Si523：13.56MHz超低功耗非接触式读卡芯片

13.56MHZ
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"Si523是一款专为13.56MHz非接触式读卡应用设计的高性能、低功耗芯片。它支持多种标准，包括ISO/IEC14443A/B和MIFARE协议，适用于构建低成本、低功耗的非接触式读卡器。该芯片具备自动载波侦测功能（ACD），能够自动检测13.56MHz的外部射频场和射频卡，且在ACD过程中无需微控制器介入。此外，还具有OSC起振失败监测功能，确保系统稳定运行。 Si523集成了高度优化的模拟电路，用于解调和译码响应，并配备了带缓冲的输出驱动器，简化了与天线的连接，只需要最少的外围元件。这款芯片的操作距离可达50mm，提供SPI、I2C和UART三种主机接口，速度分别为10Mbits/s、400kBd（快速模式）和3400kBd（高速模式）。 此外，Si523内置64字节的FIFO，支持灵活的中断模式和可编程定时器，以及CRC协处理器，增强了数据处理能力。芯片电源电压范围是2.5V至3.6V，待机时的硬掉电和软掉电电流极低，分别为1.1uA和1.1uA（3.3V），而自动寻卡电流为2.9uA（500ms）。其应用领域广泛，包括但不限于超低功耗的非接触式阅读器、ISO/IEC14443A/MIFARE读卡器以及ISO/IEC14443B读卡器。 封装图显示了Si523的管脚布局，包括电源、地线、复位、输入/输出信号以及辅助引脚。管脚定义详细列出了每个引脚的功能，例如SDI、NRSTPD、MFIN、MFOUT等，方便用户进行硬件设计。此外，芯片还包括一个结构框图，展示了天线接口、模拟接口、主机接口、FIFO、缓冲区、非接触式UART、寄存器组、SPI、I2C总线等组件的内部架构，帮助开发者理解芯片的工作原理。 Si523是一款全面集成的13.56MHz读卡芯片，以其高效能、低功耗和灵活的接口选项，为非接触式RFID应用提供了理想的解决方案。"

RT3070_Linux_STA_V2.5.0.3_DPO.zip_RT3070_Linux_STA_V2 _rt3070_si

2022-09-20 上传
标题中的“RT3070_Linux_STA_V2.5.0.3_DPO.zip_RT3070_Linux_STA_V2 _rt3070_si”提到了一个无线网卡驱动程序，针对RT3070芯片，适用于Linux操作系统。这个驱动程序的版本是V2.5.0.3，DPO可能代表“Driver Package ...

Android_OneApp_v2.2.4_b9_Release_09272_jiagu_sign.apk.1

2022-10-17 上传
Android_OneApp_v2.2.4_b9_Release_09272_jiagu_sign.apk.1

pdf分割工具Simpo_PDF_MergeSplit_2.2.1.0.exeSimpo

2010-11-06 上传
Simpo_PDF_MergeSplit_2.2.1.0.exe

SELECT bd.SENSOR_ID as sensorId, si.sensor_name AS sensorName, si.dam_id as damId, bd.WATER_LEVEL AS waterLevel, bd.WATER_TOP AS waterTop , bd.HOLE_DEPTH AS holeDepth, td.height-bd.WATER_TOP AS buryDepth FROM ( SELECT DAM_ID,SENSOR_ID, WATER_LEVEL,WATER_TOP, HOLE_DEPTH FROM B_DAM_WATER_LEVEL bd1 WHERE bd1.RN = 1 AND EXISTS ( SELECT 1 FROM B_DAM_WATER_LEVEL bd2 WHERE bd2.SENSOR_ID = bd1.SENSOR_ID AND bd2.UPLOAD_TIME > bd1.UPLOAD_TIME ) ) bd WHERE bd.SENSOR_ID = si.sensor_id AND bd.DAM_ID = td.dam_id; 这个语句是错误的

2023-06-02 上传
si.sensor_name AS sensorName, si.dam_id AS damId, bd.WATER_LEVEL AS waterLevel, bd.WATER_TOP AS waterTop, bd.HOLE_DEPTH AS holeDepth, td.height - bd.WATER_TOP AS buryDepth FROM ( SELECT DA...

select t.id, t.organization_id, t.name, t.id_card, t.sex, t.birthday, t.phone, t.introduction, t.subject_id, t.position_type, t.ntce_no, t.status, t.check_status, t.remark, o.name as organization_name, si.name as subject_name, t.create_time from t_teacher_info t join t_organization_info o on o.id = t.organization_id join sys_user su on o.id = su.organization_id join sys_dept sd on sd.dept_id = su.dept_id left join t_teacher_subject ts on ts.teacher_id=t.id left join t_subject_info si on si.id = ts.subject_id 这是oracle数据库 如何对si.name 加wm_concat函数

2023-07-12 上传
对于Oracle数据库中的si.name字段使用wm_concat函数，你可以按照以下方式修改你的查询语句： ```sql SELECT t.id, t.organization_id, t.name, t.id_card, t.sex, t.birthday, t.phone, t.introduction, t...

def sign_in_f(): user_si_name = name_entry.get() user_si_pass = password_entry.get() if user_si_name in success: i = success.index(user_si_name) if success[i+1] == user_si_pass:加一段结束程序的代码

2023-05-25 上传
user_si_name = name_entry.get() user_si_pass = password_entry.get() if user_si_name in success: i = success.index(user_si_name) if success[i+1] == user_si_pass: # 执行一些操作 # ... sys.exit()...

