Tesla ditches radar sensors on self-driving systems. Photo: Getty Images No company in the auto industry can attract as much attention as Tesla, on the one hand because of CEO Elon Musk’s controversial statements, on the other hand, because Tesla regularly makes “unusual” decisions. “. At the end of May, Tesla once again became the focus of public opinion, and this time not because of a “brake failure”, but a configuration change. Recently, Tesla announced that starting in May, Model 3 and Model Y in North America will no longer be equipped with sensor radar, cancel the millimeter wave radar in front of the vehicle and only use the camera as a receiver. data collection of automatic driving systems. This means that Tesla is poised to be the first car company to deploy a “pure vision” autonomous driving solution. Meanwhile, other self-driving car manufacturers are integrating radar, LiDAR and cameras in autonomous driving systems. In fact, Tesla CEO Elon Musk has always been adamant against LiDAR, even repeatedly criticizing LiDAR in public. He thinks this is an expensive, unnecessary technology. Even Elon Musk has called those who believe in LiDAR technology “stupid” and car manufacturers using LiDAR technology will never achieve success. So, why is Musk so adamantly “disliked” LiDAR and even all radars, and only favors a camera technology solution? To clear this up, we may need a basic understanding of autopilot schematics. Camera and LiDAR To build a set of automated driving system solutions, the automatic driving algorithm is of course inseparable. In order to achieve calculations, sufficient information is required first. During driving, the vehicle’s information collection source is the large and small sensors on the body of the vehicle. Among them, to realize autonomous driving, the most important sensors are the camera and LiDAR (must add millimeter wave radar and ultrasonic sensor, of course). Therefore, the autonomous driving system is divided into camera-based vision array and LiDAR array. “Camera-based vision” uses a high-definition camera incorporating an image recognition algorithm, while LiDAR to ensure system stability. The camera array, or pure visual diagram, is essentially a complete simulation of the state of a person driving a car, first seeing the image with the “eye (camera)”, then transmitting it to the “brain (processor)” for processing and judgment, and then give the action “Legs (steering mechanism)”. In addition to Tesla, Chinese companies including Jikrypton and Baidu have also adopted pure vision solutions. LiDAR technology uses a laser to measure distances and build a 3D map of an object, by emitting and receiving the laser’s feedback and then analyzing that data to produce the desired results. Many Chinese car companies including Xiaopeng, Weilai, BAIC ARCFOX, and many other new products also announced this solution. Which solution is better? The advantage of the “pure vision” solution is that the video data captured by the camera is the closest to the real world perceived by the human eye, and also the closest to the form of human driving. At the same time, with LiDAR prices currently very high, a pure vision solution using only the camera should be easier to control costs. Tesla’s pure vision algorithm analysis is already very strong. In addition, the image obtained by the camera is well suited for object identification and classification, in case the algorithms are excellent, it can remove confounding items and enable automatic learning during driving. car. However, as a visual algorithm similar to the human eye, the camera is easily affected by light, in harsh weather or poor lighting conditions, it is easy to cause misjudgment. Previously, when AutoPilot was turned on, Tesla models accidentally braked automatically when going over an overpass or under a normal bridge, due to the algorithm using a sudden ball in the camera as an obstacle. Rendering a 3D environment map drawn by LiDAR Compared with pure vision solution, the advantages of LiDAR solution are longer monitoring distance, higher accuracy, more sensitive response speed and unaffected by ambient light. LiDAR’s three-dimensional information processing, object size calculation, and movement speed are more efficient. However, LiDAR has problems such as weak object recognition, high cost, and accuracy in extreme weather conditions such as rain and snow. Therefore, LiDAR must be used in conjunction with other sensors to ensure safe driving. In addition, the collection of large amounts of data requires high computing power of the processor, which equates to an increase in the cost of the media processor. Theoretically, the combination of camera-based vision and LiDAR is a relatively complete solution today, but after combining many factors such as existing technology and product cost, it is too difficult to use. use a “third solution” that integrates these two major solutions. Tesla’s trade-off The cost of LiDAR is much higher than high quality cameras. In fact, abandoning LiDAR, and even now abandoning millimeter wave radar, one of the key reasons Tesla made such a choice was to achieve lower costs. After all, the cost of LiDAR is very high, the price of this technology in the early days started at 100,000 USD, but now with the development of the electronics industry, its price has decreased but basically remains the same. maintained at several tens of thousands of dollars. However, compared to high-definition cameras, the