We all hoped that 2021 would signal a return to normalcy, but that never quite happened. Microsoft’s hardware teams enjoyed strong success this year. But for Windows, software, and services? Not so much.
Microsoft’s year in review has become a holiday tradition of sorts here at PCWorld—recapping Microsoft’s strengths, failures, and moments that made us scratch our heads and mutter, “Seriously, what.” So if you enjoyed our recap of 2020, grab a glass of holiday cheer and settle down.
2021. What a year, huh?
WIN: Hybrid work
A generally pathetic sports team like the University of California Golden Bears can salvage an entire season’s worth of futility with a win over a key opponent, such as Stanford. And Microsoft made what wasn’t the happiest of years into a success with an ongoing, enormous win: Microsoft continued to facilitate working from home, and arguably led the way in doing so.
Some may prefer Google’s Workspace, Zoom, or Slack, but Microsoft Teams has clearly evolved into an effective meeting and videoconferencing tool as well as a collaboration solution. Microsoft has invested heavily into partnerships for Teams hardware as well as some very thoughtful ways of integrating in-office work with remote workers. Microsoft’s online conferences continued to set the bar. Microsoft 365 is now almost a must-have for businesses and consumers alike, because of the value all of these add. It’s been an enormous success for Microsoft as a whole.
FAIL: Microsoft Teams for consumers
It’s clear, though, that the victory party got a little out of hand. Some inebriated Microsoft M365 exec probably shouted “Let’s make everyone use Teams!!” and Microsoft Teams for Consumers was born. Then some madman mocked up a Windows 11 taskbar with Teams Chat integrated right into it. That was pushed to production, too! Only after the hangovers had worn off did someone realize that no, no one actually wants to use Teams in their personal lives. Unfortunately, by then the damage had been done.
WTF: Microsoft’s social font-picking experiment
In April, Microsoft tried out a bizarre social experiment, letting users vote on which new font would be used in 2022 within Office, Windows, and more. Microsoft encouraged users to lobby for Seaford (“organic and asymmetric forms!”), Skeena (a “humanist” sans serif), Bierstadt, and more, probably hoping a heated debate would polarize the Internet. Instead, there was… well, nothing. How do we go on without proper closure?
WTF: Microsoft Mesh and the metaverse
Months before Facebook rebranded itself as Meta and made “metaverse” the most overused term of 2021, there was Microsoft Mesh, Microsoft’s virtual-reality platform and the surprise reveal at its Microsoft Ignite conference. Looking back, it’s interesting to see how Mesh evolved over just this year: in March, when Microsoft launched Mesh, Microsoft technical fellow Alex Kipman characterized Mesh as a virtual-meeting platform, implying that the future of meetings was virtual reality.
Meta then adopted the same approach, to general disgust. By November, Microsoft’s vision of the metaverse had been recast as virtual Teams avatars in preparation for a preview to be released in 2022.
FAIL: Windows 11
To be fair, we’re biased: PCWorld’s review of Windows 11 concluded that Windows 11 was “unnecessary,” in part because Windows 10 is still an excellent operating system, and in part because Windows 11 launched with many, many rough edges. Statcounter, however, doesn’t even show Windows 11 in its desktop OS market-share list. AdDuplex says Windows 11’s share is 8.6 percent. LanSweeper says that it’s 0.2 percent. For whatever reason, Windows 11 hasn’t taken off.
While Windows 11 may not be succeeding yet, Windows itself is. Microsoft probably cares as much as more about Microsoft 365 subscription revenue and monthly users of Edge and Bing than anything else. To Microsoft’s bean-counters, Windows is just a gateway to those services.
FAIL: Windows 11’s hardware requirements
It’s fair to say that we have some TPM PTSD we’re still working through. For a few weeks, Microsoft swung back and forth on exactly which PCs could upgrade to Windows 11, and users tried to figure out what the hardware requirements of Windows 11 were, and what they needed to do if their PC didn’t meet them. Microsoft may have had the best of intentions in excluding millions of PCs for security reasons, but all Microsoft had to do was to simply sit down with reporters and clearly communicate what was going on, what consumers could expect, and the thinking behind it. They didn’t.
This was clearly Microsoft’s worst communications debacle in years, and we can only hope that some senior executive was called on the carpet for it.
