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Using several bands of radar at once can give cars a kind of second sight
Seeing around the corner is simulated by modeling an autonomous vehicle approaching an urban intersection with four high-rise concrete buildings at the corners. A second vehicle is approaching the center via a crossing road, out of the AV’s line of sight, but it can be detected nonetheless through the processing of signals that return either by reflecting along multiple paths or by passing directly through the buildings.
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.
Multipath reflections and through-building transmission allow the autonomous vehicle [red circle, at right in each diagram] to begin detecting the second vehicle [red rectangle, bottom of each diagram] around the 0.45-second mark, at which time the second vehicle remains firmly occluded by the bottom-left building. Because both frequency bands produce “ghost targets” [blue circles] due to reflections and multiple paths, the system employs a Bayesian algorithm to determine the true targets and remove the ghosts. The algorithm uses a combination of ray tracing and fusion of the results over time across both the UHF and C bands.
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.
The truck and the car are fitted with wide-aperture multiband radar from Neural Propulsion Systems, the authors’ company. Note the very wide antenna above the windshield of the truck.Neural Propulsion Systems
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.
Behrooz Rezvani, a solid-state physicist, is the CEO of Neural Propulsion Systems, in Pleasanton, Calif., a driving-technology startup he cofounded in 2017.
Babak Hassibi is cofounder and chief technologist at driving technology startup Neural Propulsion Systems, in Pleasanton, Calif. He is also a professor of electrical engineering and computer science at California Institute of Technology, in Pasadena.
Fredrik Brännström is head of the communications systems group at Chalmers University of Technology, in Gothenburg, Sweden. He is also a research scientist at Neural Propulsion Systems, in Pleasanton, Calif.
Majid Manteghi is professor of electrical engineering at Virginia Tech, in Blacksburg.
Perhaps due to ignorance, we have high confidence in technology for automating tasks. For outperforming human drivers, robocars need to detect many subtle signals, which have not been codified yet. They belong to humans' innate abilities. So far, dead robots tell us that due to the complexity of automating tasks requiring innate abilities, dull jobs like household chore or driving will be left to human for the time being. Here is further clarification: https://www.the-waves.org/2020/08/02/dull-jobs-for-human-dead-robots-redefine-future-of-work/
Error-riddled astronomical tables inspired the first computer—and the first vaporware
Allison Marsh is a professor at the University of South Carolina and codirector of the university's Ann Johnson Institute for Science, Technology & Society. She combines her interests in engineering, history, and museum objects to write the Past Forward column, which tells the story of technology through historical artifacts.
During Charles Babbage’s lifetime, this 2,000-part clockwork was as near to completion as his Difference Engine ever got.
It was an idea born of frustration, or at least that’s how Charles Babbage would later recall the events of the summer of 1821. That fateful summer, Babbage and his friend and fellow mathematician John Herschel were in England editing astronomical tables. Both men were founding members of the Royal Astronomical Society, but editing astronomical tables is a tedious task, and they were frustrated by all of the errors they found. Exasperated, Babbage exclaimed, “I wish to God these calculations had been executed by steam.” To which Herschel replied, “It is quite possible.“
Babbage and Herschel were living in the midst of what we now call the Industrial Revolution, and steam-powered machinery was already upending all types of business. Why not astronomy too?
Babbage set to work on the concept for a Difference Engine, a machine that would use a clockwork mechanism to solve polynomial equations. He soon had a small working model (now known as Difference Engine 0), and on 14 June 1822, he presented a one-page “Note respecting the Application of Machinery to the Calculation of Astronomical Tables” to the Royal Astronomical Society. His note doesn’t go into much detail—it’s only one page, after all—but Babbage claimed to have “repeatedly constructed tables of squares and triangles of numbers” as well as of the very specific formula x2 + x + 41. He ends his note with much optimism: “From the experiments I have already made, I feel great confidence in the complete success of the plans I have proposed.” That is, he wanted to build a full-scale Difference Engine.
Perhaps Babbage should have tempered his enthusiasm. His magnificent Difference Engine proved far more difficult to build than his note suggested.