price of only a few hundred dollars is still much higher. However, besides cost, Tesla also has technical trade-offs. First of all, LiDAR does not determine the specific nature of the object, so it is easy to cause wrong judgment. For example, if a large plastic bag appears in front of the vehicle, the camera solution may recognize it as a plastic bag and ignore it, but LiDAR will judge it as an obstruction and stop the vehicle. Second, millimeter wave radar also has many limitations. The detection range of millimeter wave radar is directly limited if the band is weak. It cannot identify pedestrians nor accurately model all surrounding obstacles. At the same time, due to the large amount of data obtained, it will also consume precious computing power of the processor. Finally, for Tesla, the best company in imaging solutions, they have invested heavily in imaging algorithms. To give up the large accumulation so far, and turn to another path, in terms of input – output ratio, it can be said that it is not worth it. Tesla is loyal to a completely camera-based vision solution Tesla Vsion camera system. On Tesla’s official North American website, during the introduction of Full Self-Driving FSD, the performance information of the millimeter wave radar was removed and only the image sensor and ultrasound. At the same time, Tesla also released the driver monitoring system on May 28. The Model 3 and Model Y vehicle cameras can monitor the driver’s behavior when the Autopilot system is activated, detecting and reminding the driver to keep his or her attention. Tesla states that “camera data will be stored in the vehicle, and the system will not send the data back to the company unless the user gives permission to share the data”. In addition, Tesla’s FSD V9.0 autopilot system will also be officially launched this year. According to Musk’s earlier statement, the FSD algorithm has been “refactored” to achieve pure vision through 8 cameras around the body, while effectively solving the previous “brake failure” problem. Tesla also warned that Autopilot and FSD systems would be less effective during this technical adjustment period. “During a short period of transition, Tesla Vision vehicles may experience some limitations or inactivity, such as Autosteer being limited to a top speed of 120.7 km/h. , Smart Summon (if equipped) and Emergency Lane Avoidance may be disabled during delivery,” Tesla’s announcement reads. Whether LiDAR will win the market with a secure advantage in the future, or a camera-based vision algorithm dominates the market with a lower cost advantage, the answer is still unclear. However, judging from the information currently available, Tesla will choose to stick with a pure vision solution.

The US has a rather slow 5G rollout. Photo: NetEase Since the advent of 5G technology, US carriers have been slow to deploy 5G networks. The main reasons are limited spectrum resources available for 5G, cumbersome deployment of 5G devices, and lack of killer applications for 5G services. The media often uses words like “fast” to describe 5G speeds, but the speed of 5G wireless network rollout in the US has nothing to do with the word. Over the past three years, the prospect of 5G wireless networks has been the focus of major media reports. But for most Americans, the real 5G that can achieve the desired effect is yet to emerge. Although all the major wireless carriers in the country claim that their 5G services have covered the entire US, industry analysts argue that this so-called 5G service is not much different from the US. 4G. According to market research firm Evercore ISI, most Americans may not enjoy 5G service that is significantly faster than current wireless communication networks. “Overall, it is expected that between late 2021 and late 2023, truly better 5G services will cover two-thirds of the US population,” said James Ratcliffe, an analyst at Evercore ISI. The first batch of mobile phones that support 5G networks was launched in mid-2019, according to Ratcliffe, in the most optimistic scenario, wireless network operators will be able to provide “real” 5G services to 2/3 of the US population in the next 30 months. Meanwhile, the first mobile phone to support 4G network was released in 2010, and the US 4G network was basically rolled out 18 months later. Analysts, academics and former industry executives say a combination of factors has resulted in a relatively slow rollout of 5G networks in the US. Some of these problems are related to network infrastructure: high-speed network transmission frequency bands and signal coverage are limited. Moreover, the new network equipment that needs to be on the shelves to deploy 5G networks is relatively cumbersome. In addition, many new devices are not made in the US, so the time to purchase the device will be longer. Industry observers also point out that the current 5G network still lacks breakthrough applications, and cannot stimulate user demand and promote the spread of services such as mobile video in 4G networks. John Roese, chief technology officer of Dell Technologies and a former executive at companies like Huawei and Nortel, said that although wireless operators have recently invested billions of dollars in 5G networks, when Because their revenue in 5G-related businesses is not stable, they may not be able to accelerate the construction of new networks. According to Mr. Roese, wireless network operators have had disappointing returns when