FAIL: Windows 11’s lack of browser choice
Microsoft Edge is a solid browser, adding new features all the time — which is why Microsoft screwed this up so badly. Windows 11 makes it a royal pain to set up a third-party browser as the default, robbing users of the choice between any of the excellent browsers that are available to Windows users, from Brave to Vivaldi. If that decision wasn’t legally anti-competitive, it sure felt like it. Even a way to redirect Edge-specific links was blocked. Taking steps to reverse the decision via a Windows 11 preview is a small step, but it’s one that should have never been made in the first place.
Mark Hachman / IDG
FAIL: Windows 10X
As we now know, Windows 10X was rolled into Windows 11. What we didn’t know when Windows 10X leaked in January was that it was going to replace Windows 10 — it would have made a drab but functional replacement for Windows 10 Home in S Mode (Windows 10 S) in Chromebooks. But nope, Windows 10X evolved into Windows 11, and the “true” replacement for Windows 10 S will be…
WTF: Windows 11 SE and the Surface Laptop SE
Windows 11 SE and the complementary Surface Laptop SE honestly felt like Microsoft saying well, what the hell — let’s just throw this at the wall and see if it sticks. Remember, there is still a Windows 11 Home in S Mode, and Windows 10 S was originally designed to compete with Chrome OS. Now it’s Windows 11 SE, and the Surface Laptop SE is now the new Chromebook killer. Good luck with that, folks.
WIN: Surface Pro 7+ and Surface Pro 8
It was a little surprising to see Microsoft announce two new tablets during 2021, even if the Surface Pro 7+ was technically for businesses only. But even though both debuted among a crop of the best Windows tablets we’ve seen in years, our review of the Surface Pro 7+ as well as our Surface Pro 8 review simply demonstrated that Microsoft continues to be at the top of its game where full-sized Windows tablets are concerned.
WIN: Surface Laptop Studio
While we may have expected a new Surface Book, Microsoft’s “Surface Laptop Book Pro” fusion includes an impressive pull-forward design with relatively high-performance CPU and GPU options, as our Surface Laptop Studio review shows. Can we continue the push forward and plan out an Xbox gaming laptop for 2022?
Mark Hachman / IDG
WIN: Surface Laptop 4
For the most part, Microsoft’s generally strong Surface showing continued to offset Microsoft’s struggles in software and services. Our review of the Surface Laptop 4 was a success both for AMD’s Ryzen processor as well as Microsoft itself. Note to Microsoft’s design team: deepen the key travel. Microsoft’s keyboards were once the best in the industry, and now they’re merely good.
FAIL: Surface Duo 2
I kept my personal SIM in the Surface Duo 2 folding phone well after we published PCWorld’s Surface Duo 2 review, but today I simply find myself instinctively grabbing other phones instead. Surprisingly, it was the Duo 2’s UI that pushed me over the edge. Not the bugs, but simply how the phone keeps separate columns of apps on either screen, and there’s too much pain wading through them to find what I want. In all, the Surface Duo 2 was a decent effort that fell short.
Mark Hachman / IDG
WIN: Xbox Game Pass
While you still can’t find Microsoft’s Xbox Series X game console in any reasonable quantities, two things soothed the blow: the availability of the affordable Xbox Series S, and the success of Microsoft’s Xbox Game Pass. Microsoft’s game subscription is basically a must-have by this point, allowing gamers day-one access to both Microsoft’s AAA titles (for the PC and the Xbox!) as well as a number of indie games. Here’s how to get Game Pass for cheap.
WIN: Windows 365
This is absolutely a gut call, but I suspect that the groundwork behind Windows 365 (Windows in the cloud) will eventually pay off. We already have streaming sticks, and some way of tucking a PC in your pocket and plugging it into an available HDMI port seems like a viable future. The question will be how users type, touch, and interact with Windows, all problems that the modalities maestros at Microsoft will eventually solve.
WIN: The Financial Modeling World Cup, aka the Excel Championship
I never expected that tuning in on a Saturday morning to watch financial modelers work through Microsoft Excel challenges would be the most wholesome Microsoft experience of 2021, but it absolutely was. There’s a certain intersection of meme culture, pessimism, sportsmanship, and good-natured trolling that defines modern college football fandom, and the FMWC felt like a bunch of fans who had collectively tuned into the best game of the day. Click on the YouTube links in our FMWC story and you’ll see what we mean.
The FMWC had everything: nerd culture; insightful, live commentary (Tim Heng!), and even a knowledgeable fanbase who clearly enjoyed themselves. This was Excel as esports, and it was stunningly compelling.