It wasn’t for lack of trying, or lack of funds. For Babbage managed to do something else that was almost as unimaginable: He convinced the British government to fund his plan. The government saw the value in a machine that could calculate the many numerical tables used for navigation, construction, finance, and engineering, thereby reducing human labor (and error). With an initial investment of £1,700 in 1823 (about US $230,000 today), Babbage got to work.
The 19th-century mathematician Charles Babbage’s visionary contributions to computing were rediscovered in the 20th century.The Picture Art Collection/Alamy
Babbage based his machine on the mathematical method of finite differences, which allows you to solve polynomial equations in a series of iterative steps that compare the differences in the resulting values. This method had the advantage of requiring simple addition only, which was easier to implement using gear wheels than one based on multiplication and division would have been. (The Computer History Museum has an excellent description of how the Difference Engine works.) Although Babbage had once dreamed of a machine powered by steam, his actual design called for a human to turn a crank to advance each iteration of calculations.
Difference Engine No. 1 was divided into two main parts: the calculator and the printing mechanism. Although Babbage considered using different numbering systems (binary, hexadecimal, and so on), he decided to stick with the familiarity of the base-10 decimal system. His design in 1830 had a capacity of 16 digits and six orders of difference. Each number value was represented by its own wheel/cam combination. The wheels represented only whole numbers; the machine was designed to jam if a result came out between whole numbers.
As the calculator cranked out the results, the printing mechanism did two things: It printed a table while simultaneously making a stereotype mold (imprinting the results in a soft material such as wax or plaster of paris). The mold could be used to make printing plates, and because it was made at the same time as the calculations, there would be no errors introduced by humans copying the results.
Difference Engine No. 1 contained more than 25,000 distinct parts, split roughly equally between the calculator and the printer. The concepts of interchangeable parts and standardization were still in their infancy. Babbage thus needed a skilled craftsman to manufacture the many pieces. Marc Isambard Brunel, part of the father-and-son team of engineers who had constructed the first tunnel under the Thames, recommended Joseph Clement. Clement was an award-winning machinist and draftsman whose work was valued for its precision.
Babbage and Clement were both brilliant at their respective professions, but they often locked horns. Clement knew his worth and demanded to be paid accordingly. Babbage grew concerned about costs and started checking on Clement’s work, which eroded trust. The two did produce a portion of the machine [shown at top] that was approximately one-seventh of the complete engine and featured about 2,000 moving parts. Babbage demonstrated the working model in the weekly soirees he held at his home in London.
The machine impressed many of the intellectual society set, including a teenage Ada Byron, who understood the mathematical implications of the machine. Byron was not allowed to attend university due to her sex, but her mother supported her academic interests. Babbage suggested several tutors in mathematics, and the two remained correspondents over their lifetimes. In 1835, Ada married William King. Three years later, when he became the first Earl of Lovelace, Ada became Countess of Lovelace. (More about Ada Lovelace shortly.)
Despite the successful chatter in society circles about Babbage’s Difference Engine, trouble was brewing—cost overruns, political opposition to the project, and Babbage and Clement’s personality differences, which were causing extreme delays. Eventually, the relationship between Babbage and Clement reached a breaking point. After yet another fight over finances, Clement abruptly quit in 1832.
Ada Lovelace championed Charles Babbage’s work by, among other things, writing the first computer algorithm for his unbuilt Analytical Engine.Interim Archives/Getty Images
Despite these setbacks, Babbage had already started developing a more ambitious machine: the Analytical Engine. Whereas the Difference Engine was designed to solve polynomials, this new machine was intended to be a general-purpose computer. It was composed of several smaller devices: one to list the instruction set (on punch cards popularized by the Jacquard loom); one (called the mill) to process the instructions; one (which Babbage called the store but we would consider the memory) to store the intermediary results; and one to print out the results.
In 1840 Babbage gave a series of lectures in Turin on his Analytical Engine, to much acclaim. Italian mathematician Luigi Federico Menabrea published a description of the engine in French in 1842, “Notions sur la machine analytique.” This is where Lady Lovelace returns to the story.