investing in 4G networks. He said that the main beneficiaries of 4G networks are technology companies that provide applications and other services through the network. “So wireless operators are very cautious about this.” Limited frequency range Photo: Sina To meet the large traffic that 5G networks have to transport, carriers need to open “highways” for data. The spectrum is similar to the real estate that the government grants to carriers through auctions to build highways. The more land they have, the more lanes they can open for faster 5G and easier access. Currently, 5G networks mainly transmit data through low-band, mid-band and high-band. High-band (mmWave) aka millimeter wave has the highest data transfer rate and largest bandwidth, but the smallest coverage. Low band has wider coverage but slowest transmission speed. Finally, the mid-band is considered to be the balance between coverage and transmission speed. Companies like Verizon, AT&T, and T-Mobile all claim that their 5G networks now cover more than 200 million Americans, enough to meet US demand. However, most of these networks rely on low-band radio waves to complete coverage, and network transmission speeds are limited. Wireless network operators are currently focused on capturing mid-bands and have invested a lot of money in recent tenders for these resources. But the main problem facing these companies is that most of the mid-band bands have been occupied by US government agencies and other agencies, mainly for military communications and meteorological services. . Until now, wireless network operators could only use a fraction of the mid-band resources. US government agencies are also aware of the lack of mid-band resources. Earlier this year, the US auctioned a total of $81 billion in mid-band licenses, and plans to hold another auction this fall. When operators were preparing to roll out 5G nearly 10 years ago, they generally believed that 5G networks would primarily use high-frequency radio waves, said Stefan Pongratz, analyst at Dell’Oro Group. . This portion of the spectrum resource is essentially unused. But Pangraz says that rolling out a 5G network using millimeter waves requires building more base stations, which will certainly take time to increase deployment density of base stations. He said that until recent years, the US turned its attention to mid-band radio waves as a way to roll out 5G networks faster. Where is the profit point? Photo: Justin Hall Comics Brian Kelley, an associate professor of electrical engineering at the University of Texas at San Antonio, said the lack of disruptive applications is another major factor hindering the rollout of 5G networks. Mr. Kelly, who was an engineer at Motorola, said: “It can be said that this is the biggest factor that can stimulate the speed of 5G deployment.” According to analyst Craig Moffett, the 3 main advantages of 5G networks are faster data transmission (100 times faster than 4G), support for a large number of simultaneous connections, but also significantly improved response speed between devices. device is connected. “There isn’t a single revenue model that can combine these three advantages. It’s not even clear if consumers are willing to pay more for faster video downloads,” Moffett said. Moffitt said that because 5G networks can seamlessly connect sensors and other IoT devices, companies will likely be the first adopters of 5G services. He said that 5G networks “can be considered the next generation of Wi-Fi wireless networks, which are faster, more secure and can connect more devices”. But Moffett said the question is: “Will wireless operators build their own 5G networks for enterprises, or will the enterprises themselves deploy them?” This is important, he says, because if businesses choose to deploy their own 5G networks, wireless operators may lose the associated revenue and not be able to invest more money in building infrastructure. 5G layer. Device problem Currently, in the world, there are 5 companies that have successfully produced network equipment for 5G, none of which is in the US. Photo: Sina 5G networks are very different from 4G, in part because 5G networks need to transmit data at a higher frequency band. This means that upgrading 5G networks requires different technologies and devices. Evercore analyst Ratcliffe says installing new equipment can require finding a suitable location, obtaining the right permits, and even digging up roads and laying fiber optic cables to transfer data at high speeds between base stations. Gazzola, chief executive officer of Atlanta-based infrastructure company Bennett & Pless, said it is expected that by 2022, the construction work needed to bring wireless communication networks to 5G standards will increase. . However, the epidemic and technical obstacles have delayed progress. Equipment availability is also a common issue faced by wireless network operators. Dell’s Rose said that when 4G was first introduced around 2010, there were about a dozen technology vendors offering wireless networking equipment, including Canada’s Nortel and America’s Motorola. Today, there are only five major players in the global network equipment market: Nokia, Ericsson, Samsung, ZTE and Huawei. “Two in China. One in Finland, one in Sweden and one in Korea.” These suppliers are all located outside the US. Worse still, 5G network equipment requires computer chips while the chip industry has been struggling recently to cope with supply shortages.

