With the increasing demand for human-computer interaction (HCI), flexible wearable gloves have emerged as a promising solution in virtual reality, medical rehabilitation, and industrial automation. However, the current technology still has problems like insufficient sensitivity and limited durability, which hinder its wide application. This paper presents a highly sensitive, modular, and flexible capacitive sensor based on line-shaped electrodes and liquid metal (EGaIn), integrated into a sensor module tailored to the human hand's anatomy. The proposed system independently captures bending information from each finger joint, while additional measurements between adjacent fingers enable the recording of subtle variations in inter-finger spacing. This design enables accurate gesture recognition and dynamic hand morphological reconstruction of complex movements using point clouds. Experimental results demonstrate that our classifier based on Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) achieves an accuracy of 99.15% across 30 gestures. Meanwhile, a transformer-based Deep Neural Network (DNN) accurately reconstructs dynamic hand shapes with an Average Distance (AD) of 2.076\pm3.231 mm, with the reconstruction accuracy at individual key points surpassing SOTA benchmarks by 9.7% to 64.9%. The proposed glove shows excellent accuracy, robustness and scalability in gesture recognition and hand reconstruction, making it a promising solution for next-generation HCI systems.

Artificial intelligence has advanced rapidly in the last decade, driven primarily by progress in the scale of deep-learning systems. Despite these advances, the creation of intelligent systems that can operate effectively in diverse, real-world environments remains a significant challenge. In this white paper, we outline the Thousand Brains Project, an ongoing research effort to develop an alternative, complementary form of AI, derived from the operating principles of the neocortex. We present an early version of a thousand-brains system, a sensorimotor agent that is uniquely suited to quickly learn a wide range of tasks and eventually implement any capabilities the human neocortex has. Core to its design is the use of a repeating computational unit, the learning module, modeled on the cortical columns found in mammalian brains. Each learning module operates as a semi-independent unit that can model entire objects, represents information through spatially structured reference frames, and both estimates and is able to effect movement in the world. Learning is a quick, associative process, similar to Hebbian learning in the brain, and leverages inductive biases around the spatial structure of the world to enable rapid and continual learning. Multiple learning modules can interact with one another both hierarchically and non-hierarchically via a "cortical messaging protocol" (CMP), creating more abstract representations and supporting multimodal integration. We outline the key principles motivating the design of thousand-brains systems and provide details about the implementation of Monty, our first instantiation of such a system. Code can be found at https://github.com/thousandbrainsproject/tbp.monty, along with more detailed documentation at https://thousandbrainsproject.readme.io/.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.

Advances in the field of environment perception for automated agents have resulted in an ongoing increase in generated sensor data. The available computational resources to process these data are bound to become insufficient for real-time applications. Reducing the amount of data to be processed by identifying the most relevant data based on the agents' situation, often referred to as situation-awareness, has gained increasing research interest, and the importance of complementary approaches is expected to increase further in the near future. In this work, we extend the applicability range of our recently introduced concept for situation-aware environment perception to the decentralized automation architecture of the UNICARagil project. Considering the specific driving capabilities of the vehicle and using real-world data on target hardware in a post-processing manner, we provide an estimate for the daily reduction in power consumption that accumulates to 36.2%. While achieving these promising results, we additionally show the need to consider scalability in data processing in the design of software modules as well as in the design of functional systems if the benefits of situation-awareness shall be leveraged optimally.

Eve Systems has been adding more and more Thread-enabled smart gadgets to its portfolio, including the Eve Aqua sprinkler controller (which we've previously reviewed) and the Eve Energy smart plug (ditto). Now comes Eve Door & Window, a HomeKit- and Thread-capable contact sensor, and it's as easy to set up and use as Eve's Aqua and Energy products. With able assistance from the Eve app, Eve Door & Window supports powerful automations and lets you take a deep dive into when, and how often, your doors and windows have been opened and closed. At $40, however, Eve Door & Window is mighty expensive for a contact sensor, and while it does support HomeKit, it doesn't work with Alexa or Google Assistant, which means only Apple users need apply. You can also configure it to trigger lighting scenes when you arrive home, or to turn down the thermostat when someone opens the window, so it's geared more toward home automation than home security; for the latter, you're on your own in terms of integrating the sensor with a third-party security system.

We've come a long way since the first 3D-printed item came to us by way of an eye wash cup, to now being able to rapidly fabricate things like car parts, musical instruments, and even biological tissues and organoids. While much of these objects can be freely designed and quickly made, the addition of electronics to embed things like sensors, chips, and tags usually requires that you design both separately, making it difficult to create items where the added functions are easily integrated with the form. Now, a 3D design environment from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) lets users iterate an object's shape and electronic function in one cohesive space, to add existing sensors to early-stage prototypes. The team tested the system, called MorphSensor, by modeling an N95 mask with a humidity sensor, a temperature-sensing ring, and glasses that monitor light absorption to protect eye health. MorphSensor automatically converts electronic designs into 3D models, and then lets users iterate on the geometry and manipulate active sensing parts.

HALCON, the standard machine vision software from MVTec Software GmbH (www.mvtec.com), is well prepared for new developments in the embedded vision environment. The leading provider of modern machine vision software offers proof that its software can acquire and process images from MIPI (Mobile Industry Processor Interface) camera modules. Existing HALCON interfaces, such as Video4Linux, GenTL, as well as shared memory access, can be used for this. The hardware interface experts at MVTec have successfully tested the VC MIPI OV 9281 camera module from Vision Components in conjunction with Raspberry Pi 3 and 4, using HALCON 19.05. They also utilized the same HALCON version to successfully test the MIPI IMX290 sensor module from The Imaging Source on an NVIDIA Jetson Nano via FPD-Link III (cable lengths of up to 15 meters).