Lovelace translated Menabrea’s description into English, discreetly making a few corrections. The English scientist Charles Wheatstone, a friend of both Lovelace and Babbage, suggested that Lovelace augment the translation with explanations of the Analytical Engine to help advance Babbage’s cause. The resulting “Notes,” published in 1843 in Richard Taylor’s Scientific Memoirs, was three times the length of Menabrea’s original essay and contained what many historians consider the first algorithm or computer program. It is quite an accomplishment to write a program for an unbuilt computer whose design was still in flux. Filmmakers John Fuegi and Jo Francis captured Ada Lovelace’s contributions to computing in their 2003 documentary Ada Byron Lovelace: To Dream Tomorrow. They also wrote a companion article published in the IEEE Annals of the History of Computing, entitled “Lovelace & Babbage and the Creation of the 1843 ‘Notes’.”
Although Lovelace’s translation and “Notes” were hailed by leading scientists of the day, they did not win Babbage any additional funding. Prime Minister Robert Peel had never been a fan of Babbage’s; as a member of Parliament back in 1823, he had been a skeptic of Babbage’s early design. Now that Peel was in a position of power, he secretly solicited condemnations of the Difference Engine. In a stormy meeting on 11 November 1842, the two men argued past each other. In January 1843, Babbage was informed that Parliament was sending the finished portion of Difference Engine No. 1 to the King’s College Museum. Two months later, Parliament voted to withdraw support for the project. By then, the government had spent £17,500 (about US $3 million today) and waited 20 years and still didn’t have a working machine. You could see why Peel thought it was a waste.
But Babbage, perhaps reinvigorated by his work on the Analytical Engine, decided to return to the Difference Engine in 1846. Difference Engine No. 2 required only 8,000 parts and had a much more elegant and efficient design. He estimated it would weigh 5 tons and measure 11 feet long and 7 feet high. He worked for another two years on the machine and left 20 detailed drawings, which were donated to the Science Museum after he died in 1871.
In 1985, a team at the Science Museum in London set out to build the streamlined Difference Engine No. 2 based on Babbage’s drawings. The 8,000-part machine was finally completed in 2002.Science Museum Group
Although Difference Engine No. 2, like all the other engines, was never completed during Babbage’s lifetime, a team at the Science Museum in London set out to build one. Beginning in 1985, under the leadership of Curator of Computing Doron Swade, the team created new drawings adapted to modern manufacturing techniques. In the process, they sought to answer a lingering question: Was 19th-century precision a limiting factor in Babbage’s design? The answer is no. The team concluded that if Babbage had been able to secure enough funding and if he had had a better relationship with his machinist, the Difference Engine would have been a success.
That said, some of the same headaches that plagued Babbage also affected the modern team. Despite leaving behind fairly detailed designs, Babbage left no introductory notes or explanations of how the pieces worked together. Much of the groundbreaking work interpreting the designs was done by Australian computer scientist and historian Allan G. Bromley, beginning in 1979. Even so, the plans had dimension inconsistencies, errors, and entire parts omitted (such as the driving mechanism for the inking), as described by Swade in a 2005 article for the IEEE Annals of the History of Computing.
The team had wanted to complete the Difference Engine by 1991, in time for the bicentenary of Babbage’s birth. They did finish the calculating section by then. But the printing and stereotyping section—the part that would have alleviated all of Babbage’s frustrations in editing those astronomical tables—took another nine years. The finished product is on display at the Science Museum.
A duplicate engine was built with funding from former Microsoft chief technology officer Nathan Myhrvold. The Computer History Museum displayed that machine from 2008 to 2016, and it now resides in the lobby of Myhrvold’s Intellectual Ventures in Bellevue, Wash.
The title of the textbook for the very first computer science class I ever took was The Analytical Engine. It opened with a historical introduction about Babbage, his machines, and his legacy. Babbage never saw his machines built, and after his death, the ideas passed into obscurity for a time. Over the course of the 20th century, though, his genius became more clear. His work foreshadowed many features of modern computing, including programming, iteration, looping, and conditional branching. These days, the Analytical Engine is often considered an invention 100 years ahead of its time. It would be anachronistic and ahistorical to apply today’s computer terminology to Babbage’s machines, but he was clearly one of the founding visionaries of modern computing.
Part of a continuing series looking at photographs of historical artifacts that embrace the boundless potential of technology.
An abridged version of this article appears in the June 2022 print issue as “The Clockwork Computer."
IEEE also mourns the loss of other members
Joanna Goodrich is the assistant editor of The Institute, covering the work and accomplishments of IEEE members and IEEE and technology-related events. She has a master's degree in health communications from Rutgers University, in New Brunswick, N.J.
