Designing ground-aerial robots is challenging due to the increased actuation requirements which can lead to added weight and reduced locomotion efficiency. Morphobots mitigate this by combining actuators into multi-functional groups and leveraging ground transformation to achieve different locomotion modes. However, transforming on the ground requires dealing with the complexity of ground-vehicle interactions during morphing, limiting applicability on rough terrain. Mid-air transformation offers a solution to this issue but demands operating near or beyond actuator limits while managing complex aerodynamic forces. We address this problem by introducing the Aerially Transforming Morphobot (ATMO), a robot which transforms near the ground achieving smooth transition between aerial and ground modes. To achieve this, we leverage the near ground aerodynamics, uncovered by experimental load cell testing, and stabilize the system using a model-predictive controller that adapts to ground proximity and body shape. The system is validated through numerous experimental demonstrations. We find that ATMO can land smoothly at body postures past its actuator saturation limits by virtue of the uncovered ground-effect.

The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WGs) of India. CloudSense uses vertical reflectivity profiles collected during July-August 2018 from an X-band radar to classify clouds into four categories namely stratiform,mixed stratiform-convective,convective and shallow clouds. The machine learning(ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC of 0.8 and F1-Score of 0.82. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC and F1-Score of 0.77. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.

The system creates a clear and immersive experience for music as well as film and TV content. Samsung's HW-Q950A is a fully equipped solution, offering 22 drivers, multiple speakers, and 11.1.4-channel The system slots upfiring and side-firing drivers not only in the bar, but also in the wireless surround speakers, punching out enough Dolby Atmos expansion to make you question the need for discreet speaker surround sound systems at all. As noted in our Q950A review, no soundbar can deliver the full dynamics, resonance, and presence of a true multi-speaker home theater system setup. But the Q950A comes about as close as we've heard, at a more approachable price. Its reams of drivers combine for a thrilling Dolby Atmos (and DTS:X) experience that transforms your room into a "dome" of sound. The system also offers impressive musicality for such a thin device (Samsung acquired AKG for a reason).

One of the earliest images in Natalia Almada's virtuoso documentary Users is of an infant, tightly wrapped and strapped to a Snoo smart crib, robotically being rocked to sleep to the sound of manufactured white noise. By recreating many of the sensations of being in the womb, the Snoo has become a popular gadget for new parents who need help tucking their little ones in. In many ways, it's the pinnacle of a smart gadget: Developed by Dr. Harvey Karp, with product design by the renowned Yves Behar, the Snoo solves a problem that parents have faced for millennia. But what do we lose if a robot can automatically soothe a crying baby, effectively replacing a nurturing parent. That's the question at the heart of Users, which premiered at the Sundance Film Festival this week.

The new ThinkPads are here, and they're packed with exciting new features. Fresh for the all-virtual CES 2021 exhibit, Lenovo has dropped four new models: the 9th gen X1 Carbon, the 6th gen X1 Yoga, and the brand new X1 Titanium Yoga and X1 Detachable. We're especially curious about the X1 Detachable, which is basically Lenovo's version of the Microsoft Surface Book 3--a laptop with a completely detachable screen that turns into a tablet. Unlike a tablet with a keyboard folio, the Surface Book 3 feels like a classic clamshell laptop when the display and keyboard are connected, and we expect the X1 Detachable will have a similar feel. The ThinkPad X12 Detachable could be a long-due rival for the Microsoft Surface Book 3. This is one of the most interesting releases of CES.

When Amazon demonstrated the new Echo Studio at a company event in September, I was blown away. This was some of the best audio I had ever heard from a little speaker, filling a room of hundreds of journalists and sounding like it was many speakers connected together. My initial thought was that rival Sonos, which specializes in great-sounding Wi-Fi speakers, was in trouble. After spending several hours with the $200 Studio, out Thursday, there will be no need to schedule a benefit for Sonos. The Studio does sounds great.

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.