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Temperature Controlled RGB LED Light Stick – Mood Light

The circuit presented here is a LED light stick which changes its color in respect with the room temperature. This is an Arduino compatible open source project consisting of 20 x WS2812B Addressable RGB LEDs, LM35 Temperature Sensor and ATmega328 microcontroller. Atmega328 microcontroller reads the LM35 temperature sensor and changes the RGB LEDs color as per sensor analog output. Multiple units of this board can be installed in the room to create full light color effects. Colors of this light will change as the atmosphere’s temperature changes. Placing the LED stick underneath a furniture or behind picture frames on the wall helps create a nice effect as well as improve the ambience of the room.

Features

Operating Supply 5V DC
Current Consumption 200mA (When all LEDs on)
20 x WS2812 RGB Addressable LEDs
On Board LM35 Temperature Sensor
PCB dimensions: 159.86 x 13.81 mm

Arduino I/O Configuration (ATmega328 Chip)

WS2812 LEDs connected to Digital Pin D2
Temperature Sensor LM35 Connected to ADC Analog pin A0

Programing

Example Arduino Code is provided to test the board and the code and can be modified or re-written as per user requirements.

Read the following tutorial about Atmega328 microcontroller Programming using Arduino IDE: https://www.arduino.cc/en/Tutorial/BuiltInExamples/ArduinoToBreadboard

WS2812B LED

WS2812B is an intelligent control LED light source that the control circuit and RGB chip are integrated in a package of 5050 components. It internal include intelligent digital port data latch and signal reshaping amplification drive circuit. Also include a precision internal oscillator and a voltage programmable constant current control part, effectively ensuring the pixel point light color height consistent. The data transfer protocol uses single NZR communication mode. After the pixel power-on reset, the DIN port receive data from controller, the first pixel collects initial 24bit data then sent to the internal data latch, the other data which reshaping by the internal signal reshaping amplification circuit sent to the next cascade pixel through the DO port. After transmission for each pixel, the signal to reduce 24bit. pixel adopt auto reshaping transmit technology, making the pixel cascade number is not limited the signal transmission, only depend on the speed of signal transmission. RESET time>280μs, it won’t cause wrong reset while interruption, it supports the lower frequency and inexpensive MCU. Refresh Frequency updates to 2KHz, Low Frame Frequency and No Flicker appear in HD Video Camera, it improves excellent display effect. LED with low driving voltage, environmental protection and energy saving, high brightness, scattering angle is large, good consistency, low power, long life and other advantages. The control chip integrated in LED above becoming more simple circuit, small volume, convenient installation.

Schematic

Parts List

SR.	QNTY	REF.	DESC	VENDOR/DIGIKEY/MOUSER
1	1	CN1	4 PIN MALE HEADER 2.54MM PITCH	DIGIKEY S1011EC-40-ND
2	24	C1 TO C20,C23,C25,C26,C27	0.1uF/50V SMD SIZE 0805	YAGEO
3	2	C21,C22	22PF 50V SMD SIZE 0805	YAGEO
4	1	C24	100uF/16V SDM SIZE 1210	YAGEO
5	1	R1	10K 5% SMD 0805	YAGEO
6	1	R2	10E 5% SMD SIZE 0805	YAGEO
7	20	U1 TO U20	WS2812B LED 5050	DIGIKEY 1528-1104-ND
8	1	U21	ATMEGA328-DIP28	DIGIKEY ATMEGA328P-PU-ND
9	1	U22	LM35 SMD SOIC8	DIGIKEY LM35DMX/NOPBCT-ND
10	1	Y1	16MHZ	DIGIKEY X1103-ND

Connections

Gerber View

Photos

Video

WS2812B Datasheet

DBM10 – Low-power Edge Ml/AI SoC with DSP and Neural Network Engine

Posted on 19 January, 2021 by Emmanuel Odunlade

DSP Group, a leading global provider of wireless and voice-processing chipset solutions has introduced a low-power AI/ML-enabled dual-core SoC called DBM10.

According to the CEO of the company, almost all edge applications for AI require the ultimate as regards low power, small form factor, cost effectiveness, and fast time-to-market. So it’s with great enthusiasm that they bring DBM10 to the desk of their customers and partners.

“Our team has worked to make the absolute best use of available processing power and memory for low-power AI and ML at the edge—including developing our own patent-pending weight compression scheme—while also emphasizing ease of deployment. We look forward to seeing how creatively developers apply the DBM10 platform.”

