https://community.element14.com/products/manufacturers/analog-devices/At Analog Devices, we invent highly integrated solutions that make technology seamless. We channel our collective expertise to stretch the limits of technology, understand your needs, and help you get to market fasteren-USTelligent Community 12https://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifier/234617Thu, 26 Mar 2026 08:47:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:6da47e43-d0ca-4b13-a55e-8028470303dajc2048What is the "issue"? The datasheet says the pins labelled as GND should be connected to RF/DC ground. It doesn't say they're all connected inside the device. The resistance you measure between 6 and 7 is the bias for the FET. You'd expect to see it there. Presumably you've connected a decoupling capacitor to 7 and not a GND in your circuit. If you've connected a ground to 7, you'll get whatever the FET current is with the gate at 0V, instead of the 100mA or so at their dc bias point (don't leave it running like that - I doubt the package can manage the dissipation: it probably gets fairly warm even at the 100mA). 2, 4, and 16 are dummies that you can use to run the surface ground into the paddle area - 2 and 4 allow you to bring the transmission line all the way in without a discontinuity. 16 is to run a ground in alongside the vdd to improve the decoupling performance (the return current has to get across to 6). The designer is just steering you as to how they thought the layout would work. The paddle will be the substrate connection, which you'll probably want to heatsink with vias down to the plane. I would guess the input termination goes down to the paddle, that would seem to make most sense, but I'm not very familiar with this kind of stuff - it might go to GND at 6. The chip designer seems to have intended the copper under the paddle to be the common point between input and output. What's interesting is that the 3rd party who designed the evaluation board have done the layout in a different way, isolating the paddle on the surface layer and using the plane for everything. I would assume they know what they're doing, so it would be interesting to know the pros and cons of the two approaches. Disclaimer: I've never done any RF, so decide for yourself if this makes any sense. I do have vague memories of how a FET works, though (retired engineer!).https://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifier/234614Thu, 26 Mar 2026 02:56:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:30648d1d-7d67-4f24-a932-47fa352766a6alphaiseThe issue is consistent both before and after applying power. The parts were purchased from Element14 (Singapore), and they had sent over replacement units after I alerted them of the issue - but the shorting is still present (i.e., no change). I used the continuity test on various multimeters for measurements. The resistance between pin 15 (Vdd) and 6 (GND) is about 8.1 Ω, and 2.9 Ω between pin 15 and 7 (ACG).https://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifier/234613Thu, 26 Mar 2026 02:48:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:2d7c80d8-ac39-4c52-9d9c-c3eedf357ea5alphaiseYes, I checked against the chamfered edge on the ground paddle as well.https://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifier/234611Wed, 25 Mar 2026 18:47:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:9eff974c-8a5f-4841-a5b0-55e8ee8125a7Jan CumpsDid you use the dot as pin1 indicator?https://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifier/234598Wed, 25 Mar 2026 09:04:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:426778e5-433d-448e-a0eb-568b62e8e92fmichaelkellettIs this before or after you had applied power ? Where did you buy the parts ? How are you measuring them - when you say "shorted" what do you mean - is it the same resistance in either polarity, what is that resistance. MKhttps://community.element14.com/products/manufacturers/analog-devices/f/forum/56794/support-required-for-hmc356lp3etr-low-noise-amplifierWed, 25 Mar 2026 07:33:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:d77b5146-25da-445a-93ae-e8de3dd8eb33alphaiseDuring probing of the IC, we observed that pins 6 (GND) and 7 (ACG) are shorted together, and are also shorted to pin 15 (Vdd). However, the other GND pins (ground paddle, pins 2, 4, and 16) do not appear to be shorted together. This issue was observed consistently in 2 units that were tested. For reference, I have attached the pinout diagram and a top-view image of the IC.https://community.element14.com/products/manufacturers/analog-devices/b/blog/posts/how-do-autonomous-mobile-robots-navigate-their-environmentsWed, 04 Mar 2026 15:25:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:4b3fc129-e24f-4b6a-b06b-074801877d88SarahCrowe28by Sarvesh Pimpalkar Previously we discussed the importance that inertial measurement units (IMUs) play in localization for autonomous mobile robots (AMRs) in a Localization: The Key to Truly