Srihari helped create an artificial intelligence system in 1991 that enabled machines to read handwritten letters. The U.S. Postal Service still uses the system to sort mail. Srihari was a pioneer in the field of computational forensics who in 2002 developed CEDAR-FOX, a software system that identifies people through their handwriting.
He was a professor of computer science and engineering for more than 40 years. He taught at the State University of New York as well as the University of Buffalo, where he founded its Center of Excellence for Document Analysis and Recognition. The faculty and students use the CEDAR research lab to work on technologies involving pattern recognition, machine learning, data mining, information retrieval, and computational linguistics.
It was at CEDAR where Srihari helped develop the AI system. The U.S. Postal Service provided the program with more than US $60 million in funding during the project’s 25 years.
In 2002 Srihari created CEDAR-FOX, which has been updated to allow the system to identify people through their fingerprints and shoe prints.
Srihari held seven U.S. patents.
Because of his expertise, Srihari was asked in 2007 to serve on the U.S. National Academy of Sciences’ committee on identifying the needs of the forensic science community, the only computer scientist on the body. It produced a report in 2009 about how the U.S. criminal justice system could strengthen its use of forensic science.
Srihari received bachelor’s degrees in physics and mathematics in 1967 from Bangalore University in India. He also earned a bachelor’s degree in electrical communication engineering in 1970 from the Indian Institute of Science, in Bangalore. Srihari went on to earn a Ph.D. in computer and information science in 1976 from Ohio State University, in Columbus.
Former head of Mitre’s space surveillance systems
Gager joined the research and engineering division of AIL, in St. James, N.Y., in 1951. There he conducted research in radar techniques and helped develop technologies such as moving-target identification equipment, monopulse radar, and high-resolution radar.
He left the company in 1979 to join The Mitre Corp. in McLean, Va., where he helped develop surveillance sensors and technology for electronic warfare and tactical defense measures. He was promoted in 1984 to head the company’s space surveillance systems department.
After he retired, he and his wife moved to Norwell, Mass., and he became an active IEEE volunteer. He also taught a course about the history and evolution of U.S. intelligence operations for Harvard’s Institute for Learning in Retirement.
Gager received a bachelor’s degree in electrical engineering in 1950 from the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering).
Founder of the IEEE Microwave Theory and Techniques Society’s Milwaukee Section
Ishii was an active IEEE volunteer who established the IEEE Microwave Theory and Techniques Society Milwaukee Section. He served as an associate editor of IEEE Transactions on Circuits and Systems from 1989 to 1991.
He served as a consultant for several companies including Wisconsin Electric Power, Honeywell, and Johnson Controls, as well as a number of law firms.
Ishii received a bachelor’s degree and a Ph.D. in engineering from Nihon University, in Tokyo. He stayed on as an electrical engineering professor after graduating in 1950. He left six years later to pursue a second master’s degree and a doctorate at the University of Wisconsin-Madison. He graduated in 1959 and joined Marquette University, in Milwaukee, as a professor. He retired in 1998 and was named professor emeritus.
He held two U.S. patents and three Japanese patents for microwave devices.
Ishii was honored with several awards including the 2000 IEEE Millennium Medal, the 1984 IEEE Centennial Medal, and the 1969 T.C. Burnum IEEE Milwaukee Section Memorial Award.
Megargel worked as an electrical engineer for several companies including General Electric, Valley Forge, and International Signal and Control.
After graduating in 1945 from Lake Ariel Consolidated School, in Pennsylvania, he enlisted in the U.S. Army. He was stationed in Japan and helped with the country’s reconstruction projects following World War II. He was honorably discharged in 1947.
He was granted several U.S. patents.
Megargel received a bachelor’s degree in electrical engineering in 1951 from Pennsylvania State University.
Mirela Sechi Moretti Annoni Notare
Editorial advisory board member of The Institute
Notare was a professor at the Universidade Federal de Santa Catarina, in Florianópolis, Brazil.
She was an active IEEE member for 25 years, serving on several boards and committees including The Institute’s editorial advisory board. She was a member of the Region 9 NoticIEEEro newsletter committee and was on the editorial staff of IEEE Latin America Transactions.
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