SIM800C IP应用教程：AT命令与HTTP/FTP操作详解

SIM800C

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SIM800C 中文资料.pdf

世联芯科技长期供应SIM800C,SIM800C是一款四频GSM/GPRS模块,为城堡孔封装。其性能稳定，外观小巧，性价比高，能满足客户的多种需求。 SIM800C工作频率为GSM/GPRS 850/900/1800/1900MHz，可以低功耗实现语音、SMS和数据信息的传输。SIM800C尺寸为17.6*15.7*2.3mm，能适用于各种紧凑型产品设计需求.

SIM800C GPRS原理图

SIM800C模块资料+程序

SIM800C模块用户手册，原理图，51、STM32、Arduino例程

verilator工程目录下这些文件的作用是什么：Vtop VtopALL.a VtopSyms.cpp Vtop_024rootSlow.cpp sim_main.d Vtop.cpp VtopALL.cpp VtopSyms.h Vtop__ver.d sim_main.o Vtop.h VtopALL.d Vtop_024root.cpp VtopverFiles.dat verilated.d Vtop.mk VtopALL.o Vtop___024root.h Vtop_classes.mk verilated.o

- `Vtop__Syms.cpp`：这是由 Verilator 自动生成的 C++ 源文件，包含了仿真器中使用到的模块和信号的符号表信息。 - `Vtop___024root__Slow.cpp`：这是由 Verilator 自动生成的 C++ 源文件，包含了仿真器中的主时钟...

配置文件内容修改为“{ "ttu_type": 0, "sim_t1_ip": "192.168.0.1", "sim_t2_ip": "192.168.0.2", "sim_m_ip": "192.168.0.3", "single_mode_enable": 1, "parallel_mode_enable": 0, "transformer_rated_capacity": 630, "light_load_rate_limit": 20, "heavy_load_rate_limit": 80, "no_voltage_limit": 110, "no_voltage_phases": 1, "single_mode_closing_delay": 1000, "parallel_mode_closing_delay": 1000, "single_mode_opening_delay": 1000, "ct_ratio": 200, "voltage_difference_judgment_enable": 1, "phase_difference_judgment_enable": 1, "allowable_voltage_difference_for_closing": 36, "allowable_phase_difference_for_closing": 5 }”

const char* sim_t1_ip = cJSON_GetObjectItem(root, "sim_t1_ip")->valuestring; const char* sim_t2_ip = cJSON_GetObjectItem(root, "sim_t2_ip")->valuestring; const char* sim_m_ip = cJSON_GetObjectItem...

翻译以下代码 <select id="getExistCount" resultMap="baseRm"> select * from (select * from (select vsci.validate_sim_card_id,vsci.device_code,vsci.manufacturer_id, vsci.protocol_name,vsci.device_type,vsci.device_type_name,vsci.appkey from iledproduction.validate_sim_card_info vsci where vsci.manufacturer_id in <foreach collection="manufacturerIdList" item="item" index="index" open="(" close=")" separator=","> #{item} </foreach> <if test="propMap.size>0 and propMap!=null"> <foreach collection="propMap" index="index" item="item"> <if test='index.toString=="device_type"'> and vsci.device_code = "${item[1]}" </if> </foreach> </if>)a left join (select psi.station_name,psi.appkey as appkey_b,psi.department_key from iledproduction.production_station_info psi ) b on a.appkey=b.appkey_b )c left join (select dp.department_name,dp.department_key as department_key_d from iledproduction.department dp )d on c.department_key=d.department_key_d <if test="propMap.size>0 and propMap!=null"> <foreach collection="propMap" index="index" item="item"> <if test='index.toString=="manufacturer_id"'> where vsci.manufacturer_id = ${item[1]} </if> </foreach> </if> limit #{firstResult},#{maxResults} </select>

SELECT vsci.validate_sim_card_id, vsci.device_code, vsci.manufacturer_id, vsci.protocol_name, vsci.device_type, vsci.device_type_name, vsci.appkey FROM iledproduction.validate_sim_card_info vsci ...

sim_ekb_install_2020_02_29.zip

sim_ekb_install_2020_02_29.zip是一个文件压缩包，其中包含了一个名为"sim_ekb_install"的应用程序，用于在2020年2月29日安装和配置模拟电子键盘（sim_ekb）。模拟电子键盘是一种软件工具，它模拟物理键盘的功能...