So that was 2021, from Microsoft’s perspective — a lot of head-scratching decisions, some rage, but then joy where none was expected. Here’s to a better 2022 for all of us!
Original Source: pcworld.com
We’ve Set Target to Build a New India Before 100th Year of Independence: PM Modi
New Delhi [India], January 23 (ANI): Prime Minister Narendra Modi after unveiling the hologram statue of Netaji Subhas Chandra Bose at India Gate on Sunday evening, said that the government has a target to build a new India before the 100th year of independence.
“Netaji used to say ‘Never lose faith in the dream of independent India. There is no power in the world that can shake India’,” PM Modi said while addressing the
Letting Robocars See Around Corners
An autonomous car needs to do many things to
make the grade, but without a doubt, sensing and understanding its environment are the most critical. A self-driving vehicle must track and identify many objects and targets, whether they’re in clear view or hidden, whether the weather is fair or foul.
Today’s radar alone is nowhere near good enough to handle the entire job—cameras and lidars are also needed. But if we could make the most of radar’s particular strengths, we might dispense with at least some of those supplementary sensors.
Conventional cameras in stereo mode can indeed detect objects, gauge their distance, and estimate their speeds, but they don’t have the accuracy required for fully autonomous driving. In addition, cameras do not work well at night, in fog, or in direct sunlight, and systems that use them are prone to
being fooled by optical illusions. Laser scanning systems, or lidars, do supply their own illumination and thus are often superior to cameras in bad weather. Nonetheless, they can see only straight ahead, along a clear line of sight, and will therefore not be able to detect a car approaching an intersection while hidden from view by buildings or other obstacles.
Radar is worse than lidar in range accuracy and angular resolution—the smallest angle of arrival necessary between two distinct targets to resolve one from another. But we have devised a novel radar architecture that overcomes these deficiencies, making it much more effective in augmenting lidars and cameras.
Our proposed architecture employs what’s called a sparse, wide-aperture multiband radar. The basic idea is to use a variety of frequencies, exploiting the particular properties of each one, to free the system from the vicissitudes of the weather and to see through and around corners. That system, in turn, employs advanced signal processing and
sensor-fusion algorithms to produce an integrated representation of the environment.
We have experimentally verified the theoretical performance limits of our radar system—its range, angular resolution, and accuracy. Right now, we’re building hardware for various automakers to evaluate, and recent road tests have been successful. We plan to conduct more elaborate tests to demonstrate around-the-corner sensing in early 2022.
Each frequency band has its strengths and weaknesses. The band at 77 gigahertz and below can pass through 1,000 meters of dense fog without losing more than a fraction of a decibel of signal strength. Contrast that with lidars and cameras, which lose 10 to 15 decibels in just 50 meters of such fog.
Rain, however, is another story. Even light showers will attenuate 77-GHz radar as much as they would lidar. No problem, you might think—just go to lower frequencies. Rain is, after all, transparent to radar at, say, 1 GHz or below.
This works, but you want the high bands as well, because the low bands provide poorer range and angular resolution. Although you can’t necessarily equate high frequency with a narrow beam, you can use an antenna array, or highly directive antenna, to project the millimeter-long waves in the higher bands in a narrow beam, like a laser. This means that this radar can compete with lidar systems, although it would still suffer from the same inability to see outside a line of sight.
For an antenna of given size—that is, of a given array aperture—the angular resolution of the beam is inversely proportional to the frequency of operation. Similarly, to achieve a given angular resolution, the required frequency is inversely proportional to the antenna size. So to achieve some desired angular resolution from a radar system at relatively low UHF frequencies (0.3 to 1 GHz), for example, you’d need an antenna array tens of times as large as the one you’d need for a radar operating in the K (18- to 27-GHz) or W (75- to 110-GHz) bands.
Even though lower frequencies don’t help much with resolution, they bring other advantages. Electromagnetic waves tend to diffract at sharp edges; when they encounter curved surfaces, they can diffract right around them as “creeping” waves. These effects are too weak to be effective at the higher frequencies of the K band and, especially, the W band, but they can be substantial in the UHF and C (4- to 8-GHz) bands. This diffraction behavior, together with lower penetration loss, allows such radars to detect objects
around a corner.
One weakness of radar is that it follows many paths, bouncing off innumerable objects, on its way to and from the object being tracked. These radar returns are further complicated by the presence of many other automotive radars on the road. But the tangle also brings a strength: The widely ranging ricochets can provide a computer with information about what’s going on in places that a beam projected along the line of sight can’t reach—for instance, revealing cross traffic that is obscured from direct detection.
