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Category: drones

Autonomous microdrone achieves first airtoair insect kill on the way ‘towards completely eradicating mosquitoes’ uses car parking sensors, can eliminate insects at up to 26 feet

A micro-drone designed to locate and eradicate mosquitoes has passed an important milestone. Tornyol Systems shared a video where the eponymous autonomous drone chalked up its first live air-to-air kill. For some reason (perhaps demonstration visibility), the 40g (1.4 ounce) drone’s first confirmed kill on video features a moth. Tornyol boldly claims that the demo shows a significant stride has been made “towards completely eradicating mosquitoes.”

Tornyol Systems co-founder Alex Toussaint shared the above Tweet, congratulating the engineering team that has worked alongside him on the project. On the company website, there is a mosquito-hostile manifesto laid out, which provides insight into the company’s primary drone development goal.

Human-machine learning boosts noninvasive brain-computer control in untrained users

Implantable devices in the brain have been used for about 30 years to assist people with disabilities in completing motor tasks. However, the devices are simply not accessible to the vast majority of people who need help. Despite decades of work in this field, fewer than 100 people worldwide have benefited from the technology. The costs are prohibitive, and the brain surgeries are inherently risky.

That’s why Carnegie Mellon researchers, including Bin He, professor of biomedical engineering, electrical and computer engineering, and the Neuroscience Institute, have long been working on noninvasive brain-computer interfaces (BCIs) to develop technology that is less expensive, safer and more accessible to a wider population. Over the past 10 to 15 years, they have used noninvasive BCIs to fly a drone, control a robotic arm, maintain continuous control of a robotic arm and, most recently, complete fine motor tasks at the finger level. Yet the accuracy and level of control using noninvasive technology remain challenging.

Engineers develop AI system to speed satellite tracking of wildfires

A new artificial intelligence system developed by West Virginia University engineers could help firefighters respond to wildfires sooner by enabling satellites to detect blazes and automatically adjust their positions for continued monitoring.

Unlike drones and ground-based sensors, satellites can monitor vast areas of the planet without requiring local infrastructure or routine maintenance. WVU researchers Brycen Pearl, Joshua Warner and Hang Woon Lee developed a framework that allows satellites not only to detect wildfires but also to coordinate with one another and adjust their observation schedules as fires spread.

“Wildfires move quickly—as fast as 15–20 mph (24–32 km/h) under the right conditions—and major wildfires can cover hundreds of thousands of acres,” according to Lee, director of the WVU Space Systems Operations Research Laboratory and assistant professor at the WVU Benjamin M. Statler College of Engineering and Mineral Resources.

Frontiers: The relentless advancement of artificial intelligence (AI) across sectors such as healthcare

The automotive industry, and social media necessitates the development of more efficient hardware solutions that can implement diverse learning algorithms. This lead article explores the evolution of AI learning algorithms and their computational demands, using autonomous drone navigation as a case study to highlight the limitations of traditional hardware. Traditional hardware, based on the von Neumann architecture, suffers from limited computational efficiency due to the separation of compute units and memory, also known as the “memory wall” problem. To overcome this barrier, this article discusses novel approaches to AI hardware design, focusing on compute-in-memory (CIM) techniques and stochastic hardware.

Small transistor sharpens low-cost thermal cameras without extreme cooling

With help from a small transistor, a team of researchers led by Professor Fengnian Xia figured out a way to make a type of thermal imaging technology dramatically more accurate. The results are published in Nature Sensors.

Robots, drones, self-driving vehicles and other autonomous devices rely on thermal sensing and imaging to navigate the spaces they travel in. It’s also used in many other technologies, including night vision, remote thermometers and rescue operations.

Giving drones a sense of ‘pain’ could help them predict instability before it happens

Imagine you’re running and you sprain your ankle. The pain makes you gingerly limp the rest of the way home. This is a great example of how nature adapts to failures in a system. The pain tells you: “If you continue running like normal, the injury will only get worse.” So you naturally adjust the way you run. Drones currently cannot do this with a worn-out propeller.

Researchers from Delft University of Technology and Wageningen University & Research have now demonstrated that a concept we learned from nature, which was originally developed to predict collapse in ecosystems, can also help detect when engineered systems are heading toward failure. This is crucial for ensuring drone and autonomous vehicle safety as they increasingly become part of everyday life.

“You can compare our approach to the way humans experience pain. After an injury, pain provides immediate feedback about our condition and helps us judge what actions remain safe,” says Jasper van Beers, a researcher at Delft University of Technology. “Machines generally lack this form of self-awareness. The new indicators, derived from real-time measurement data, offer a first step toward giving engineered systems a similar ability to recognize when they are approaching their limits.”

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3,970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

Keywords: SpiNNaker; artificial neural network; natural language processing; neuromorphic computing; sentiment analysis; spiking neural networks.

PubMed Disclaimer

AI isn’t a dual-use technology, it is inherently violent

When the Pentagon branded Anthropic CEO Dario Amodei “a liar with a god complex” over fears that his company’s AI could be used for weapons and surveillance, it exposed a deeper truth: the boundary between civilian and military technology no longer exists. The same systems that power translation, logistics, and digital assistants can just as easily identify targets or manipulate populations. Thomas Christian Bächle and Jascha Bareis argue that today’s AI is not simply “dual use” — it is inherently violent in design. Adaptive, autonomous, and globally networked, these machines fuse daily life with geopolitics, making peace itself a fading abstraction.

Drones have become an uncanny threat—not least in the wake of the cost of human life and the degrees of suffering and destruction they have inflicted in Russia’s war on Ukraine. In many European countries they have been sighted near critical infrastructure or military sites, either used for reconnaissance or sabotage, at times causing major disruptions in civilian air travel. Drones unsettle a population that is fearful and weary of the brutality of war at their doorstep. They have become a major element to what is labelled hybrid warfare, fought beyond the conventional ways of violence.

But this is not the whole picture. For years, drones have also been envisioned as a technology that bears the potential of bringing about major changes for the better: more efficient disaster relief, medical supply chains reaching even the remotest areas, optimized logistics or transportation. Drones also introduced a new visual – bird’s-eye-aesthetic of how to see the world.

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