DBM10 AI SoC is a high-performance and low power chip with Digital Signal Processor and dedicated nNetLite Neural Network Engine. It is designed to improve voice and sensor processing and also ensure that consumption is low when working with sufficient-sized neural networks.

DBM10 is supported by an embedded memory along with serial and audio interfaces that make it easily compatible with many external devices, including application processor, codecs, microphones, and sensors. It is well suited for wearables, tablets, smartphones, true wireless stereo headsets and smart home gadgets like remote controls.

Features of the DBM10 AI SoC include:

SPI and I2C serial interfaces
Universal Asynchronous Receiver Transmitter for serial communication
PDM and TDM audio interfaces
General Purpose IOs for MCUs and peripherals
32-bit timer
Watchdog timer
Low-power ADCs for analog microphones
JTAG for debugging, and,
48-pin Quad Flat No-leads package, and,
Suitable for AI voice-based applications.

Specifications of the neural network processor:

Has a compact form factor of about 4 mm x 4 mm
Has ~ 500 µW ultra-low-power inference consumption for voice NN algorithms
can run “Hello Edge 30-word detection” model @ 1 MHz (125 MHz available)
allows porting of large models, typical about 10s of megabytes, without significant accuracy loss using model optimization and compression.

The SoC offers “full flexibility of partitioning innovative algorithms between DSP and NN processor”, and allows machine learning, voice and sensing functions such as noise reduction, voice trigger, voice authentication, acoustic echo cancellation, wake-word detection, voice activity detection, and sensor data processing.

The SoC also offers a real-time software framework that has an internal host for communication with an external master processor running on Linux OS. The framework supports RTOS and provides drivers for processor peripherals and Android/Linux interface.

We don’t have any information on price, availability and shipment yet, but other relevant details about the SoC can be found either in the company’s press release page or the product page.

PD Micro – Breadboard-Friendly USB-C Power Supply Based on Arduino

Posted on 19 January, 202119 January, 2021 by Micael Coutinho

We have all been there. Everytime we start adding new peripherals, sensors and actuators to our projects, we eventually will reach a point where the 3.3 and 5 V voltages from the microcontroller (when they are available) are not enough, either because we require more current than what the MCU pins can deliver, or we have different voltage requirements. A good example of this are motors and high power LED’s. But, if a microcontroller can’t deliver, then something else will. But what if the solution was already there, with your MCU and enabled you to have greater control over these voltages? Well, with the help of some USB Type-C magic and the new Arduino MCUs, you can!

The PD Micro is an innocent-looking Arduino at first, friendly enough for you to place it into a breadboard and start tinkering. But when you get to the nitty-gritty, you are going to find much more, something rather unique on this type of board. Through the use of a 30 V P-channel MOSFET load switch controlled by an Arduino MCU and a library developed by Ryan Ma, you can safely enjoy USB Type-C’s promising USB PD (Power Delivery), which allows you to negotiate power of up to 100 W. The PD Micro adds some extra protection to your circuitry by only delivering the power required is ready and meets the target voltage and current levels. A really cool thing about it is that you can get away with not using another board. As you have already an Arduino at your disposal, all you might need for your project is already there.

Regarding specifications, you have at your disposal:

ATmega32U4 MCU, clocked at 16 MHz (Arduino Pro Micro)
I / O: 9x 10-bit ADC pins, 12x Digital I / O’s, 5x GND pins, 3x VBUS pins (USB Power Delivery) and 1x 5V pin
Power: USB PD grants negotiable 5 V, 9 V, 12 V, 15 V or 20 V at a maximum of 5 A (100 W at 20 V)
PD status LED’s: Tx / Rx activity, 5x for Power Delivery voltage level and 5x for current level
DC-DC converter for efficient 5 V 0.5 A output
Size: 1.6 x 0.7 inches (plus 0.3 inches for the power connector pins)

When it comes to the control of USB Power Delivery, this device has the unique feature of allowing you to control it with an Arduino. There are other boards that do this job, but I doubt you will find something nearly as convenient to use and with all the functionalities this one has. Lastly, this is still a CrowdSupply project, which already smashed its goal, while there are still some days left. You can get the cheapest kit, consisting of a PD Micro and the necessary headers and terminals for it at $28, with free US shipping.