基于STM32单片机SIM800C模块发送短信的代码

以下是基于STM32单片机和SIM800C模块发送短信的代码，供参考：首先，需要定义一些常量和变量： ```c #define SIM800C_USART USART3 #define SIM800C_USART_RCC RCC_APB1Periph_USART3 #define SIM800C_USART_GPIO...

import win32com.client as com vissim_com = com.Dispatch("Vissim.Vissim.600") Sim = vissim_com.Simulation vissim_com.LoadNet('C:\\Users\\86183\\Desktop\\study\\study.inpx') vissim_com.LoadLayout('C:\\Users\\86183\\Desktop\\study\\study.layx') Sim = vissim_com.Simulation a = Sim.RandomSeed print(a) Sim.RunContinuous() Sim.s这段代码有什么问题

如果你想获取当前的随机种子值，可以直接读取 `Sim.RandomSeed` 的值而无需进行赋值操作。以下是修正后的代码： ```python import win32com.client as com vissim_com = com.Dispatch("Vissim.Vissim.600") Sim = ...

sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_x,absoluteLinearVelocity[1]) sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_y,absoluteLinearVelocity[2]) sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_z,absoluteLinearVelocity[3])

这是Simulink中设置物体起始速度的代码，它将物体的线性速度设置为一个三维向量。其中的forwarder表示要设置速度的物体，absoluteLinearVelocity是一个包含三个浮点数的数组，表示物体在三个方向上的初始速度。

char* command1 = "echo `uci get m_system.usesiminfo.operator;uci get m_system.usesiminfo.card_slot; \ uci get m_system.usesiminfo.iccid;uci get m_system.usesiminfo.lock;uci get m_system.system.simnum`"; char* command2 = "echo `uci get m_system.sim1.operator;uci get m_system.sim1.card_slot;uci get m_system.sim1.iccid; \ uci get m_system.sim2.operator;uci get m_system.sim2.card_slot;uci get m_system.sim2.iccid; \ uci get m_system.sim3.operator;uci get m_system.sim3.card_slot;uci get m_system.sim3.iccid; \ uci get m_system.sim4.operator;uci get m_system.sim4.card_slot;uci get m_system.sim4.iccid`"; SetGetTCPCommand("set", &command1); SetGetTCPCommand("set", &command2);一个程序执行这个代码，另一个进程执行这个代码while (1) { char* SendString = NULL; SetGetTCPCommand("get", &SendString);} 而SetGetTCPCommand程序放在公用库，这样get取值两个set都能取到么

如果`SetGetTCPCommand`程序放在一个公共库中，并且两个进程分别调用这个库中的函数，那么它们可以共享并获取到同一个TCPCommand的值。这是因为在共享库中的静态变量（如`static char *TCPCommand`）是在进程的...

acc_val = zeros(length(opt.lambda), length(opt.Sim_scale)); for i = 1 : length(opt.lambda) load(['../SynC_CV_classifiers/SynC_fast_' opt.loss_type '_classCV_' dataset '_split' num2str(opt.ind_split) '_' feature_name '_' norm_method '_' Sim_type... '_lambda' num2str(opt.lambda(i)) '.mat'], 'W_record'); for j = 1 : nr_fold Ybase = Ytr; Ybase(fold_loc{j}) = []; Xval = Xtr(fold_loc{j}, :); Yval = Ytr(fold_loc{j}); W = W_record{j}; for k = 1 : length(opt.Sim_scale) Sim_base = Compute_Sim(Sig_Y(unique(Ybase), :), Sig_Y(unique(Ybase), :), opt.Sim_scale(k), Sim_type); Sim_val = Compute_Sim(Sig_Y(unique(Yval), :), Sig_Y(unique(Ybase), :), opt.Sim_scale(k), Sim_type); V = pinv(Sim_base) * W; Ypred_val = test_V(V, Sim_val, Xval, Yval); acc_val(i, k) = acc_val(i, k) + evaluate_easy(Ypred_val, Yval) / nr_fold; end clear W; end clear W_record;