Autonomous Mobile Robots - element14 Community . Today we will elaborate on how navigation relies on a fusion of sensor technologies working together to allow AMRs true freedom within dynamically changing environments. So how do mobile robots learn to get around? As kids we are all thought to “stop, look and listen” before crossing a road, but does this same concept apply to robots. As humans we rely on our eyes and ears to help us “navigate” our environment, robots on the other hand use sensors to provide an awareness of their surroundings. AMRs use Simultaneous Localization and Mapping (SLAM) techniques to navigate. The process involves the AMR being driven around the facility and scanning its environment. These scans are combined and generate a complete map of the area. AMRs utilize an array of sensors and algorithms for localization and navigation. Sensor technology such as industrial vision time-of-flight cameras, radar and lidar are the “eyes” of an AMR, combined with data from IMUs and wheel odometry (position encoders). However, no single sensor is perfect. The true power lies in the diverse sensor types working together to produce effortless navigation in dynamically changing environments. Each sensor has strengths and weaknesses that are balanced out by having more than one sensor type being relied on for navigation purposes. Let’s consider how multiple sensors can enhance the overall AMR performance. Environmental Factors while Navigating Lidar sensors can be sensitive to various environmental factors, such as ambient light, dust, fog, and rain. These factors can degrade the quality of the sensor data and, in turn, affect the performance of the SLAM algorithm. Similarly, other sensor modalities can be affected by reflective surfaces, dynamic moving objects (other AMRs or workers) thus further confusing SLAM. The table below summarizes how environment affects different sensors modalities. Table 1: Comparison table of sensor modalities While IMUs and wheel odometry are not affected by visual elements within the working environment, the use of this sensor data in conjunction with visual data means the AMR can operate better in any scenario encountered. Let’s consider the challenge of navigating on a sloping floor surface. Navigating on a Slope While maneuvering on a slope, traditional SLAM algorithms encounter challenges when relying on lidar, as the 2D point data does not show gradient information. Consequently, slopes are misconstrued as walls or obstacles, leading to higher cost maps. As a result, conventional SLAM approaches with 2D systems become ineffective on slopes. IMUs help to solve this challenge by extracting gradient information to effectively negotiate navigating on a slope. How does the sensor data get combined? In a typical ROS (Robot Operating System), vision sensors along with IMU and wheel odometry are combined through a process called sensor fusion. A widely used opensource ROS package is robot_locatlizaton 1 which utilizes EKF (Extended Kalman filtering) algorithms at its core. By fusing data from diverse sensors such as lidar, cameras, IMUs, and wheel encoders, EKF helps in better estimating and understanding of the robot's state and its environment. Through recursive estimation, EKF refines the robot's position, orientation, and velocity while simultaneously creating and updating a comprehensive map of the surroundings. This fusion of sensor data enables mobile robots to overcome individual sensor limitations and navigate complex terrains with greater precision and reliability. By leveraging techniques like EKF help in collective insights of sensors, deriving meaningful sensor fusion of various sensor modalities allowing mobile robots to effectively perceive and interact with their environment and help navigate AMRs autonomously. A future blog in this series will cover the Robot Operating System in more detail. However, the focus of this blog is to leave you confident that sensor fusion offers increased reliability, increases the quality of data, while providing greater safety for objects and people within the environment as AMRs aren’t relying on a single means to navigate. To learn more visit analog.com/mobile-robotics . Reference / Resources: 1 https://docs.ros.org/en/melodic/api/robot_localization/html/index.html ADTF3175 1 MegaPixel Time-of-Flight Module ADTF3175BMLZ ANALOG DEVICES, Imaging Module, 1024 x 1024 Active Pixel, 3.5µm x 3.5µm | Farnell® Ireland EVAL-ADTF3175 Time-of-Flight Evaluation Kit EVAL-ADTF3175D-NXZ ANALOG DEVICES, Evaluation Board, ADTF3175, ADSD3100 | Farnell® IrelandperceptionADI Mobile RoboticssensorsMotor and Controlroboticsmobile roboticstime-of-flightautonomous mobile robotIndustrial Automation TechnologyOptical Sensing TechnologyMotors & Motion Controlindustrial automationTime of Flight (ToF) Sensor and SolutionsADTF3175Inertial Measurement Units (IMU)motor and motion controlIMUshttps://community.element14.com/products/manufacturers/analog-devices/b/blog/posts/localization-the-key-to-truly-autonomous-mobile-robotsFri, 