To see far and in detail—to see sideways and even directly through obstacles—is a promise that radar has not yet fully realized. No one radar band can do it all, but a system that can operate simultaneously at multiple frequency bands can come very close. For instance, high-frequency bands, such as K and W, can provide high resolution and can accurately estimate the location and speed of targets. But they can’t penetrate the walls of buildings or see around corners; what’s more, they are vulnerable to heavy rain, fog, and dust.
Lower frequency bands, such as UHF and C, are much less vulnerable to these problems, but they require larger antenna elements and have less available bandwidth, which reduces range resolution—the ability to distinguish two objects of similar bearing but different ranges. These lower bands also require a large aperture for a given angular resolution. By putting together these disparate bands, we can balance the vulnerabilities of one band with the strengths of the others.
Different targets pose different challenges for our multiband solution. The front of a car presents a smaller radar cross section—or effective reflectivity—to the UHF band than to the C and K bands. This means that an approaching car will be easier to detect using the C and K bands. Further, a pedestrian’s cross section exhibits much less variation with respect to changes in his or her orientation and gait in the UHF band than it does in the C and K bands. This means that people will be easier to detect with UHF radar.
Furthermore, the radar cross section of an object decreases when there is water on the scatterer’s surface. This diminishes the radar reflections measured in the C and K bands, although this phenomenon does not notably affect UHF radars.
The tangled return paths of radar are also a strength because they can provide a computer with information about what’s going on sideways—for instance, in cross traffic that is obscured from direct inspection.
Another important difference arises from the fact that a signal of a lower frequency can penetrate walls and pass through buildings, whereas higher frequencies cannot. Consider, for example, a 30-centimeter-thick concrete wall. The ability of a radar wave to pass through the wall, rather than reflect off of it, is a function of the wavelength, the polarization of the incident field, and the angle of incidence. For the UHF band, the transmission coefficient is around –6.5 dB over a large range of incident angles. For the C and K bands, that value falls to –35 dB and –150 dB, respectively, meaning that very little energy can make it through.
A radar’s angular resolution, as we noted earlier, is proportional to the wavelength used; but it is also inversely proportional to the width of the aperture—or, for a linear array of antennas, to the physical length of the array. This is one reason why millimeter waves, such as the W and K bands, may work well for autonomous driving. A commercial radar unit based on two 77-GHz transceivers, with an aperture of 6 cm, gives you about 2.5 degrees of angular resolution, more than an order of magnitude worse than a typical lidar system, and too little for autonomous driving. Achieving lidar-standard resolution at 77 GHz requires a much wider aperture—1.2 meters, say, about the width of a car.
Besides range and angular resolution, a car’s radar system must also keep track of a lot of targets, sometimes hundreds of them at once. It can be difficult to distinguish targets by range if their range to the car varies by just a few meters. And for any given range, a uniform linear array—one whose transmitting and receiving elements are spaced equidistantly—can distinguish only as many targets as the number of antennas it has. In cluttered environments where there may be a multitude of targets, this might seem to indicate the need for hundreds of such transmitters and receivers, a problem made worse by the need for a very large aperture. That much hardware would be costly.
One way to circumvent the problem is to use an array in which the elements are placed at only a few of the positions they normally occupy. If we design such a “sparse” array carefully, so that each mutual geometrical distance is unique, we can make it behave as well as the nonsparse, full-size array. For instance, if we begin with a 1.2-meter-aperture radar operating at the K band and put in an appropriately designed sparse array having just 12 transmitting and 16 receiving elements, it would behave like a standard array having 192 elements. The reason is that a carefully designed sparse array can have up to 12 × 16, or 192, pairwise distances between each transmitter and receiver. Using 12 different signal transmissions, the 16 receive antennas will receive 192 signals. Because of the unique pairwise distance between each transmit/receive pair, the resulting 192 received signals can be made to behave as if they were received by a 192-element, nonsparse array. Thus, a sparse array allows one to trade off time for space—that is, signal transmissions with antenna elements.
Seeing in the rain is generally much easier for radar than for light-based sensors, notably lidar. At relatively low frequencies, a radar signal’s loss of strength is orders of magnitude lower.Neural Propulsion Systems
In principle, separate radar units placed along an imaginary array on a car should operate as a single phased-array unit of larger aperture. However, this scheme would require the joint transmission of every transmit antenna of the separate subarrays, as well as the joint processing of the data collected by every antenna element of the combined subarrays, which in turn would require that the phases of all subarray units be perfectly synchronized.