最后，使用 `V`、`Sim_val`、`Xval` 和 `Yval` 调用函数 `test_V()` 进行预测，并将预测结果与真实标签计算准确率，并将其累加到 `acc_val(i, k)` 中。在每一折循环结束后，清除权重 `W`。在外层循环结束后，清除...

将下面代码写成matlab形式 int runBm3d( const Mat image_noisy, Mat& image_basic, Mat& image_denoised ) { int Height = image_noisy.rows; int Width = image_noisy.cols; int Channels = image_noisy.channels(); vector<Mat> block_noisy;//store the patch vector<int>row_idx;//patch idx along the row direction vector<int>col_idx; GetAllBlock(image_noisy, Width, Height, Channels, kHard, pHard, block_noisy, row_idx, col_idx); int bn_r = row_idx.size(); int bn_c = col_idx.size(); tran2d(block_noisy, kHard); vector<int> sim_num;//index number for the selected similar patch in the block vector vector<int> sim_idx_row;//index number for the selected similar patch in the original Mat vector<int> sim_idx_col; vector<Mat>data;//store the data during transforming and shrinking Mat kaiser = gen_kaiser(beta, kHard);//2-D kaiser window float weight_hd = 1.0;//weights used for current relevent patch Mat denominator_hd(image_noisy.size(), CV_32FC1, Scalar::all(0)); Mat numerator_hd(image_noisy.size(), CV_32FC1, Scalar::all(0)); for (int i = 0; i < bn_r; i++) { for (int j = 0; j < bn_c; j++) { //for each pack in the block sim_num.clear(); sim_idx_row.clear(); sim_idx_col.clear(); data.clear(); getSimilarPatch(block_noisy, data, sim_num, i, j, bn_r, bn_c, int((nHard - kHard) / pHard) + 1, NHard, tao_hard);//block matching for (int k = 0; k < sim_num.size(); k++)//calculate idx in the left-top corner { sim_idx_row.push_back(row_idx[sim_num[k] / bn_c]); sim_idx_col.push_back(col_idx[sim_num[k] % bn_c]); } tran1d(data, kHard);//3-D transforming DetectZero(data, lambda3d * sigma);//shrink the cofficient weight_hd = calculate_weight_hd(data, sigma); Inver3Dtrans(data,kHard);//3-D inverse transforming aggregation(numerator_hd, denominator_hd, sim_idx_row, sim_idx_col, data, weight_hd, kHard, kaiser);//aggregation using weigths } } image_basic = numerator_hd / denominator_hd;

[numerator_hd, denominator_hd] = aggregation(numerator_hd, denominator_hd, sim_idx_row, sim_idx_col, data, weight_hd, kHard, kaiser); %aggregation using weigths end end image_basic = numerator_...