09 Jan 2026 16:47:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:18a35bcd-d38f-40cc-b724-7c8b5f00bcfbSarahCrowe28by Sarvesh Pimpalkar In our previous blog Finding the Right Fit for your Industrial Automation Need - AGVs or AMRs we explored the key differences between Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). One major takeaway was that AMRs hold a clear advantage when navigating dynamic environments, thanks to their superior sensing capabilities and advanced computing power. But why exactly do these features make such a difference? In this next series, we will dive deeper into the technology behind AMRs and uncover how their advanced perception and decision-making enable them to adapt, respond, and thrive in complex, ever-changing industrial settings. Stay tuned as we break down the reasons AMRs are redefining flexibility and efficiency in automation. Localization is the process of determining where a robot is located within its environment. For mobile robots, the ability to map its surroundings and identify its position relative to that map are key. With greater localization awareness, tasks can be performed faster and more efficiently, as the majority of a mobile robot’s tasks involve moving from one location to another. It is this freedom of movement that gives AMRs independence within a factory, but how does it work? Introduction to Inertial Measurement Units (IMUs) Inertial Measurement Units (IMUs) provide crucial motion data for precise robot positioning. Integrated accelerometers measure acceleration with respect to the earth’s gravitational field, gyroscopes measure the rate of rotation providing angular velocity, and magnetometers support accurate orientation estimation in challenging environments. By integrating all three of these advanced sensing technologies, IMUs enable robots to precisely determine their orientation, position, and movement. Let’s consider the challenges for localization and how IMUs overcome these. Dead Reckoning: A navigation technique to estimate current position based on a previously known position. By constantly providing data on position, orientation, and speed over elapsed time, IMUs enable precise estimation, contributing to reliable navigation for AMRs. Robustness: Environmental factors can have a significant impact on sensor performance. Lidar sensors, for instance, may exhibit sensitivity to ambient light, dust, fog, and rain, resulting in decreased sensor data quality and potential disruptions in performance. Other sensor modalities, such as cameras, may encounter challenges from reflective surfaces and dynamic obstacles like other AMRs or workers. In contrast, IMUs demonstrate robustness across diverse conditions, including environments with electromagnetic interference, enabling them to operate effectively both indoors and outdoors. This adaptability makes IMUs an optimal choice for mobile robots, ensuring consistent performance in the face of environmental complexities. Enhanced Reliability: IMUs stand out by providing high-fidelity positional output of up to 4kHz raw data. Other perception sensors are typically limited to update rates of ~10Hz to 30Hz. This increased update rate enhances reliability of IMU performance, especially in dynamic environments, enabling AMRs to estimate their position quickly and accurately in the short time between other measurements. IMUs Versus Visual Odometry You might be wondering, with the advancement in vision systems, why is the IMU still playing such a pivotal role in mobile robotics? Here’s why: SLAM (Simultaneous Localization And Mapping) algorithms match observed sensor data with stored data to localize within the map. But what happens when observed sensor data is limited, for example in a long corridor with straight walls of uniform color, texture, or reflectivity? SLAM algorithms can struggle to localize precisely in such environments, and the AMR is likely to lose its position quickly due to a lack of distinctive features. IMUs act as a valuable guidance system by providing heading and orientation information in feature-sparse environments such as corridors. They provide high short-term accuracy and immediate measurements between vision sensor measurements. IMUs have lower computational needs than visual odometry, enhancing redundancy and further endorsing them for AMR operations. IMUs: Part of a Holistic AMR Design While IMUs offer many benefits over other sensors, they can also be prone to drift. In situations where the environment is constantly changing, it may be advantageous for AMR operations to rely on multiple sensor inputs. This allows each sensor to overcome the limitations of the others for greater success. The next blog of the series will explore how this sensor fusion works and the combined benefit it brings to robotics. Factories of the future may be purpose built and optimized for AMRs to operate in, but adapting these robots to existing warehouses