--We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples. In this paper, we first create a new dataset with over one hour of audio data that enables our research and will be useful to future studies in this field. Second, we utilize the technologies provided by Edge Impulse to enhance our model's performance and achieve a high Accuracy of up to 97% on our dataset. For the validation, we implement our prototype using the Arduino Nano 33 BLE Sense microcontroller board. This microcontroller board is specifically designed for IoT and AI applications, making it an ideal choice for our target use case scenarios. While most existing research focuses on a limited set of keywords, our model can process 23 different keywords, enabling complex commands. Natural Language Processing (NLP) and Speech Recognition are crucial domains in Artificial Intelligence (AI). While NLP deals with enabling computers to analyze, understand, reason on, and generate human language in textual form, speech recognition is concerned with that in spoken form.

Mobile smartphones compactly provide sensors such as cameras, IMUs, GNSS measurement units, and wireless and wired communication channels required for robotics projects. They are affordable, portable, and programmable, which makes them ideal for testing, data acquisition, controlling mobile robots, and many other robotic applications. A robotic system is proposed in this paper, consisting of an Android phone, a microcontroller board attached to the phone via USB, and a remote wireless controller station. In the data acquisition mode, the Android device can record a dataset of a diverse configuration of multiple cameras, IMUs, GNSS units, and external USB ADC channels in the rawest format used for, but not limited to, pose estimation and scene reconstruction applications. In robot control mode, the Android phone, a microcontroller board, and other peripherals constitute the mobile or stationary robotic system. This system is controlled using a remote server connected over Wi-Fi or Bluetooth. Experiments show that although the SLAM and AR applications can utilize the acquired data, the proposed system can pave the way for more advanced algorithms for processing these noisy and sporadic measurements. Moreover, the characteristics of the communication media are studied, and two example robotic projects, which involve controlling a toy car and a quadcopter, are included.

Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas. The local indigenous knowledge on drought gathered from the domain experts is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases.

One of a new generation of microcontroller boards with hardware optimised for embedded AI at the'Edge'. No new electronic gadget is considered to be'high-tech' nowadays unless it features some form of alleged'artificial intelligence' or AI. The really sophisticated applications require staggering amounts of computer power – can anything be done with a humble microcontroller? I'll start with some basics first. One major problem encountered when trying to explain or discuss artificial intelligence is that everyone, whether layman or scientist/engineer, has a firm opinion on the subject.

To celebrate another year in the books, here are our top 10 best selling Adafruit products from 2019! The Adafruit Trinket M0 may be small, but do not be fooled by its size! It's a tiny microcontroller board, built around the Atmel ATSAMD21, a little chip with a lot of power. We wanted to design a microcontroller board that was small enough to fit into any project, and low cost enough to use without hesitation. Perfect for when you don't want to give up your expensive dev-board and you aren't willing to take apart the project you worked so hard to design.