function sysCall_init() forwarder=sim.getObjectHandle('ConveyorBelt_forwarder') sim.setFloatSignal("belt_con",1) sen=sim.getObjectHandle('sensor') end function sysCall_cleanup() end function sysCall_actuation() beltVelocity=sim.getScriptSimulationParameter(sim.handle_self,"conveyorBeltVelocity") con=sim.getFloatSignal("belt_con") senflag,dis,_,_,_=sim.readProximitySensor(sen) print(senflag) if con==1 and senflag ==0 then -- Here we "fake" the transportation pads with a single static rectangle that we dynamically reset -- at each simulation pass (while not forgetting to set its initial velocity vector) : relativeLinearVelocity={beltVelocity,0,0} -- Reset the dynamic rectangle from the simulation (it will be removed and added again) sim.resetDynamicObject(forwarder) -- Compute the absolute velocity vector: m=sim.getObjectMatrix(forwarder,-1) m[4]=0 -- Make sure the translation component is discarded m[8]=0 -- Make sure the translation component is discarded m[12]=0 -- Make sure the translation component is discarded absoluteLinearVelocity=sim.multiplyVector(m,relativeLinearVelocity) -- Now set the initial velocity of the dynamic rectangle: sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_x,absoluteLinearVelocity[1]) sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_y,absoluteLinearVelocity[2]) sim.setObjectFloatParameter(forwarder,sim.shapefloatparam_init_velocity_z,absoluteLinearVelocity[3]) end end

sim.getFloatSignal("belt_con") 获取到传送带的控制信号，如果该信号为 1 并且传送带上没有物体，则通过 sim.resetDynamicObject(forwarder) 重置 forwarder 物体，并使用 sim.setObjectFloatParameter() ...

mg_usage_statistic->data_usage[mg_sim_status->sim_idx].total_send += (t_flowrate.total_send >> 10); mg_usage_statistic->data_usage[mg_sim_status->sim_idx].total_recv += (t_flowrate.total_recv >> 10);

这段代码的作用是将 t_flowrate 结构体中的总发送和总接收数据量（以千字节为单位）累加到 mg_usage_statistic 结构体中对应的 SIM 卡的数据使用情况统计中。其中 mg_sim_status->sim_idx 表示当前 SIM 卡的索引，而...

def SGNS_process(self): print("Calculating the sim_sgns...") logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') self.sim_sgns = [] vec_sgns = Word2Vec(LineSentence('text8.txt'), vector_size=100, window=2, sg=1, hs=0, min_count=1, workers=multiprocessing.cpu_count()) tbar = tqdm(total=len(self.word_vector)) for word in self.word_vector: try: self.sim_sgns.append(vec_sgns.wv.similarity(word[0], word[1])) except: self.sim_sgns.append(0) tbar.update(1) tbar.close() # print(self.sim_sgns) f = open('sim_sgns', 'wb', encoding='utf-8') pickle.dump(self.sim_sgns, f) f.close()

具体来说，代码首先创建了一个空的列表 self.sim_sgns，然后使用 gensim 库中的 Word2Vec 函数训练了一个 SGNS 模型，这个模型使用的是 'text8.txt' 文件中的数据进行训练，词向量的维度为 100，窗口大小为 2，采用...

Sim_tr = Compute_Sim(Sig_Y(unique(Ytr), :), Sig_Y(unique(Ytr), :), Sim_scale, Sim_type); Sim_te = Compute_Sim(Sig_Y(unique(Yte), :), Sig_Y(unique(Ytr), :), Sim_scale, Sim_type); V = pinv(Sim_tr) * W; Ypred_te = test_V(V, Sim_te, Xte, Yte); acc_te = evaluate_easy(Ypred_te, Yte); disp(acc_te); save(['../SynC_results/SynC_fast_' opt.loss_type '_' dataset '_split' num2str(opt.ind_split) '_' feature_name '_' norm_method '_' Sim_type... '_lambda' num2str(lambda) '_Sim_scale' num2str(Sim_scale) '.mat'], 'W', 'V', 'lambda', 'Sim_scale', 'acc_te');

接下来，使用 `V`、`Sim_te`、`Xte` 和 `Yte` 调用函数 `test_V()` 进行测试集的预测，并将预测结果与真实标签计算准确率赋给变量 `acc_te`。最后，打印出测试集的准确率，并将权重 `W`、投影矩阵 `V`、参数 `...

sim_ekb_install_2021_06_02.exe

sim_ekb_install_2021_06_02.exe是一个可执行文件，这个文件名显示了一些关键信息。首先，"sim_ekb" 表示这个文件与“sim”（模拟）和“ekb”（Ekaterinburg）相关。它可能与某个与模拟或者Ekaterinburg有关的应用...

