An Experimental Study of NOMA for Connected Autonomous Vehicles

Connected autonomous vehicles (CAV) constitute an important application of future-oriented traffic management .A vehicular system dominated by fully autonomous vehicles requires a robust and efficient vehicle-to-everything (V2X) infrastructure that will provide sturdy connection of vehicles in both short and long distances for a large number of devices, requiring high spectral efficiency (SE). Power domain non-orthogonal multiple access (PD-NOMA) technique has the potential to provide the required high SE levels. In this paper, a vehicular PD-NOMA testbed is implemented using software defined radio (SDR) nodes. The main concerns and their corresponding solutions arising from the implementation are highlighted. The bit error rates(BER) of vehicles with different channel conditions are measured for mobile and stationary cases. The extent of the estimation errors on the success rate beyond the idealized theoretical analysis view is investigated and the approaches to alleviate these errors are discussed. Finally, our perspective on possible PD-NOMA based CAV deployment scenarios is presented in terms of performance constraints and expectancy along with the overlooked open issues.Comment: 7 Pages, 6 figure

Françoise Sullivan : La peinture à venir

Richly illustrated with images of Sullivan’s works and of her studio, this carefully designed catalogue offers a survey of the artist’s pictorial practice in reference to space, to the act of painting, to performance (movement) and to colour with special attention to the artist’s distinctive approach. In a text written in the form of a correspondence, Régimbald-Zeiber links the work of Sullivan to the Russian avant-garde and Constructivism. Biographical notes on artist, authors and collaborators. Texts in French and English. 3 bibl. ref

Valores gráficos del verso libre en el Grupo del 27(I)

Partiendo del concepto de métricas interrelacionadas con otros niveles de estudio en la producción literaria, especialmente el elemento gráfico como un factor rítmico, este ensayo intenta sistematizar los contenidos más frecuentes que producen un efecto gráfico (como el alargamiento o acortamiento de la línea) y en qué medida este dispositivo es productivo. El cuerpo estudiado está formado por poemas de verso libre escritos por los poetas más representativos de la llamada "Generación del 27", ya que la calidad de línea libre es menos restrictivo y la "vanguardia" representa un estilo peculiar en el que la tipografía adquiere una importancia relevante en la literatura.Starting out from the concept of metrics interrelated with other levels of study in the literary production -specially the graphic element as a rhymthical factor, this essay tries to systematize the most frequent contents that produce a graphic effect (such as the lengthening or shortening of the line) and to which extent this device is productive. The body studied is formed by free-lined poems written by the most representative poets of the so called «Generation of the 27», since the quality of free line is less restric- tive and the «vanguardia» represent a peculiar style in which typography acquires a relevant importance in literature

Graph-Based Object Classification for Neuromorphic Vision Sensing

Neuromorphic vision sensing (NVS) devices represent visual information as sequences of asynchronous discrete events (a.k.a., "spikes'") in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, object classification with NVS streams cannot leverage on state-of-the-art convolutional neural networks (CNNs), since NVS does not produce frame representations. To circumvent this mismatch between sensing and processing with CNNs, we propose a compact graph representation for NVS. We couple this with novel residual graph CNN architectures and show that, when trained on spatio-temporal NVS data for object classification, such residual graph CNNs preserve the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we present and make available a 100k dataset of NVS recordings of the American sign language letters, acquired with an iniLabs DAVIS240c device under real-world conditions

General In-Hand Object Rotation with Vision and Touch

We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.Comment: CoRL 2023; Website: https://haozhi.io/rotateit

Tapered whisker reservoir computing for real-time terrain identification-based navigation

This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain

Deep learning computer vision for robotic disassembly and servicing applications

Fastener detection is a necessary step for computer vision (CV) based robotic disassembly and servicing applications. Deep learning (DL) provides a robust approach for creating CV models capable of generalizing to diverse visual environments. Such DL CV systems rely on tuning input resolution and mini-batch size parameters to fit the needs of the detection application. This paper provides a method for determining the optimal compromise between input resolution and mini-batch size to determine the highest performance for cross-recessed screw (CRS) detection while utilizing maximum graphics processing unit resources. The Tiny-You Only Look Once v2 (Tiny-YOLO v2) DL object detection system was chosen to evaluate this method. Tiny-YOLO v2 was employed to solve the specialized task of detecting CRS which are highly common in electronic devices. The method used in this paper for CRS detection is meant to lay the ground-work for multi-class fastener detection, as the method is not dependent on the type or number of object classes. An original dataset of 900 images of 12.3 MPx resolution was manually collected and annotated for training. Three additional distinct datasets of 90 images each were manually collected and annotated for testing. It was found an input resolution of 1664 x 1664 pixels paired with a mini-batch size of 16 yielded the highest average precision (AP) among the seven models tested for all three testing datasets. This model scored an AP of 92.60% on the first testing dataset, 99.20% on the second testing dataset, and 98.39% on the third testing dataset

Looking Back and Looking Forward: Reprising the Promise and Predicting the Future of Formation Flying and Spaceborne GPS Navigation Systems

A retrospective consideration of two 15-year old Guidance, Navigation and Control (GN&C) technology 'vision' predictions will be the focus of this paper. A look back analysis and critique of these late 1990s technology roadmaps out-lining the future vision, for two then nascent, but rapidly emerging, GN&C technologies will be performed. Specifically, these two GN&C technologies were: 1) multi-spacecraft formation flying and 2) the spaceborne use and exploitation of global positioning system (GPS) signals to enable formation flying. This paper reprises the promise of formation flying and spaceborne GPS as depicted in the cited 1999 and 1998 papers. It will discuss what happened to cause that promise to be mostly unfulfilled and the reasons why the envisioned formation flying dream has yet to become a reality. The recent technology trends over the past few years will then be identified and a renewed government interest in spacecraft formation flying/cluster flight will be highlighted. The authors will conclude with a reality-tempered perspective, 15 years after the initial technology roadmaps were published, predicting a promising future of spacecraft formation flying technology development over the next decade