SELECT s.student_num, s.student_name, '计算机学院' AS college_name, c.level, c.class_name FROM student_info s JOIN classes c ON s.class_id = c.id JOIN volunteer_time_table_item t ON s.student_num = t.student_num INNER JOIN student_info s ON c.id = s.college_id INNER JOIN volunteer_time_table_item t ON s.student_num = t.student_num INNER JOIN volunteer_time_table vtt ON vtt.id = t.volunteer_time_table_id WHERE c.college_id = 1 AND s.level = 2021 AND vtt.volunteer_status = 5 AND vtt.deleted = 0 AND t.deleted = 0 AND s.deleted = 0 AND c.deleted = 0 ORDER BY t.volunteer_time_value DESC LIMIT 5;哪里有问题

2023-05-15 上传
INNER JOIN volunteer_time_table_item ti ON si.student_num = ti.student_num INNER JOIN volunteer_time_table vtt ON vtt.id = ti.volunteer_time_table_id WHERE c.college_id = 1 AND s.level = 2021 AND ...

for i in df_si["调单商户号"]: in1 = 0 out = 0 if(df_mingxi[df_mingxi['调单账户号码'] == i].shape[0] == 0): df_si.loc[df_si["调单商户号"] == i, "进项交易次数"] = 0 df_si.loc[df_si["调单商户号"] == i, "出项交易次数"] = 0 else: count_io = df_mingxi[df_mingxi['调单账户号码'] == i]["收付"].to_list() in1 = pd.value_counts(count_io)["进"] out = pd.value_counts(count_io)["出"] df_si.loc[df_si["调单商户号"] == i, "进项交易次数"] = in1 df_si.loc[df_si["调单商户号"] == i, "出项交易次数"] = out

2023-06-06 上传
这段代码的作用是遍历 DataFrame `df_si` 中的所有调单商户号码，并根据它们在 DataFrame `df_mingxi` 中的收付情况，更新 DataFrame `df_si` 中的进项交易次数和出项交易次数列。 具体来说，代码首先使用 for 循环...

if __name__ == '__main__': os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # you can modify your file path file_train = './data/train.csv' file_test = './data/test.csv' embed_dim = 8 read_part = True sample_num = 6000000 test_si

2023-07-12 上传
ze = 1000000 # you can modify your model path model_path = './model' # parameters for training batch_size = 1024 epoch = 5 learning_rate = 0.001 # you can modify the number of hidden units hidden_...

class DyCAConv(nn.Module): def __init__(self, inp, oup, kernel_size, stride, reduction=32): super(DyCAConv, self).__init__() self.pool_h = nn.AdaptiveAvgPool2d((None, 1)) self.pool_w = nn.AdaptiveAvgPool2d((1, None)) self.pool_h1 = nn.MaxPool2d((None, 1)) self.pool_w1 = nn.MaxPool2d((1, None)) mip = max(8, inp // reduction) self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(mip) self.act = h_swish() self.conv_h = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0) self.conv_w = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0) self.conv = nn.Sequential(nn.Conv2d(inp, oup, kernel_size, padding=kernel_size // 2, stride=stride), nn.BatchNorm2d(oup), nn.SiLU()) self.dynamic_weight_fc = nn.Sequential( nn.Linear(inp, 2), nn.Softmax(dim=1) ) def forward(self, x): identity = x n, c, h, w = x.size() x_h = self.pool_h(x) x_w = self.pool_w(x).permute(0, 1, 3, 2) x_h1 = self.pool_h1(x) x_w1 = self.pool_w1(x).permute(0, 1, 3, 2) y = torch.cat([x_h, x_w, x_h1, x_w1], dim=2) y = self.conv1(y) y = self.bn1(y) y = self.act(y) x_h, x_w, _, _ = torch.split(y, [h, w, h, w], dim=2) x_w = x_w.permute(0, 1, 3, 2) x_w1 = x_w1.permute(0, 1, 3, 2) a_h = self.conv_h(x_h).sigmoid() a_w = self.conv_w(x_w).sigmoid() a_w1 = self.conv_w(x_w1).sigmoid() # Compute dynamic weights x_avg_pool = nn.AdaptiveAvgPool2d(1)(x) x_avg_pool = x_avg_pool.view(x.size(0), -1) dynamic_weights = self.dynamic_weight_fc(x_avg_pool) out = identity * (dynamic_weights[:, 0].view(-1, 1, 1, 1) * a_w + dynamic_weights[:, 1].view(-1, 1, 1, 1) * a_h + dynamic_weights[:, 1].view(-1, 1, 1, 1) * a_w1) return self.conv(out)在里面修改一下，换成这个y = torch.cat([x_h+x_h1, x_w+x_w1], dim=2)

2023-06-01 上传
如果您想将原来代码中的y拼接成x_h+x_h1和x_w+x_w1，可以按照以下方式进行修改： ``` # 将原来的y拆分成x_h, x_w, x_h1, x_w1 x_h, x_w, x_h1, x_w1 = torch.split(y, [h, w, h, w], dim=2) # 将x_h和x_h1相加，x_...
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