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The future of lidar is uncertain unless, as Voyant hopes to do, its price and size are reduced to fractions of their current values. As long as lidars are sandwich-sized devices that cost thousands, they won’t be ubiquitous — so Voyant has raised some cash to bring its smaller, cheaper, more easily manufactured, yet still highly capable lidar to production.

When I wrote up the company’s seed round back in 2019, the goal was more or less to shrink lidar down from sandwich to fingernail size using silicon photonics. But the real challenge faced by nearly every lidar company is getting the price down. Between a strong laser, capable receptor and a mechanical or optical means of directing the beam, it just isn’t easy making something cheap enough that, like an LED or touchscreen, you can easily put several of them in a vehicle that costs less than $30,000.

CEO Peter Stern joined the company just as COVID was getting started, and they were looking for a way to turn a promising prototype developed by co-founders Chris Phare and Steven Miller into a working and marketable product. After going back to basics they ended up with a photonics-based frequency-modulated continuous wave (FMCW) system (just go with it for now) that could be manufactured at existing commercial fabs.

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A team of engineers from the University of California San Diego has unveiled a prototype four-legged soft robot that doesn’t need any electronics to work. The robot only needs a constant source of pressurized air for all its functions, including its controls and locomotion systems.

Most soft robots are powered by pressurized air and are controlled by electronic circuits. This approach works, but it requires complex components, like valves and pumps driven by actuators, which do not always fit inside the robot’s body.

In contrast, this new prototype is controlled by a lightweight, low-cost system of pneumatic circuits, consisting of flexible tubes and soft valves, onboard the robot itself. The robot can walk on command or in response to signals it detects from the environment.

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Having launched 31 orbital Falcon 9 missions and four suborbital Starship tests, 2021 was the most active year for SpaceX to date. These launches included a number of new reuse records, including flying a booster for the eleventh time, flying the same booster twice in under a month, flying a fairing half for the fifth time, and setting a turnaround record for Dragon.

Falcon 9 Boosters

2021 brought only two new Falcon 9s into the fleet: B1067 and B1069, which first flew on the CRS-22 and CRS-24 missions, respectively. All of the other 29 Falcon 9 missions were flown on flight-proven boosters. These flights included the first eighth, ninth, tenth, and eleventh flight of a first stage, meeting and surpassing CEO Elon Musk’s stated goal to fly a Falcon 9 first stage 10 times without major refurbishment.

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We see that text data is ubiquitous in nature. There is a lot of text present in different forms such as posts, books, articles, and blogs. What is more interesting is the fact that there is a subset of Artificial Intelligence called Natural Language Processing (NLP) that would convert text into a form that could be used for machine learning. I know that sounds a lot but getting to know the details and the proper implementation of machine learning algorithms could ensure that one learns the important tools in the process.

Since the r e are newer and better libraries being created to be used for machine learning purposes, it would make sense to learn some of the state-of-the-art tools that could be used for predictions. I’ve recently come across a challenge on Kaggle about predicting the difficulty of the text.

The output variable, the difficulty of the text, is converted into a form that is continuous in nature. This makes the target variable continuous. Therefore, various regression techniques must be used for predicting the difficulty of the text. Since the text is ubiquitous in nature, applying the right processing mechanisms and predictions would be really valuable, especially for companies that receive feedback and reviews in the form of text.

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An almost perfect way to stealthily store malware.

Korean researchers have detected a vulnerability in SSDs that allows malware to plant itself directly in an SSD’s empty over-provisioning partition. As reported by BleepingComputer, this allows the malware to be nearly invincible to security countermeasures.

Over-provisioning is a feature included in all modern SSDs that improves the lifespan and performance of the SSD’s built-in NAND storage. Over-provisioning in essentially just empty storage space. But, it gives the SSD a chance to ensure that data is evenly distributed between all the NAND cells by shuffling data to the over-provisioning pool when needed.

While this space is supposed to be inaccessible by the operating system — and thus anti-virus tools — this new malware can infiltrate it and use it as a base of operations.

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