解释这段代码的意思import numpy as np import matplotlib.pyplot as plt Neck_Fz_sim = np.loadtxt("Neck Lower Fz_sim.txt", delimiter=",",) Neck_Fz_sled = np.loadtxt("Neck Lower Fz_sled140ms.txt", delimiter=",",) from objective_rating_metrics.rating import ISO18571 iso_rating = ISO18571(reference_curve=Neck_Fz_sled, comparison_curve=Neck_Fz_sim) overall_rating = iso_rating.overall_rating() print(str(overall_rating)) print('overall_rating: ', iso_rating.overall_rating()) print('corridor_rating: ', iso_rating.corridor_rating()) print('phase_rating: ', iso_rating.phase_rating()) print('magnitude_rating: ', iso_rating.magnitude_rating()) print('slope_rating: ', iso_rating.slope_rating()) x_data_sim = Neck_Fz_sim[...,0] y_data_sim = Neck_Fz_sim[...,1] x_data_sled = Neck_Fz_sled[...,0] y_data_sled = Neck_Fz_sled[...,1] plt.plot(x_data_sim,y_data_sim,linewidth=2,label = 'simulation') plt.plot(x_data_sled,y_data_sled,linewidth=2,label = 'sled') plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.05),y_data_sled - max(np.abs(y_data_sled) * 0.05), color='g', alpha=0.2) plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.5),y_data_sled - max(np.abs(y_data_sled) * 0.5), color='y', alpha=0.2) plt.text(0,1500,'overall_rating: \roverall_rating\ncorridor_rating:\nphase_rating:\nmagnitude_rating:\nslope_rating:',fontsize = 10) plt.title("Neck Lower Fz") plt.xlabel("Time(s)") plt.ylabel("Force(N)") plt.show()

代码首先使用NumPy中的loadtxt函数从两个文件中加载数据，并将其分别存储在Neck_Fz_sim和Neck_Fz_sled变量中。接着，使用objective_rating_metrics包中的ISO18571类来计算仿真数据和实验数据之间的差异，并计算了五...

self.sim_svd = [] print("Calculating the sim_svd...") tbar = tqdm(total=len(self.word_vector)) for word in self.word_vector: if word[0] in self.vocab and word[1] in self.vocab: idx0 = self.vocab.index(word[0]) idx1 = self.vocab.index(word[1]) vector0 = self.result[idx0] vector1 = self.result[idx1] norm0 = np.linalg.norm(vector0) norm1 = np.linalg.norm(vector1) frac0 = np.dot(vector0, vector1) frac1 = norm0 * norm1 if frac1 != 0: sim = np.around(frac0 / frac1, 1) else: sim = 0 self.sim_svd.append(sim) else: self.sim_svd.append(0) tbar.update(1) tbar.close() # print(self.sim_svd) f = open('sim_svd', 'wb') pickle.dump(self.sim_svd, f) f.close()

这段代码看起来是在计算两个词语在词向量空间中的相似度，并将结果保存到了一个文件中。...最后，代码将计算得到的相似度列表 self.sim_svd 保存到一个名为 'sim_svd' 的二进制文件中，使用 pickle.dump() 函数实现。

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SIM800C IP应用教程：AT命令与HTTP/FTP操作详解

SIM800C 中文资料.pdf

SIM800C GPRS原理图

SIM800C模块资料+程序

sim_ekb_install_2020_02_29.zip

基于STM32单片机SIM800C模块发送短信的代码

mg_usage_statistic->data_usage[mg_sim_status->sim_idx].total_send += (t_flowrate.total_send >> 10); mg_usage_statistic->data_usage[mg_sim_status->sim_idx].total_recv += (t_flowrate.total_recv >> 10);

sim_ekb_install_2021_06_02.exe

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