The blog post introduces an innovative concept for a computer vision system that automatically identifies climbing routes from a photo of a climbing wall. It would analyze features such as color-coded routes and different hold types (e.g., jug, crimp, sloper). Additionally, using geometry and physics, the system would create a personalized climbing plan (beta) for the climber, tailored to their skill level and technique. The post explores the potential for smarter climbing experiences through AI and technology.

This blog post provides an overview of key concepts in algorithms, starting with Dynamic Programming and Greedy Algorithms, exploring techniques like Divide and Conquer, Fourier Transform, and foundational theory on P and NP, NP-Completeness, and NP-Hard problems. It delves into Linear Programming and Integer Linear Programming, focusing on Approximation Algorithms and their applications, including TSP (Traveling Salesman Problem) approximation schemes. The post also covers advanced topics such as RSA Public Key Cryptography and the Basics of Quantum Computing, along with an introduction to advanced data structures, offering a comprehensive guide to important algorithmic techniques and their real-world applications.

Language modeling in NLP is essential for applications like autocomplete and translation, focusing on text fluency by predicting the next word based on context. It utilizes models such as unigram, bigram, and n-gram, with effective modeling requiring efficient context management through fixed windows. Key challenges include representing history for accurate predictions, while neural language models and techniques like LSTM enhance performance by managing memory and context in sequences.

Addresses the challenges of structured vs. unstructured data, and introduces the Bag of Words model for feature representation. Explain why each document can be represented as a point in a d-dimensional feature space.

Discusses neural networks, their structure, including input, hidden, and output layers, and the process of weight adjustment using gradient descent. Additionally, this post covers deep learning as a subset of machine learning that utilizes multi-layer neural networks for complex tasks, with a focus on PyTorch for implementation and includes a code example for a simple neural network model.

Explores the Bayes' theorem, which calculates the posterior probability of a hypothesis given evidence. Discusses how the generalized Bayesian model simplifies calculations under the Naive Bayes assumption of independent effects.

Reviews key concepts in probability, including random variables (discrete and continuous), probability distributions (PMF and PDF), expectation and variance, joint distributions, conditional expectation, and complex queries involving random variables.

In projects involving the ESP32, internal flash storage may be insufficient, necessitating the use of a Micro SD card. However, many people encounter SD card instability even when following a guide. This post provides steps to troubleshoot SD card failures, including following instructions, trying different SD cards, checking voltage requirements, measuring voltage with a multimeter, and optimizing power supply connection.

The ESP32 microcontroller has an internal Real-Time Clock (RTC) with limitations in maintaining time during power loss. To overcome this, an external RTC module, such as DS1307, DS3231, can be integrated with the ESP32. This requires wiring the module to the ESP32, using I2C or other interfaces, and optionally connecting a backup battery. Example code is provided for setting up the external RTC and synchronizing with NTP servers for accurate timekeeping in IoT applications.

Today, I published my first YouTube video explaining the system design for my ESP32 data logger project and demoing the React front end for the ESP32. While the video was generally well done, I identified several areas for improvement. Qiwei Mao, Atlanta GA.

By organizing files into subdirectories, using binary formats, maintaining in-memory metadata, and optimizing file structures for both writing and reading, you can significantly improve the I/O performance of your ESP32 data logger.

This code is for an ESP32-based data logging system, utilizing various libraries and APIs for handling tasks such as data logging, wireless communication, and configuration management.

Explain how the master node performs scheduled tasks such as polling data, time synchronization, and configuration verification. Additionally, it handles web API commands triggered by a flag. The main control loop checks the current time and executes tasks at specified intervals, ensuring efficient coordination and communication within the network. The implementation includes a flow chart for clarity and detailed steps for setting up time synchronization and task scheduling.

In this post, we explore a rough protocol for transferring large files over LoRa using ESP32 devices. The protocol involves sending metadata, file chunks, and an end-of-transfer message, with acknowledgment handling to ensure reliable transmission.

Explore how to send and receive structured messages using LoRa with ESP32 devices. Instead of sending simple strings, we use structs to include a MAC address for identification, extraction instructions, and a payload. By using an enum to declare message types and identifying the message type with the first byte (msgType), we eliminate the need to include message length in the transmission.

When developing with the ESP32 or Arduino, using the LoRa.onReceive function requires careful handling due to its operation within an interrupt context. Interrupt Service Routines (ISRs) in the ESP32 have stringent requirements and limitations. Performing complex or time-consuming tasks within an ISR can lead to various issues, including crashes and reboots.

Learn the difference between LoRa and LoRaWAN and how to set up a cost-effective LoRa network with ESP32 devices and transceivers. This guide covers the limitations of a no-gateway setup and provides solutions for timing coordination, avoiding conflicts, and assigning unique identifiers. Includes practical examples and code snippets using the Arduino LoRa library to help you implement your own LoRa network.