PD Micro CrowdSupply link: https://www.crowdsupply.com/ryan-ma/pd-micro

Ambarella CV5 AI Vision SoC for Low Power Computer Vision Applications

Posted on 19 January, 2021 by Saumitra Jagdale

Ambarella announced the CV5 AI Vision SoC for AI applications using its specialized computer vision technology. The AI Vision SoC features 8K video or 4K video processing along with video encoding/decoding, and CVflow computer vision processing in a single, low-power design. The SoC is fabricated using 5nm technology and allows the development of intelligent automotive camera systems, consumer cameras (drone, action, and 360°), and robotic cameras.

Talking about its low power design, the SoC can function below 2W for 8K video recording at 30 fps, and below 5W for 8K video recording at 60 fps. The CVflow architecture of the device offers the DNN (Deep Neural Network) processing hence, compatible for intelligent cameras in numerous AI applications.

The target applications of the SoC include sports and robotics cameras. It also offers advanced image processing for VR cameras. It supports multi-channel drive recorders and data loggers. The SoC is expected to grow in the automobile industry for ADAS systems with multiple cameras for driver assistance. This includes lane departure warning, forward collision warning, and driver monitoring.

Key Features of CV5 AI Vision Processor

High-Efficiency Video Encoding with H.265 and H.264 video compression
Flexible multi-streaming capability and 8KP60 video performance
Multiple CBR and VBR bit rate control modes for encoding
CNN / DNN-based processing for detection and classification
Computer Vision engine for conventional CV operations
CNN toolkit for easy porting of neural networks implemented in Caffe, TensorFlow, PyTorch, or ONNX frameworks
Advanced image processing includes multi-exposure HDR and EIS
LED flicker mitigation and 3D motion-compensated temporal filtering (MCTF)
RGGB / RCCB / RCCC / RGB-IR / monochrome sensor support

The image signal processor (ISP) of the SoC can perform imaging in low-light conditions. Also, the high dynamic range (HDR) processing functions for optimized withdrawal of image details in high contrast conditions. “The CV5’s CVflow architecture provides computer vision processing at full 8K, enabling image recognition over long distances, with high accuracy.”

“The SoC also combines Ambarella’s powerful CVflow AI engine with dual Arm A76 CPUs to provide the performance necessary for a wide range of AI-based algorithms. CV5’s CVflow AI engine can accelerate simultaneous localization and mapping (SLAM), path planning, and obstacle detection, and avoidance algorithms for navigation and autonomous operation.”

For using customized neural networks for dedicated use cases there on the CV5 AI Vision SoC, Ambarella’s software development kit offers a complete set of tools for software and AI implementation. This development kit is also compatible with the company’s previous version CV28M SoC. The software ecosystem includes a compiler, debugger, and support for various ML frameworks, such as PyTorch, ONNX, Caffe, and TensorFlow.

For more information visit the official company page. Currently, the pricing information of the SoC is not available. Images and technical specifications have also been taken from the company page and announcement.

MKR SharkyPro Boards Packs STMicroelectronics’ STM32WB55CG into an Arduino MKR Form Factor

Posted on 19 January, 202119 January, 2021 by Emmanuel Odunlade

In the midst of discovering wireless modules that simplify Bluetooth LE, Zigbee, OpenThread connectivity, it turned out that Midatronics also has a SharkyPro module based on STM32WB55, and they have launched a pair of Arduino MKR form factor development boards called MKR SharkyPro I & II.

The Sharky MKR boards are built around the SharkyPro module that has STMicroelectronics’ STM32WB55CG/CE MCU with a 64 MHz Arm® Cortex®-M4 core serving as the application processor and a 32 MHz Arm® Cortex®-M0+ core as the network processor. They are also Bluetooth 5.0-certified with support for Mesh 1.0 software, proprietary BLE stacks, OpenThread software, and other IEEE 802.15.4 proprietary stacks like ZigBee ® , or proprietary protocols.