None of this is easy. But even if it could be implemented, the performance of such a perfectly synchronized distributed radar would still fall well short of that of a carefully designed, fully integrated, wide-aperture sparse array.
Consider two radar systems at 77 GHz, each with an aperture length of 1.2 meters and with 12 transmit and 16 receive elements. The first is a carefully designed sparse array; the second places two 14-element standard arrays on the extreme sides of the aperture. Both systems have the same aperture and the same number of antenna elements. But while the integrated sparse design performs equally well no matter where it scans, the divided version has trouble looking straight ahead, from the front of the array. That’s because the two clumps of antennas are widely separated, producing a blind spot in the center.
In the widely separated scenario, we assume two cases. In the first, the two standard radar arrays at either end of a divided system are somehow perfectly synchronized. This arrangement fails to detect objects 45 percent of the time. In the second case, we assume that each array operates independently and that the objects they’ve each independently detected are then fused. This arrangement fails almost 60 percent of the time. In contrast, the carefully designed sparse array has only a negligible chance of failure.
Seeing around the corner can be depicted easily in simulations. We considered an autonomous vehicle, equipped with our system, approaching an urban intersection with four high-rise concrete buildings, one at each corner. At the beginning of the simulation the vehicle is 35 meters from the center of the intersection and a second vehicle is approaching the center via a crossing road. The approaching vehicle is not within the autonomous vehicle’s line of sight and so cannot be detected without a means of seeing around the corner.
At each of the three frequency bands, the radar system can estimate the range and bearing of the targets that are within the line of sight. In that case, the range of the target is equal to the speed of light multiplied by half the time it takes the transmitted electromagnetic wave to return to the radar. The bearing of a target is determined from the incident angle of the wavefronts received at the radar. But when the targets are not within the line of sight and the signals return along multiple routes, these methods cannot directly measure either the range or the position of the target.
We can, however,
infer the range and position of targets. First we need to distinguish between line-of-sight, multipath, and through-the-building returns. For a given range, multipath returns are typically weaker (due to multiple reflections) and have different polarization. Through-the-building returns are also weaker. If we know the basic environment—the position of buildings and other stationary objects—we can construct a framework to find the possible positions of the true target. We then use that framework to estimate how likely it is that the target is at this or that position.
As the autonomous vehicle and the various targets move and as more data is collected by the radar, each new piece of evidence is used to update the probabilities. This is Bayesian logic, familiar from its use in medical diagnosis. Does the patient have a fever? If so, is there a rash? Here, each time the car’s system updates the estimate, it narrows the range of possibilities until at last the true target positions are revealed and the “ghost targets” vanish. The performance of the system can be significantly enhanced by fusing information obtained from multiple bands.
We have used experiments and numerical simulations to evaluate the theoretical performance limits of our radar system under various operating conditions. Road tests confirm that the radar can detect signals coming through occlusions. In the coming months we plan to demonstrate round-the-corner sensing.
The performance of our system in terms of range, angular resolution, and ability to see around a corner should be unprecedented. We expect it will enable a form of driving safer than we have ever known.
Source Here: spectrum.ieee.org
Staff of China-Laos Railway Celebrate Upcoming Chinese New Year
© Provided by Xinhua
VIENTIANE, Jan. 23 (Xinhua) — China-Laos Railway Luang Prabang Operation Management Center, run by China Railway Kunming Group, held celebration activities on Sunday for the upcoming Chinese Lunar New Year.
Original Article: bignewsnetwork.com
Global1 month ago
C.D.C. Panel Will Discuss Blood Clot Risk Linked to J.&J.’s Vaccine
Medicine2 months ago
In Finland, New Swedish PM Discusses Forestry, Security Policy
Biz2 months ago
What You Need to Know About Online Business in 2022
Biz2 months ago
OnlineBusiness.com Acquires CSEO, a Leading Marketing Company for Small Businesses
Biz2 months ago
Top Domain Sales for Q3 2021
Commerce2 months ago
USDA Invests $633 Million in Climate-Smart and Resilient Infrastructure for People in Rural Communities
Global2 months ago
Omicron Case With a New York Tie Shows How Virus Outpaces Response
Lifestyle2 months ago
OpEd: LIRR Better Than Ever With Infrastructure Upgrades