and factories presents challenges. Learn more about how ADI’s IMU technology can be utilized in mobile robotics at analog.com/mobile-robotics . ADIS16500AMLZ ANALOG DEVICES, MEMS Module, Accelerometer, Gyroscope | Farnell® Ireland Additional Resources: https://www.analog.com/en/products/adis16500.html https://www.analog.com/en/products/adis16550.html https://www.analog.com/en/product-category/inertial-measurement-units.htmlADI Mobile RoboticssensorsMotor and Controlroboticsmobile roboticsautonomous mobile robotIndustrial Automation TechnologyMotors & Motion Controlindustrial automationInertial Measurement Units (IMU)motor and motion controlIMUshttps://community.element14.com/products/manufacturers/analog-devices/f/forum/56541/urgent-support-is-required-for-eval-ad9984aebzTue, 30 Dec 2025 00:14:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:83f9c914-1fa8-4332-9d2f-832ec008d46cneerajrd82I m writing the issue observations in a clear and structured manner given below: 1. We have interconnected EVAL-AD9984AEBZ board with our test bench as given below: 2. We first switch "ON" RGsB video generator source. Then changed the settings on VESA complaint Sync on Green Analog signal with 1600x1200 pixels at 60 Hz. 3. We checked RGsB display, if this display is working correctly. We found that above shown RGsB Display was actually showing Sync on green signal. Now, we can actually change the resolution of Sync on green video signal. 4. We connected EVAL-AD9984AEBZ to CPU using USB cable provided -> this made USB PWR LED (D7) glow onboard & switch "ON" the EVAL-AD9984AEBZ board using AC to DC adapter provided with board -> this made main power LED (D6) glow on board. 5. Now, we loaded AVES Blue & selected the connection tab. Thereafter, double clicked on CY7C68013 which actually connect this board with Windows based PC. 6. We added "AD9984A-VER.1.0c.txt" script in AVES Blue which gives us the options given below: Out of these, we have selected "162MHz 60Hz UXGA(1600x1200)". Now, this settings will load default settings for AD9984A & ADV7511 as recommended by ADI. Now, HDMI Display connected with this evaluation board gets refresh just for second & then goes into sleep mode displaying nothing at all. Upon loading AD9984.xml file in AVESBlue, we get these answers on loading settings from AD9984 given below: PLL of AD9984 is getting locked can be seen in above screenshot. Please note: Given below procedure is not written in the documentation of EVAL-AD9984AEBZ but still we have managed to monitor the functioning of ADV7511. Upon loading ADV7511.xml file in AVESBlue, we get these answers on loading default settings from ADV7511 given below: PLL of ADV7511 is not getting locked can be seen in above screenshot. Since PLL of ADV7511 is not getting locked, there is no picture on HDMI output. these are default settings given by Analog Devices: 98 00 01 ; Chip revision 98 01 87 ; PLL Div 98 02 00 ; PLL Div 98 03 F0 ; VCO range, Charge pump current 98 04 E0 ; VCO range, Charge pump current 98 05 00 ; Red Gain 98 06 80 ; Red Gain 98 07 00 ; Green Gain 98 08 80 ; Green Gain 98 09 00 ; Blue gain 98 0A 80 ; Blue gain 98 0B 00 ; Red offset 98 0C 80 ; Red offset 98 0D 00 ; Green offset 98 0E 80 ; Green offset 98 0F 00 ; Blue offset 98 10 80 ; Blue offset 98 11 20 ; Sync separator 98 12 00 ; Hsync 98 13 20 ; Hsync duration 98 14 04 ; Vsync 98 15 0A ; Vsync duration 98 16 04 ; precoset 98 17 04 ; postcoast 98 18 00 ; clamping 98 19 08 ; clamp placement 98 1A 14 ; clamp duration 98 1B 33 ; clamp polarity 98 1C FF ; ADI recommended write 98 1D 78 ; sync on green 98 1E 34 ; Input channel select 98 1F 93 ; Output video mode 98 20 07 ; Output impedance 98 21 20 ; ADI recommended write 98 22 32 ; ADI recommended write 98 23 14 ; sync filter window width 98 24 A8 ; sync detect 98 25 FD ; sync polarity 98 26 4E ; Hsync per vsync 98 27 20 ; Hsync per vsync 98 28 8F ; ADI recommended write 98 29 02 ; ADI recommended write 98 2A 00 ; ADI recommended write 98 2B 00 ; ADI recommended write 98 2C 00 ; ADI recommended write 98 2D 08 ; ADI recommended write 98 2E 20 ; ADI recommended write 98 3C 0E ; Auto gain 72 01 00 ; Set 'N' value at 6144 72 02 18 ; Set 'N' value at 6144 72 03 00 ; Set 'N' value at 6144 72 15 00 ; 24-bit, 444 RGB input 72 16 50 ; RGB 72 18 08 ; Disable CSC 72 17 01 ; Enable DE Generation 72 35 7B ; DE Generation 72 36 F0 ; DE Generation 72 37 0C ; DE Generation 72 38 80 ; DE Generation 72 39 4B ; DE Generation 72 3A 00 ; DE Generation 72 3B 40 ; Set Pixel Repetition 72 3C 00 ; Set VIC 72 40 80 ; General control packet enable 72 41 10 ; Power down control 72 49 A8 ; Set dither mode - 12-to-10 bit 72 4C 05 ; Set 10 bit output 72 96 20 ; HPD interrupt clear 72 55 00 ; Set to RGB 72 56 08 ; Set active format aspect 72 98 03 ; ADI recommended write 72 99 02 ; ADI recommended write - lock count limit 72 9C 30 ; PLL filter R1 value 72 9D 61 ; Set clock divide 72 A2 A4 ; ADI recommended write 72 A3 A4 ; ADI recommended write 72 AF 16 ; Select HDMI