Key Features and Specifications SharkyPro I & II include:

SharkyPro Wireless Modules
- STMicro STM32WB55CG/CE wireless MCU with an Arm Cortex-M4 core @ 64 MHz and an Arm Cortex-M0+ core @ 32 MHz; 512KB flash; 256KB SRAM
- 2.4 GHz – RF transceiver
- Bluetooth 5.0 Low Energy, Bluetooth Mesh 1.0, OpenThread, Zigbee, General IEEE 802.15.4 protocol stacks
- Onboard chip antenna (for SharkyPro I)
- SMA antenna connector (for SharkyPro II)
- 3.3V supply voltage
- 13 nA consumption in shutdown mode
- 600 nA consumption in Standby mode + RTC + 32 KB RAM
- Operating temperature: -40 °C to 85 °C
- RX Sensitivity: -96 dBm
- Dimensions: 23 mm x 14.6 mm
1x Micro USB port for power and programming
2x 14-pin headers compatible with Arduino MKR pinout and featuring USART, ADC, SPI, I2C, QSPI
1x 8-pin SWD connector
1x Reset button
1x User-programmable input button
User LED
Power Supply: 5V via Micro USB port, 2.5 to 5.5V via Vin pin, 2.0 to 5V via Vbatt pin (battery)
Dimensions:
- MKR SharkyPro I – 63 mm x 25 mm
- MKR SharkyPro II – 72.5 mm x 25mm

SharkyPro I & II have the same specifications but can be distinguished by whether they have an on-board chip antenna or an SMA connector for an external header. If it’s the former, it is the MKR SharkyPro I but if it is the latter, it is the MKR SharkyPro II. Also, when it comes to size, SharkyPro II is also a little bigger than the SharkyPro I.

The two boards are breadboard-friendly and are designed to be pin-compatible with Arduino’s MKR boards. You can use Arduino IDE to program the boards or the SWD header alternatively, via an STLink in-circuit debugger and programmer alongside SW4STM32, Atollic, IAR, or Keil IDE.

The boards and the module are currently available but nothing about their prices has been disclosed publicly. Other details however are available on the product pages for the development board and the module.

I2C Detective identifies the I2C devices connected to your microcontroller

Posted on 19 January, 2021 by mixos

The I2C Detective identifies the I2C devices connected to your microcontroller from a database of the most popular I2C sensors and other devices. It lists each device on the I2C bus, and can distinguish between multiple candidates at a particular address by reading the device IDs. by David Johnson-Davies

In the above example, although there are other possible sensors with addresses 0x1C, 0x39, 0x6A, and 0x77, the I2C Detective eliminates these as possibilities by reading the registers corresponding to the sensor device IDs to identify them. If a sensor doesn’t provide a device ID, such as with the SHT30 on address 0x44, the I2C Detective lists all the popular sensors that support this address.

The I2C Detective will run on any Arduino-compatible board, from the Arduino Uno upwards.

I2C Detective identifies the I2C devices connected to your microcontroller – [Link]

Himax WE-I Plus EVB: Computer vision and AI on the Edge

Posted on 18 January, 202118 January, 2021 by Micael Coutinho

Edge computing has many great advantages. When you take some resource-heavy work from servers and let your less capable but still slightly powerful microcontrollers, you end up freeing server resources. Also, depending on what you are doing, you can get away with not using a server at all! There is a lot you can do these days, when it comes to AI and machine learning on the edge. A great example of it lies on the Himax WE-I Plus EVB development board, which we will take a look today.

The Himax We-I Plus EVB is a small, yet powerful development board for AI and computer vision applications. Being developed by Sparkfun, it makes use of Google Tensorflow Lite for Microcontrollers framework and Synopsys embARC MLI library, it contains all the necessary ingredients for your computer vision projects. It makes use of a WE-I Plus ASIC with an embedded DSP developed by Synopsys running at 400 MHz and some beefy 2 MB internal SRAM and Flash to deploy your neural network models. Regarding peripherals, you have at your disposal a CMOS image sensor capable of delivering 640 x 480 pixel images at 60 frames per second, along with a 3-axis accelerometer, and 2 microphone sensors.

Taking a detailed glance at the specs:

WE-I Plus ASIC (HX6537-A) – ARC 32-bit EM9D DSP with FPU, clocked at 400 MHz
Memories – 2 MB SRAM and 2MB Flash
Himax HM0360 AoS TM ultra-low power CCM – 1 / 6” CMOS sensor, with 640 x 480 pixel resolution at 60 FPS
FTDI USB to SPI / I2C / UART bridge
STM LSM9DS1 3-axis accelerometer
2x microphones (L/R)
2x LED’s
Expansion: 1x I2C port, 3x GPIO, power and ground pins

The Himax WE-I Plus EVB's size really is impressive — The Himax WE-I Plus EVB’s size really is impressive

Now, besides the interesting hardware package it proves to be, a means to develop your projects easily and fast is still needed. This is where its integration with Edge Impulse comes in. Being the leading development platform for machine learning on edge devices, based on TinyML (which you can read about below), making this process a lot easier for you. There, you can train models with the datasets collected with your edge device and then deploy them on your device with almost no hassle. It is a very powerful tool, whether if you are taking your first steps in machine learning or an advanced developer, so I recommend you give it a try. It has a free tier, so you have nothing to lose.