On each microprocessor there are billions of transistors and nm (nanometer) – the unit of measurement of transistor size. The smaller the size, the more transistors the processor can hold, making it faster and more efficient. TSMC’s most advanced chip today uses a 5 nm process with about 173 million transistors per square millimeter. Silicon chip wafers in TSMC’s production line. Photo: TSMC. This breakthrough was discovered by the MIT team, with components optimized by TSMC and improved by NTU. The core component uses semi-metallic bismuth as the electrode of a two-dimensional material to replace silicon, allowing for reduced resistance and increased amperage. Energy efficiency will thus rise to unprecedented heights in the semiconductor industry. Chipmakers have been trying to stuff more and more transistors into increasingly smaller chips, but are nearing the limits of silicon-based technology. That prompted scientists to look for two-dimensional materials to replace silicon to produce chips on the 1 nm process or smaller. More transistors on a chip gives manufacturers more options to deliver core innovations to improve performance for leading tasks like AI and cloud computing, and pave the way for better security. Encryption and encryption are performed by hardware. The need to increase performance and save energy in each processor has never cooled down, especially in the era of the cloud, AI, and IoT. Most of today’s chip-integrated devices use 10 nm or 7 nm process technology. The two largest chip manufacturers in the world, TSMC and Samsung, are launching chips with the 5 nm process, while Intel is still at the 7 nm stage. TSMC also only plans to start moving to the 4mm process later this year before mass production in 2022.

Diem My 9X shows off her hot body with “sweet” curves This new set of photos makes people ecstatic at the beautiful beauty of the 1990 actress Diem My’s body is always up to standard with absolute measurements If she goes to the movies and in real life, Diem My’s style is quite simple, her event and photography fashion is very personal, hot, but equally sweet. Diem My’s three-round measurements in a stylish black outfit The outfit shows her underwear but she still looks luxurious Diem My 9x knows how to be diverse in all kinds of clothes when taking fashion photos The “tight” 3rd round of a beautiful actress in a black bodysuit is very flattering Diem My 9x has an extremely desirable appearance. Her current measurements are 83-63-94. In order to get the current body shape, the beauty strictly follows a healthy diet along with rigorous exercise Diem My’s fat-free body in a “fashionable” set with a blue-black shirt mixed with high-waisted pants and sexy leather socks Although she is 31 years old, the beauty of Diem My 9x is still young and radiant

Khanh Van shows off her fiery body with three-round measurements of 83-61-95 at the last Miss Universes 2020 semi-finals The smooth skin and tight round 1 of the 26-year-old long legs are not inferior to your country’s contestants The representative of Vietnam impresses the international audience with a bright smile Before leaving for the US to compete at Miss Universe 2020, Khanh Van once made the audience in the country ecstatic with a set of swimsuit photos with an impeccable body. Miss Khanh Van chose a Western-style makeup layout with curly brown hair and sharp makeup tones Miss Universe Vietnam 2019 once shared that she had to go through many challenges to have the body she dreamed of Khanh Van is free to relax in a bikini with a minimalistic shape that is extremely flattering Legs 9x tall 1m75 with body index 83-61-95 cm With a set of photos with a blue and purple bikini before the semi-final night, Vietnamese netizens are more confident that she will definitely shine on stage. To get this millimeter-standard body, Khanh Van once shared that she had to train with a personal trainer for a few months to tighten muscles and lose fat. The body is as flexible as the waves of Miss Khanh Van in a bikini as blue as the sea Regardless of the design of lingerie or bikini, Khanh Van is easily conquered and completely respects the curve The audience can see that the Vietnamese representative always chooses a curly hairstyle, bold makeup and healthy brown skin in every photo shoot with a bikini design.

