CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data.

Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.

Alpha-band activity in parietofrontal cortex predicts future availability of vibrotactile feedback in prosthesis use.

Prosthesis disuse and abandonment is an ongoing issue in upper-limb amputation. In addition to lost structural and motor function, amputation also results in decreased task-specific sensory information. One proposed remedy is augmenting somatosensory information using vibrotactile feedback to provide tactile feedback of grasping objects. While the role of frontal and parietal areas in motor tasks is well established, the neural and kinematic effects of this augmented vibrotactile feedback remain in question. In this study, we sought to understand the neurobehavioral effects of providing augmented feedback during a reach-grasp-transport task. Ten persons with sound limbs performed a motor task while wearing a prosthesis simulator with and without vibrotactile feedback. We hypothesized that providing vibrotactile feedback during prosthesis use would increase activity in frontal and parietal areas and improve grasp-related behavior. Results show that anticipation of upcoming vibrotactile feedback may be encoded in motor and parietal areas during the reach-to-grasp phase of the task. While grasp aperture is unaffected by vibrotactile feedback, the availability of vibrotactile feedback does lead to a reduction in velocity during object transport. These results help shed light on how engineered feedback is utilized by prostheses users and provide methodologies for further assessment in advanced prosthetics research.

Prediction of vascular invasion using a 7-point scale computed tomography grading system in adrenal tumors in dogs.

BACKGROUND: Previous studies evaluating the accuracy of computed tomography (CT) in detecting caudal vena cava (CVC) invasion by adrenal tumors (AT) used a binary system and did not evaluate for other vessels. OBJECTIVE: Test a 7-point scale CT grading system for accuracy in predicting vascular invasion and for repeatability among radiologists. Build a decision tree based on CT criteria to predict tumor type. METHODS: Retrospective observational cross-sectional case study. Abdominal CT studies were analyzed by 3 radiologists using a 7-point CT grading scale for vascular invasion and by 1 radiologist for CT features of AT. ANIMALS: Dogs with AT that underwent adrenalectomy and had pre- and postcontrast CT. RESULTS: Ninety-one dogs; 45 adrenocortical carcinomas (50%), 36 pheochromocytomas (40%), 9 adrenocortical adenomas (10%) and 1 unknown tumor. Carcinoma and pheochromocytoma differed in pre- and postcontrast attenuation, contralateral adrenal size, tumor thrombus short- and long-axis, and tumor and thrombus mineralization. A decision tree was built based on these differences. Adenoma and malignant tumors differed in contour irregularity. Probability of vascular invasion was dependent on CT grading scale, and a large equivocal zone existed between 3 and 6 scores, lowering CT accuracy to detect vascular invasion. Radiologists' agreement for detecting abnormalities (evaluated by chance-corrected weighted kappa statistics) was excellent for CVC and good to moderate for other vessels. The quality of postcontrast CT study had a negative impact on radiologists' performance and agreement. CONCLUSIONS AND CLINICAL IMPORTANCE: Features of CT may help radiologists predict AT type and provide probabilistic information on vascular invasion.

Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview.

Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends.

Frequency based design of modal controllers for adaptive optics systems.

This paper addresses the problem of reducing the effects of wavefront distortions in ground-based telescopes within a "Modal-Control" framework. The proposed approach allows the designer to optimize the Youla parameter of a given modal controller with respect to a relevant adaptive optics performance criterion defined on a "sampled" frequency domain. This feature makes it possible to use turbulence/vibration profiles of arbitrary complexity (even empirical power spectral densities from data), while keeping the controller order at a moderate value. Effectiveness of the proposed solution is also illustrated through an adaptive optics numerical simulator.

Glass transition associated with the jamming of vibrated granular matter.

We investigate a vibrated granular system composed of millimeter-size glass beads. When the system is submitted to a perturbation with decreasing intensity, below the fluidization limit, it evolves in a way similar to glass-forming liquids until it reaches an amorphous jammed state. This jamming transition is observed by the means of an immersed oscillator, either in the free or forced mode, while the granular medium is submitted to an external perturbation at a fixed frequency or within a frequency band. The complex susceptibility of the oscillator is measured as a function of the probe forcing frequency or as a function of the perturbation intensity. Data show that the jamming dynamics is "activated," similarly to thermal systems. The empirical control parameter is found proportional to the square root of the vibration intensity and inversely proportional to the vibration frequency. In the case of broadband external vibration, the average frequency of the power spectrum has to be considered.