mode 72 BA A0 ; Set TX Clock Delay 72 DE 9C ; ADI recommended write 72 E4 60 ; VCO_Swing_Reference_Voltage 72 FA 7D ; Nbr of times to look for good phase 72 48 00 ; Evenly distrubuted output 72 55 10 ; RGB out 72 56 00 ; Set AVI 72 57 00 ; Adobe RGB While debugging the reasons of misbehaviour of this board, we found that HSOUT,VSOUT & DATACK output from AD9984a was not compliant as per standard VESA timing for 1600x1200 pixel at 60 Hz sync on green with help of good quality 350MHz oscilloscope. These 3 signals were not stable, there was jitter in all 3 signals. So, we decided to stabilized the waveform by changing the settings of following registers: CHIP REGISTER SETTINGS AD9984A 0x12 0x48 AD9984A 0x13 0xC0 AD9984A 0x14 0x0C AD9984A 0x1D 0xB8 AD9984A 0x34 0xB4 ADV7511 0x41 0x00 These settings stabilized all 3 signals as per VESA timing standards for 1600x1200 pixel at 60Hz SoG. We were able to secure the PLL lock of ADV7511 as result of above settings as per screenshot given below: but still there was no picture output on HDMI Display shown above. Now, I sincerely think that all of whom are accessing this, should be able to understand the how we are able reproduce the failure of this board. if experts are able to understand this procedure ? Kindly! help on urgent basis as this is critical problem. Since i m not getting any support. THANKS WITH REGARDS NEERAJ GUPTAvideohdmianaloghttps://community.element14.com/products/manufacturers/analog-devices/b/blog/posts/exploring-automation-possibilities-with-the-adalm1k-learning-kitFri, 05 Dec 2025 00:45:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:b2ba3d68-8779-498b-8120-a4b4a8d84ae0ak95Summary: During my search for a low cost electronics learning module, I came accross the ADALM1K which has interesting features for the price point (approx. 70$). It incorporates a source measure unit (SMU), an oscilloscope and a function generator. On top of that the hardware and software is open-source which is a learning experience in itself to undestand how the kit works. My goal was first to test how the kit works overall. Once I had some confidence in its usage functions, I dived deeper into its evaluation / application software to look for automation oppurtinites with Python, where one could automate custom workflows for measurement and learning purposes. I was able to integrate the ADALM1K with my Raspberry Pi setup and automate its functionality using the provided libsmu/pysmu Python package from Analog. I ended up creating a small Python library ( pytest-analog ) around libsmu so I could write some automated tested for my projects usning the ADALM1K . As an example, I created automated test cases via Python to measure the power consumption of a DUT (ESP32 Dev board). This could be extended to create more complex test cases for your system under test using very low cost equipment such as the ADALM1K Hardware: The ADALM1K features two analog channels that support source and measure functionality for different waveforms in voltage or current mode. To offer this functionality, it uses a number of building blocks to take the fixed supply and digital interface of USB and offer voltage and current operation. The main hardware sepcs are as follows: Two channels supporting measure and source function simultaneously: voltage (0 to 5V) or current output (- 200 to +200mA) Two fixed power supplies (2.5V and 5V) supprting up to 200 mA current draw 16-bit (0.05%) basic measure accuracy with 4 digit resolution Four digital signals (GPIOs 3.3V CMOS) ADALM1K Block diagram per SMU Channel ( wiki.analog.com ) As per the above the diagram, an analog channel on the ADALM1K combines a function generator and an oscilliscope instrument on the same pin. In Rev F of the board, the analog ouput and input functions could be seperated with the two provided addiotnal split pins such that the oscillscope function is brought out along with 1 MΩ from the function generator function. Therefore, each analog channel could be configured to one of the following options: Source Voltage and Measure Current (with / wihtout Split IOs) Source Current and Measure Voltage (with / wihtout Split IOs) High Impedance (with / wihtout Split IOs) If you would like to learn more about the ADALM1K hardware and its design, the following resources are good to read through: ADALM1000 Design Document ADALM1000 HW Overview ADALM1000 Design & Integration Files ADALM1000 Board Upside View ADALM1000 Board Underside View Software: The software landscape of the ADALM1K comprises the device firmware and the host software which can run on different platforms (Winodws / Linx / OS-X). The ADALM1K firmware runs on an Atmel based microcontroller. The host software includes a C++ library ( libsmu ) containing the abstractions for streaming data to and from ADALM1K via USB. In addition, the Pixelpulse 2 and ALICE GUI-based tools are available to control the ADALM1K and make measurements with it. Testing with Analog Discovery: In order to test the