.lil_matrix((npair,npair)) Lk.setdiag(lk) Lk[:,rk] = -(lk.reshape(-1,1)) Lk[rk,:] = -(lk.reshape(1,-1)) Lk_tensor.append(sp.sparse.csr_matrix(Lk)) si_map[k] = idx

2023-05-25 上传
npair = size(nbk{idx}, 1); rk = find(nbk{idx} == k);...Lk(1:npair+1:end) = lk; Lk(:,rk) = -repmat(lk', npair, 1); Lk(rk,:) = -repmat(lk, 1, npair); Lk_tensor{end+1} = csr_matrix(Lk); si_map(k) = idx;

province_id = [{"name": 14, "value": "山西"}, {"name": 22, "value": "吉林"}, {"name": 34, "value": "安徽"}, {"name": 36, "value": "江西"}, {"name": 51, "value": "四川"}, {"name": 41, "value": "河南"}] for x in province_id: df = pd.read_csv('F:/python学习/课设/' + x["value"] +'省控线.csv')name = x["value"] sname.append(name) score_lk = scores_get(df, x["value"], "理科") scores_zhongl = score_lk.append(score_lk) score_wk = scores_get(df, x["value"], "文科") scores_zhongw = score_wk.append(score_wk) #Python如何将列表[1,2]和[3,4]变成[[1,2],[3,4]]

2023-06-13 上传
scores_zhongl = [score_lk, score_lk] scores_zhongw = [score_wk, score_wk] result = [scores_zhongl, scores_zhongw] print(result) ``` 运行以上代码，输出结果为： ``` [[[score_lk], [score_lk]], [[score_...

android lk启动流程

2023-07-29 上传
Android LK启动流程如下： 1. 开机启动：当用户按下手机的电源键时，系统会进入LK小内核，LK会根据参数值来判断是正常开机启动。 2. recovery启动：当用户按下特定组合键（如音量加键+电源键）时，系统会进入恢复...

def create_C1(dataset): C1 = [] for transaction in dataset: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() return list(map(frozenset, C1)) # print(len(create_C1(dataset))) def scan_D(D, Ck, min_support): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not can in ssCnt: ssCnt[can] = 1 else: ssCnt[can] += 1 num_items = float(len(D)) retList = [] support_data = {} for key in ssCnt: support = ssCnt[key] / num_items if support >= min_support: retList.insert(0, key) support_data[key] = support return retList, support_data def apriori_gen(Lk, k): retList = [] len_Lk = len(Lk) for i in range(len_Lk): for j in range(i + 1, len_Lk): L1 = list(Lk[i])[:k - 2] L2 = list(Lk[j])[:k - 2] L1.sort() L2.sort() if L1 == L2: retList.append(Lk[i] | Lk[j]) return retList def apriori(dataset, min_support): C1 = create_C1(dataset) D = list(map(set, dataset)) L1, support_data = scan_D(D, C1, min_support) L = [L1] k = 2 while len(L[k - 2]) > 0: Ck = apriori_gen(L[k - 2], k) Lk, support_k = scan_D(D, Ck, min_support) support_data.update(support_k) L.append(Lk) k += 1 return L, support_data如果我想在这段代码中加入置信度，怎么加

2023-06-08 上传
Lk, support_k = scan_D(D, Ck, min_support) support_data.update(support_k) L.append(Lk) k += 1 rules_list = generate_rules(L, support_data, min_conf) return L, support_data, rules_list ``` 其中...

#!/usr/bin/env python3 import os import sys import cv2 from pyzbar import pyzbar def main(image_folder_path, output_file_name): img_files = [f for f in os.listdir(image_folder_path) if f.endswith(('.png'))] qr_codes_found = [] for img_file in img_files: img_path = os.path.join(image_folder_path,img_file) img = cv2.imread(img_path) barcodes = pyzbar.decode(img) for barcode in barcodes: if barcode.type == 'QRCODE': qr_data = barcode.data.decode("utf-8") qr_codes_found.append((img_file, qr_data)) with open(output_file_name,'w') as f: for file_name,qr_content in qr_codes_found: f.write(f"{file_name}: {qr_content}\n") if __name__ == "__main__": image_folder_path = '/home/lk' output_file_name = 'qr_codes_found.txt' main(image_folder_path,output_file_name)

2023-07-12 上传
在你的代码中，`image_folder_path` 被设置为 `'/home/lk'`，这可能是你的主目录下的一个文件夹。请确保该文件夹中包含 `.png` 格式的图像文件。 2. 请确保你已经安装并导入了 `cv2` 和 `pyzbar` 库。你可以使用以下...