As some final thoughts on the Himax We-I Plus EVB, it comes at a price of $65, which seems reasonable, considering you can fit it almost anywhere and also its processing capabilities.

TinyML article: https://www.electronics-lab.com/tinyml-enables-ai-in-smallest-endpoint-devices/
Himax WE-I Plus EVB link: https://www.sparkfun.com/products/17256
Edge Impulse link: https://www.edgeimpulse.com/

DFI’s AL05P is a 2.5-inch Pico-ITX SBC with PoE and an Apollo Lake Processor

Posted on 18 January, 2021 by Emmanuel Odunlade

DFI has launched AL05P – a 2.5-inch Pico-ITX SBC that offers PoE and is powered by an Intel Atom® Processor E3900 Series.

AL05P is not the first Intel Apollo Lake Pico-ITX board to appear in the market, but it is amongst the first to come with a PoE support (Power-over-Ethernet). The Linux-ready AL05P board is equipped with 4GB RAM, up to 64GB eMMC, a mini DisplayPort output, two USB 3.1, one GbE port with isolated PoE, and one mini PCIe for seamless and stable operations. The board also has an extended temperature range of -20 to 70°C that seems to be absent in many of the small SBCs with PoE.

Highlights:

Intel® Pentium®/Celeron®/Intel Atom® Processor E3900
Support LPDDR4 memory down up to 4GB
Rich I/O: 1 LAN, 1 COM, 2 USB 3.0, 2 USB 2.0
Expansion and Storage: 1 Mini PCIe, 1 SMBus, 1 eMMC
PoE single board solution support PoE PD 48VDC
15-Year CPU Life Cycle Support Until Q4′ 31 (Based on Intel IOTG Roadmap)

Features and Specifications include:

Any of the following SoCs:
- Intel Atom x5-E3940 processor, quad-core, 2MB cache, 1.6/1.8 GHz, Intel HD Graphics; 9.5W TDP
- Intel Atom x5-E3930 processor, dual-core, 2MB cache, 1.3/1.8 GHz, Intel HD Graphics; 6.5W TDP
- Intel Pentium N4200 processor, quad-core, 2MB cache, 1.1/2.5 GHz, Intel HD Graphics; 6W TDP
- Intel Celeron N3350 processor, dual-core, 2MB cache, 1.1/2.4 GHz, Intel HD Graphics, 6W TDP
Up to 4GB LPDDR4-2400 via single channel
16GB/32GB/64GB eMMC
128 Mbit SPI flash (UEFI boot only)
Gigabit Ethernet RJ45 port
Mini DP++ up to 4096 x 2160 @ 60Hz
Full-size Mini PCIe slot
2x USB 3.1 Gen1 ports
2x USB 2.0 interfaces
1x RS-232/422/485 header
1x 8-bit GPIO
1x SMBus header
RTC + CR2032 coin cell battery
Watchdog timer
TPM 2.0
15mm thick heat spreader for fanless operation
Power Supply: 12V DC header or 48V DC via PoE
Power Consumption:
- Typical: E3950:12V @ 0.33A (3.96Watt)
- Max: E3950:12V @ 1.76A (21.12Watt)
Temperature Range:
- Operating: 0 – 60°C, -20 – 70°C
- Storage: -40 – 85°C
Humidity: 5 – 90% RH (operating and storage)
Dimensions: 100 mm x 72 mm (2.5″ Pico-ITX form factor)
Operating System: Linux, Windows 10 IoT Enterprise (64-bit)

The Intel Atom® E3900 series in the AL05P is of high-performance and low-power (9W), so we expect it to be able to give a real-time response to edge events, handling image recognition and data streaming analysis easily.