opertion of the ADALM1K via the provided GUI software Pixelpulse 2 and ALICE , I did some basic checks to test the ADALM1K analog and digital inputs/outptus via my Analog Discovery 3 (another great electronics learning tool). In these tests, I connected the two instruments to my PC and started their host softwares simultaneously to feed inputs and read outputs. Test 1: Checking analog outputs of the ADALM1K via the Analog Discovery scope channels: As shown in the image below, I connected the ADALM1K source channels A, B to the Analog Discovery scope channels 1, 2. Then using ALICE software I configured an output voltage on channels A, B and then read measured voltages on the scope channels via the WaveForms software of Analog Discovery 3. I configured channels A, B to output 3.6, 1.2 V. On the Analog Discovery, I read approximately similar values on channels 1, 2. There is a slight 200 mV deviations which also seem to occur when I set the ADALM1K to ouput 0 V. Therfore, the ADALM1K requires some calibration before doing any real testing. Test 2: Checking GPIOs state of the ADALM1K via the Analog Discovery digital channels: In this test, I connected the ADALM1K GPIO pins (0-3) to the Analog Discovery Digital IOs (0-3). Using ALICE software, I have set the ADALM1K GPIOs to a defined high / low state and then checked if the same state is read on the Analog Discovery side using its Static IO instrument in the WaveForms software. I was able to confirm the GPIOs state (high for pins 0,1 and low for pins 2,3 ) Integration with Raspberry Pi: My next step was to connect the ADALM1K to my Raspberry Pi and get it working using the libsmu library provided by Analog. I wanted the setup to be headless so I could simply login remotely to the Pi and then run Python scripts to run different workflows with ADALM1K (i.e. something like a remote testing station). On the hardware side, I could simply connect the ADALM1K to my Raspberry Pi 5 via USB. On the software side, I had to compile and install libsmu from source as there was no ready-to-use package for Raspbian OS platform. You can refer to the this github repo section for building and installing libsmu on a Linux / Windows based platform. After installing libsmu, a utility executable named:"smu" should be available and you could trigger it to check if the ADALM1K board can be detected. For example: running smu -l command to list available devices as show below. In addition, I have installed pysmu which contains the Python bindings for libsmu to control ADALM1K via Python. To test the interaction via Python, I started an interactive Python session via Ipython and ran some commands to check if I could configure the ADALM1K and read data from it as shown below. You can refer to pysmu_examples to get more familiar with it. Python Wrapper and Test Automation: After integrating the ADALM1K with my Raspberry Pi setup and getting familiar with the libsmu / pysmu libraries, I decided to create a small python library ( wrapper class ) to ease the control of the instrument functions and also create pytest-fixtures for setting up / tear down of the instrument in an automated testing environment. The library is named pytest-analog and it also support the automation of the Analog Discovery instrument from Digilent. To demonstrate the usage of ADALM1K with pytest-analog in automated testing context, I created a small python test case where the ADALM1K would measure the power consumption of an ESP32 microcontroller running different sketches. Given the instrument source and measure capabilites, it can power the ESP32 with a given voltage and measure drawn current simultaneously. The steps to create an automated test with the ADALM1K via pytest-analog are listed below: Create an empty workspace folder (e.g pytest_analog_tmp) and navigate to it (In bash terminal): mkdir pytest_analog_tmp && cd pytest_analog_tmp Create a Python virtual environment to install pytest_analog and its dependencies in isolation: python3 -m venv venv . venv/bin/activate python3 -m pip install --extra-index-url https://test.pypi.org/simple/ pytest-analog python3 -m pip install matplotlib Create an empty "pytest.ini" file to provide the ADALM1K configuration at the start of the test with the content below. The first couple of lines are pytest specific to configure its output options and logging format. The last two lines are used by the fixtures in pytest-analog to configure channels A, B output voltage. Channel A is used to power the ESP32 at its 3.3V supply pin. [pytest] # pytest options addopts = -v --capture=tee-sys # Filtering Warnings filterwarnings = ignore::DeprecationWarning # Logging Options log_cli=true log_level=INFO log_format = %(asctime)s %(levelname)s %(message)s log_date_format = %Y-%m-%d %H:%M:%S # ADALM1000 Fixtures Options # Voltage Source adalm1k_ch_a_voltage = 3.30 adalm1k_ch_b_voltage = 0.00 Create a test_esp32_current_consumption.py file with the content below: import numpy as np import pytest import time