I am a Zoologist from the Arduino Chinese community. I personally like to make all kinds of interesting things with Arduino. This time I brought you a Tetris game console made by ESP32 (DFRobot’s FireBeetle).
In some specific cases, the company's machines do not support the USB interface, so you will encounter problems if you want to use a USB interface keyboard and mouse on your computer. For traditional wired mice, it can be done directly through the adapter on Taobao (this can be achieved because the USB mouse chip can directly identify the currently used interface), but unfortunately, the wireless mouse does not have such an interface, so it cannot be used through This adapter is used to complete the transfer. So, use an Arduino to make a device that converts wireless USB to PS/2.
Introduction: A simple digital power amplifier based on PAM8610.
Introduction: No matter how rubbish the computer configuration is, installing RGB light pollution will increase the light pollution performance by 30% - Picture Bar boss
The wireless wearable surface electromyographic signal acquisition and monitoring system designed this time can mainly complete the collection, filtering, dynamic visual detection of human body surface electromyographic signals, and control mechanical retraction through electromyographic signals. It mainly includes the collection end, the receiving end and the monitoring host computer. The collection end is mainly responsible for collecting the surface electromyographic signals of the human body, quantifying them into digital signals, and transmitting them to the receiving end. The receiving end is mainly responsible for transmitting the received surface electromyographic signals to the computer for processing through the serial port. The monitoring host computer can obtain the surface electromyography signal transmitted from the receiving end through the serial port, thereby calculating the characteristics of the signal, including: mean value (MEAN), absolute mean value (MAV), root mean square (RMS), variance (VAR), Zero-crossing rate (ZC) and waveform length (WL). Moreover, the original surface EMG signals and characteristics are visualized and dynamically monitored through a dynamic line chart. At the same time, the control of the robot hand can also be realized through the receiving end.
This work uses the WeChat applet as the APP and uses the mobile phone screen as the terminal to display low-power dynamic current waveforms. The APP integrates current range switching, sampling period setting, dynamic trigger mode, dynamic calibration, run/stop mode, zoom control, waveform data saving, Bluetooth automatic reconnection, automatic sleep (AUTO POWER OFF), low battery alarm and other functions. This current analyzer uses TI's CC2640 BLE solution to perform high-precision measurements of dynamic low-power current. The current range covers nA, uA, and mA. The measurement error at mA level is controlled within ±1%, and the error at uA level is within ±1%. Within 1%, the nA level error is within ±45nA, and the sampling period has 5ms, 50ms, 500ms, 5s and other gears. The current meter is equipped with a 1800mah battery. The full-speed operating current is about 3.5ma and the working time is about 500 hours. The automatic sleep operating current is about 170uA and the sleep standby time can reach about 10,000 hours. The shutdown operating current is 0uA. The lithium battery can be charged repeatedly. About 800 times.
In order to realize the functional design of sports data detection equipment, I analyzed the movement patterns of most of the existing gym equipment and found their commonalities. The motion space of the gym is fixed, so its motion can be described as periodic reciprocating motion, and single-cycle motion can be described in terms of linear displacement and angular displacement. Therefore, the number of exercises, frequency, etc. can be calculated through the ranging sensor and angle sensor, and then combined with the model of the fitness equipment (obtaining the amount of exercise in a single cycle), we can estimate the overall amount of exercise, the number of exercises, and the frequency of exercise in a fitness session. Wait for data. Data can be transmitted to the mobile APP through the Bluetooth module, or transmitted to the network through other methods.
Introduction: A common-emitter single-tube amplifier is designed, which can adjust the position of the static operating point by adjusting the sliding rheostat to study the distortion waveform.
The radar-sensing smart lighting is a smart, energy-saving lighting with radar human body sensing and real-time data transmission. The smart lighting uses a 24GHz radar sensor, LoRa wireless communication module and embedded microprocessor. This smart lighting can distinguish between vehicles and pedestrians, automatically turn on and off lights, automatically upload data, and can also be remotely managed manually. The radar human body sensing of this smart lighting is not affected by temperature, brightness, and weather, and will not cause privacy concerns. The installation and It is very convenient to use; this smart lighting uses cluster technology to connect thousands of sensor lights to the same local area network, and uses the LoRaWAN gateway to send data to the cloud, enabling online cluster management of hundreds of thousands of smart lights. It is expected that the smart lighting will play a huge advantage in energy saving, traffic optimization, smart city and other aspects.
The 6418 tube headphone amp uses 6418 tubes as the preamplifier and NE5532 or OPA2604 as the power amplifier. It is boosted and powered by a lithium battery and is only about the size of a mobile phone.
Introduction: When the USB 5V power supply is connected, it can continuously emit a temperature just enough to warm your hands. It can be used to warm your hands, stomach, hot milk, etc.
Smart socket, ESP8266, blinker for app. Functions: Button on/off, APP switch, timer switch, Tmall elf control.
Xiaobishe, WiFi dual color temperature dimming lamp
Combined with low-power AI chips and mobile networks, the person/object detection model is deployed to the device to achieve lower power consumption, real-time response, and traffic saving.