AL05P can be closely compared with a similar AL051 that sells for $320 on Amazon. One major difference between them is that AL05P has PoE support while AL051 does not. It is believed that the AL05P replaced one of the two GbE ports found on the AL051 to provide its 48VDC PoE capability. AL05P also has just one (mDP++) with no M.2 socket and no SATA port while AL051 also has up to three display interfaces (mDP++, VGA, LVDS), one M.2 socket, and one SATA port.

DFI AL05P has been launched but there’s no word on pricing and availability yet. You might want to take a look at the datasheet of the board or check on DFI’s announcement page or the product page for additional information concerning the board.

$149 BeagleV is a powerful and open-hardware RISC-V Linux SBC

Posted on 18 January, 2021 by Emmanuel Odunlade

Two reliable parties, BeagleBoard.org foundation, and Seeed Studio have partnered with a leading RISC-V solutions provider, StarFive, to design the most affordable Linux-capable RISC-V SBC called BeagleV. So now, you don’t have to spend several hundred dollars, or even over a thousand dollars to have a complete system that runs Linux on RISC-V hardware.

The $149 BeagleV is a perfect edge computing device with powerful AI performance. The board is equipped with RISC-V U74 dual-core with 2MB L2 cache @ 1.0GHz with 8GB of LPDDR memory, ISP/NNE, PCle 3.0, GbE, and dedicated hardware encoder/decoder supporting H.264 4k@60fps. The processor offers rich and powerful AI features including the Vision DSP Tensilica-VP6 for computing vision, NVDLA engine, and the neural network engine.

Features and Specifications:

SiFive U74 RISC-V Dual core processor running at 1.5GHz and with 2MB L2 cache
Highly integrated Vision DSP Tensilica-VP6 for computing vision
NVDLA Engine (configuration 2048 MACs running at 800MHz – 3.5 TOPS)
Neural Network Engine (1024 MACs running at 500MHz – 1 TOPS)
8GB LPDDR4 (2x 4GB LPDDR4 SDRAM)
1x MicroSD card slot (operating system and data storage)
2.4GHz 802.11b/g/n Wi-Fi 4
Bluetooth 4.2
Video Decoder/Encoder (H264/H265) up to 1 channel 4K@60FPS or 8 channel 1080p@30FPS
Dual channels of ISP with each supporting up to 4K@30FPS
Audio Processing DSP and sub-system
2x MIPI-CSI
1x MIPI-DSI up to 4Kp30
1x HDMI support up to 1080P@30FPS
MIPI-CSI TX support for video output after ISP and AI processing
JPEG Encoder/Decoder
4x USB 3.0 ports
1x GbE port
1x 3.5mm Audio jack
40-pin GPIO header (28 x GPIO, I2C, I2S, SPI, UART)
TRNG and OTP support
DMAC and QSPI support
1x Reset button
1x Power button, and,
5V@3A power supply with USB Type-C

Layout

The first production batch of BeagleV due in March will be “GPU-less”, while the next batch slated for September will integrate an Imagination Technology GPU core. The first version will also be coming with only the $149 8GB RAM model but subsequent production will eventually include a cost-reduced $119 4GB model.

Since the demand for the board is expected to be higher than supply, at least for the first batch, BeagleBoard.org foundation and Seeed Studio have asked those who are interested to pre-order now ahead of its May/April launch. They are to fill in an application form with details of their planned projects.

BeagleV will be open-source hardware, so we expect that design files, firmware, and the software will be made available publicly. The board will also be supported by mainline Linux, Debian-based software image, Fedora, and FreeRTOS.

Further details may be found on the official website.

LED Fading Effect / LED Strobe using 555

Posted on 18 January, 2021 by mixos

In general, generating an LED fade effect requires a microcontroller or another expensive circuit. We have built this low-cost LED fading board using an inexpensive 555 Timer. The 555 timer is used as an astable multivibrator, which generates low-frequency pulses, further, this pulse train is feed to the base of BJT transistor TIP122 with a series resistor along with a high-value electrolytic capacitor. The combination of resistor R5 and capacitor C6 at the base of Q1 gradually increases and decreases the base voltage which provides soft ON/OFF (Ramp) to the load. The circuit can drive a load up to 18W without heatsink. An onboard potentiometer is provided to adjust the frequency of flash. Connector CN1 DC is for 12V Input, Connector CN2 is provided to connect the LED. This circuit also can be used as a LED Strobe Light and covert a regular 12V LED Light into a Flashing Strobe Light with few changes of components.

LED Fading Effect / LED Strobe using 555 – [Link]