import math import logging import matplotlib.pyplot as plt from pytest_analog import ADALM1KWrapper, AnalogChannel from datetime import datetime def test_esp32_current_consumption( adalm1k_voltage_source: ADALM1KWrapper ) -> None: # 2000 samples collected at base rate 100 kHz with averaging every 1000 samples -> 20 seconds MEASUREMENTS_COUNT = int(2000) # Do some averaging over collected data AVERAGE_RATE = int(1e3) # average every 1 / AVERAGE_RATE, Default sampling rate of ADALM1K is 100 kHz samples = [] ch_a_avg_voltage = [] ch_a_avg_current = [] # Read incoming samples in a blocking fashion (timeout = -1) for _ in range(MEASUREMENTS_COUNT): samples.append(adalm1k_voltage_source.read_all(AVERAGE_RATE, -1)) # Average captured readings for idx in range(MEASUREMENTS_COUNT): # voltages in V ch_a_voltage = [sample[0][0] for sample in samples[idx]] # currents in mA ch_a_current = [sample[0][1] * 1000 for sample in samples[idx]] ch_a_avg_voltage.append(np.mean(ch_a_voltage)) ch_a_avg_current.append(np.mean(ch_a_current)) logging.info(f"Average current consumption: channel A: {np.mean(ch_a_avg_current):.3f} mA") logging.info(f"Max current consumption: channel A: {np.max(ch_a_avg_current):.3f} mA") # plot current profile t = np.arange(0, MEASUREMENTS_COUNT) / (100e3 / AVERAGE_RATE) # Default sampling rate is 100 kHz fig, ax = plt.subplots() fig.suptitle(f"ESP32 Blinky current consumption", wrap=True) fig.supxlabel("Time (s)") ax.plot(t, ch_a_avg_current, color='red') ax.set(ylabel= "CH_A I(mA)") ax.margins() ax.set_xlim([0, math.ceil(np.max(t))]) ax.grid(True, which='both') ax.minorticks_on() plt.savefig(f"I_consumption_esp32_blinky_{time.strftime(datetime.now().strftime('%H%M'))}.png") Run the test using pytest: pytest test_esp32_current_consumption.py which generates the following logs in the terminal. Current consumption profile for ESP32 blinking an LED every 2 seconds Current consumption profile for ESP32 going into deep sleep mode for 5 seconds then waking up shortly After the test execution, a graph is generated with the measured current consumption profile during the test. The graphs above are for two different sketches running on the ESP32. The first one is a basic blinky sketch where one could certainly see an increase in current draw when the LED is on. The duration of those peaks match closely with the LED-On period as expected. The second sketch showcases the ESP32 deep sleep mode for extreme power saving applications, where the device is consuming a couple of miliamps during sleep mode and then waking up shortly every 5 seconds. IMPORTANT: The above results can be inaccurate and require verification with a professional equipment to compare how good the ADALM1K is in capturing power characterstics of a given DUT.pythonanalog devicessmuadalm1000analog discoveryhttps://community.element14.com/products/manufacturers/analog-devices/b/blog/posts/finding-the-right-fit-for-your-industrial-automation-need-_2d00_-agvs-or-amrsTue, 07 Oct 2025 15:34:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:d709db55-d2bc-4f9a-9fba-f97fe9842e12SarahCrowe28by Shane O'Meara This blog marks the second instalment in our ongoing series, delving into the fast-evolving landscape of industrial mobile robots. We initially examined how Industrial Mobile Robotics are revolutionizing the Factory of the Future Industrial Mobile Robotics - Revolutionizing the Factory of the Future Today - element14 Community In this edition, we take a closer look at two key technologies shaping the future of mobile automation: Automated Guided Vehicle (AGVs) and Autonomous Mobile Robot (AMRs). While both AGVs and AMRs are designed to enhance efficiency and flexibility in industrial environments, they differ significantly in terms of navigation, adaptability and use cases. By examining their core similarities and distinctions, we aim to help you determine which solution best aligns with your industrial automation needs. A mobile Automated Guided Vehicle (AGV) and an Autonomous Mobile Robot (AMR) share most of the same core fundamental attributes and will carry out similar automation tasks in an industrial application. The primary function for an AGV or an AMR is the transportation of materials from one location to another location in a factory or warehouse. The fundamental difference between an AGV and an AMR is in their navigation capability and ability to avoid obstacles. An AGV follows a predetermined track or route and will not deviate from this path as it travels from one location to another. If an AGV detects an obstacle blocking the path it will stop and remain in place until the obstacle is removed. An AMR also has the ability to detect an obstacle but has the intelligence to adapt its route to avoid the obstacle, calculate a new route and proceed to complete its goal without user intervention. An AGV can use different sensing techniques to detect its route. Magnetic tape stuck onto the factory floor. A corresponding sensor under the AGV detects the