Recently, the team upgraded the technique to power and program the multipoint biostimulator from a single transmitter. Thanks to new research, electrical and computer engineer Kaiyuan Yang and colleagues at the George R. Brown School of Engineering (Rice University) won an award at the Society of Electrical and Electrical Engineers’ Custom Integrated Circuits Conference death (IEEE). The researchers performed experiments showing that an alternating magnetic field can be controlled by a battery-powered generator outside the body. The transmitter can be mounted on a belt or harness, powering and programming two or more implants at least 60 millimeters away. The implant can be programmed with a delay measured in microseconds. That could allow them to coordinate the activation of multiple wireless pacemakers in individual heart chambers, says engineer Yang. “Our study shows that it is possible to program the implants to stimulate in a coordinated pattern. We sync every device, like a symphony. That gives us a lot of scope in stimulant treatments, whether it’s for the heart rhythm or the spinal cord.” The scientists also experimented using small implants. Each tissue is about the size and weight of a vitamin. Experiments performed on hydra vulgaris rodents have demonstrated that, for at least a short distance, such devices can stimulate two separate hydras to contract. Then, activate a fluorescent tag to respond to electrical signals and respond at controlled amplitudes along the rodent’s sciatic nerve. “A study on spinal cord regeneration showed that multipoint stimulation in a certain pattern helps restore the nervous system. Several clinical studies are underway, but all are using desktop devices. There is no implantable tool that can do this,” said engineer Yang. Lab devices, called MagNI (for electromagnetic nerve implantation). MagNI was introduced early last year as a cordless spinal cord stimulator for power and programming. That means the wire does not need to be passed through the patient’s skin. Because, putting the wire through the skin is a method that can cause infection.

Motorola partnered with Guru to develop remote wireless charging technology. Photo: Motorola GuRu powers devices at watts-over-meters, Guru’s proprietary technology has revolutionized the consumer experience and eliminates power as an everyday concern. . GuRu’s patented, miniature modules will allow devices to be powered at long distances by precise power delivery. Motorola and GuRu, will work together to meet Motorola’s stringent requirements for quality, power efficiency, and safety. Florian Bohn, CEO and Co-Founder of GuRu, said the partnership with Motorola will help Guru access the ecosystem of smart devices and solutions with the goal of improving the consumer experience. use. Guru will introduce true mobility to mobile smart devices and IoT sensors. GuRu’s technology enables safe, efficient, and customizable transmission of electricity through the air using integrated millimeter wave (mmWave) circuits, modules, and proprietary Smart RF Lensing technology. A miniature power transmission system that efficiently and accurately delivers power over long distances like never before. GuRu’s proprietary technology enables safe, accurate transmission and recovery of micro-watt to multi-watt-level wireless power over long distances. Photo: Guru GuRu’s wireless solution can be used as a charging hub or power source in a variety of settings that require access to a power source. It’s easy to set up, secure, and sophisticated enough to find and adjust different power levels to match the needs of any device. Even during use, it continuously charges devices and as a safety measure, reroutes power as needed. Motorola and Guru have not provided much information about this partnership and when phones using remote wireless charging technology will be available to consumers, but the two will work together to meet the requirements. strict requirements on quality, efficiency of electricity use and safety. Earlier this year, Motorola showed off a demo of a device allegedly called the Motorola One Hyper that charges two Moto Edge phones remotely, but didn’t say if it would work with Guru on the project. Remote wireless charging technology has also been researched by many companies since 2015, such as Energous company researched and claimed to be able to charge phones from about 4 meters away. This year, Xiaomi also introduced Mi Air Charging Technology, which in the future can remotely charge multiple devices at the same time.

IBM’s 2 nm chip had a density of 333 million transistors per square millimeter. By comparison, TSMC’s most advanced chip with a 5 nm process has about 173 million transistors, while Samsung’s 5 nm chip is 127 transistors per square millimeter. Billions of transistors are found on each microprocessor, and the size of the transistor is measured in nanometers. The smaller the size, the more transistors the processor can hold, making it faster and more energy efficient. IBM successfully built a chip with a size of only 2 nm but the most powerful in the world. Most of the processor chips found on personal computers or smartphones today are manufactured using a 7 nm or 10 nm process. For example, the 7-nm Apple A13 Bionic chip has 8.5 billion transistors. Meanwhile, the A14 Bionic microprocessor manufactured on a 5 nm process is integrated with 11.8 billion transistors. More transistors on a chip give manufacturers more options to deliver core innovations, to improve performance for leading tasks like AI and cloud computing, and pave the way for security. Cryptography and encryption are hardware-enforced. The need to increase performance and save energy in every processor has never cooled, especially in the era of the cloud, AI and IoT. The 2 nm chip technology is estimated to achieve 45% higher efficiency, 75% lower energy use than the advanced 7nm chips currently on the market.