@ai 在以下代码基础上，给出代码计算数据的置信度和提升度并输出：import csv nihao=open(r"D:\qq\Groceries.csv","r") reader=csv.reader(nihao) nihao=list(reader) for x in range(1,9836): del nihao[x][0] del nihao[0] nihao_str = str(nihao).replace('{', '').replace('}', '')# 将花括号替换为空字符串 zaijian = eval(nihao_str)# 将字符串转换回列表 def load_dataset(): # 这里只是一个示例数据集，请根据实际情况修改 dataset = zaijian return dataset def create_C1(dataset): C1 = [] for transaction in dataset: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() return list(map(frozenset, C1)) def scan_D(D, Ck, min_support): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not can in ssCnt: ssCnt[can] = 1 else: ssCnt[can] += 1 num_items = float(len(D)) retList = [] support_data = {} for key in ssCnt: support = ssCnt[key] / num_items if support >= min_support: retList.insert(0, key) support_data[key] = support return retList, support_data def apriori_gen(Lk, k): retList = [] len_Lk = len(Lk) for i in range(len_Lk): for j in range(i+1, len_Lk): L1 = list(Lk[i])[:k-2] L2 = list(Lk[j])[:k-2] L1.sort() L2.sort() if L1 == L2: retList.append(Lk[i] | Lk[j]) return retList def apriori(dataset, min_support=0.01): C1 = create_C1(dataset) D = list(map(set, dataset)) L1, support_data = scan_D(D, C1, min_support) L = [L1] k = 2 while len(L[k-2]) > 0: Ck = apriori_gen(L[k-2], k) Lk, support_k = scan_D(D, Ck, min_support) support_data.update(support_k) L.append(Lk) k += 1 return L, support_data

2023-06-07 上传
retList.append(Lk[i] | Lk[j]) return retList def apriori(dataset, min_support=0.01): C1 = create_C1(dataset) D = list(map(set, dataset)) L1, support_data = scan_D(D, C1, min_support) L = [L1] ...

逐句在代码中添加注释 self.frame_idx += 1 self.prev_gray = frame_gray cv.imshow('try', mask) cv.imshow('lk_track', vis)

2023-05-09 上传
# 递增帧索引 self.frame_idx += 1 # 更新上一帧的灰度图像 self.prev_gray = frame_gray # 在窗口中显示光流跟踪的结果 cv.imshow('try', mask) # 在窗口中显示光流跟踪的可视化...cv.imshow('lk_track', vis)

def generate_L(data_set, k, min_support): """ Generate all frequent itemsets. Args: data_set: A list of transactions. Each transaction contains several items. k: Maximum number of items for all frequent itemsets. min_support: The minimum support. Returns: L: The list of Lk. support_data: A dictionary. The key is frequent itemset and the value is support. """ support_data = {} C1 = create_C1(data_set) L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data) Lksub1 = L1.copy() L = [] L.append(Lksub1) for i in range(2, k + 1): Ci = create_Ck(Lksub1, i) Li = generate_Lk_by_Ck(data_set, Ci, min_support, support_data) Lksub1 = Li.copy() L.append(Lksub1) return L, support_data

2023-06-09 上传
然后调用函数create_C1创建初始候选项集C1，再调用函数generate_Lk_by_Ck根据C1生成频繁1项集L1。接着，将L1存储在列表L中，并将L1赋值给Lksub1，表示当前频繁项集的项数为1。 接下来，函数generate_L通过一个循环...
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