location of tape and adjusts its position. Additional pieces of magnetic tape can be used to code locations. Inductive wire embedded in the floor. Again, a sensor detects the wire and adjusts the AGV position accordingly. Visual tracking , colored tape or markers such as April Tags are placed on the ground and detected with RGB cameras to map route and determine location. Laser guided by a 360° laser mounted on the AGV and several reflectors installed in the facility. The AGV measures the distance and angle to the reflectors and triangulates its position. The main advantage of AGVs is that they follow their predetermined route precisely and consistently hence making them ideal for high volume, repetitive tasks. Their main disadvantages are that they require infrastructural changes within the facility they intend to operate in and significant effort to setup and maintain. Tape, wire, markers or reflectors must be installed on the floor or walls and then maintained. Visual markers can be impacted by dirt or magnetic tape may become dislodged. Inductive wire is extremely robust but any adjustments have a significant impact on day to day operation as the floor must be redone. Additionally, as mentioned previously, they lack the ability to avoid obstacles so they stop operating if an obstacle is detected. AMRs use Simultaneous Localization and Mapping (SLAM) to navigate the factory floor. Depth sensors, commonly Lidar scanners, mounted on the AMR are used to map the factory floor. The AMR is first driven around the facility and scans are accumulated to generate a complete map which is stored on the AMR and in fleet management control software. The generated map can be enhanced with additional information such as keep out zones, speed reduction areas and docking station locations. Goals can be placed on the map as x, y coordinates or dropped pins for the AMR to navigate between. During operation the most recent scan from the Lidar scanners is compared against the stored map and the current AMR position and orientation are calculated. The AMR then uses the built in navigation system to determine the optimized route to its goal considering the stored map and any obstacles it encounters on the path. Disadvantages of AMRs compared to AGVs is that they have a higher initial cost and are less predictable than an AGV which takes a defined time to reach its goal. The obvious advantage of AMRs is that there is no requirement for infrastructural changes to enable their operation as an AMR has the sensing capability and intelligence to localize and navigate autonomously without markers. New tasks and goals can be updated within the map or fleet management software quickly and efficiently. Facility expansions and automation upgrades can be easily accommodated with the generation of new maps or extension of existing maps. In the dynamic environments of the factory of the future, the ease of use, flexibility, scalability of an AMR gives it a clear advantage compared to AGVs. AMRs generally have enhanced sensing capabilities, with longer range Lidar, 3D depth sensing, radar and RGB vision technologies. These sensing modalities combined with superior compute power and artificial intelligence open up additional possibilities for advanced features and improved human robot interaction. To learn more on how ADI’s technology is enabling AMR and AGV robotic deployments, visit analog.com/mobile-robotics .ADI Mobile RoboticsroboticsAutonomous Guided RobotMotors and Motion Controlmobile roboticsIndustrial Automation Technologyindustrial automationhttps://community.element14.com/products/manufacturers/analog-devices/f/forum/11890/have-a-technical-question-for-maxim-ask-an-expert/230673Tue, 16 Sep 2025 09:48:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:99807992-6907-4c97-b6e2-a80e85da26basvanderwolfPlease contact our EngineerZone for assistance: https://ez.analog.com/.https://community.element14.com/products/manufacturers/analog-devices/f/forum/11890/have-a-technical-question-for-maxim-ask-an-expert/230672Tue, 16 Sep 2025 09:40:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:58f3de1a-6246-4220-813a-43717c0d45b6michaelkellettBob - there has been no activity in this thread in the last 15 years. Might be worth making enquiries direct to Analog Devices (who own Maxim). MKhttps://community.element14.com/products/manufacturers/analog-devices/f/forum/11890/have-a-technical-question-for-maxim-ask-an-expert/230661Mon, 15 Sep 2025 22:08:00 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:99621e99-c57b-499e-ac22-f1957cab20e1BobJapengaI am a retired embedded design engineer with 50 years of electronics experience. I am intterested in developing a product around the MAX86140 using the MAX-HEALTH-BAND. I have read the guide and the data sheet and watched the video - but there are still many more questions I have. I have not found the full cadre of documentation yet. I want to ascertain if the MAX86140 can do what I want it to do. Thanks in advance.bbb