BRL Test has Agilent / HP 8903B Audio Analyzer Sale Priced at $999
Agilent 8903B Audio Analyzer
The Keysight/ Agilent/ HP 8903B audio analyzer. Ideally suited for audio measurements from 20 Hz to 100 kHz. The 8903B is an easy to use low-distortion audio source , high-performance distortion analyzer, frequency counter, ac voltmeter, dc voltmeter, and SINAD meter. With microprocessor control of source and analyzer, the 8903B can perform stimulus-response measurements (such as signal-to-noise ratio and swept distortion) automatically with no additional equipment. For ease of use, most measurements on the 8903B are made with only one or two keystrokes. The 8903B automatically tunes and auto ranges for maximum accuracy and resolution. For quick identification of input signals, the analyzer counts and displays the input frequency in all ac measurement modes.
Frequency Range: 50 Hz to 100 kHz
Display Range: 0 to 99.99 dB Accuracy: ±1 dB
Input Voltage Range: 50 mV to 300 V
Residual Noise (the higher of): 80 kHz BW: −85 dB or 17 µV 500 kHz BW: −70 dB or 50 µV
Time to Return First Measurement: <2.5 second
Measurement Rate: One reading per second
Range: 20 Hz to 100 kHz
Accuracy: 0.3% of setting
Range: 0.6 mV to 6 V open circuit
Resolution: 0.3% or better
Accuracy: 2% of setting 60 mV to 6 V, 20 Hz to 50 kHz. 3% of setting 6 mV to 6 V, 20 Hz to 100 kHz. 5% of setting 0.6 mV to 6 mV, 20 kHz to 100 kHz.
Flatness (1 kHz reference): ±0.7% (±0.06 dB), 20 Hz to 20 kHz. ±2.5% (±0.22 dB), 20 Hz to 100 kHz.
Distortion and Noise (the higher of): 80 kHz BW: −80 dB or 15 µV, 20 Hz to 20 kHz. 500 kHz BW: −70 dB or 38 µV, 20 Hz to 50 kHz. −65 dB or 38 µV, 50 kHz to 100 kHz.
Impedance: 600 Ω ±1% or 50 Ω ±2% front panel or HP-IB programmable (47 special function).
Frequency Switching Speed: <3 ms (does not include HP-IB programming time)
Output Level Switching Speed: 20 ms (does not include HP-IB programming time)
Sweep Mode: Log sweep with up to 500 points per decade or 255 points total between entered start and stop frequencies.
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Matthew Meiller – Impact of Antenna Design, Tune and Match on Wireless Range
By: Matthew Meiller
Abstract: Wireless device range can be the pivotal make or break characteristic of a successful end product. This paper will dig into the mystery and explore the mechanisms by which wireless range can be reduced or optimized through RF and antenna design. The discussion is relevant to board and system- level circuit and antenna design. The useful rule of thumb that every 1dB of additional RF loss reduces wireless range by 10% is presented.
Index Terms— Wi-Fi, Bluetooth, BLE, Zigbee, RFID, GSM, GPS, MBAN, HBAN, UWB, CDMA, Chip Antenna, Circuit Board Antenna, Wireless Range Reduction, Wireless Range Optimization, Radio Module, 802.11 and 802.15.4
Any RF engineer who has optimized RF or microwave system hardware in the lab will agree that squeezing out the last 1 or 2dB from a design can be the most challenging aspect. After reading this paper you may better appreciate the value of such rigor. This is where the rubber meets the road for applying the art and science of RF design to the development of wireless products. At this point the product requirements may be defined, the theoretical path loss calculations may be complete and you want to ensure execution of the hardware development goes smoothly. Or, the product may be designed and prototypes delivered and debugged, but questions are being asked regarding the wireless range or lack thereof. This article will help the reader understand quantitatively how much wireless range may be lost if the antenna tuning and match steps are neglected, there is more RF loss in the design than anticipated or a related aspect of the design is out of control.
Unintended Loss in the Design
There are many possible sources of insertion loss, mismatch loss and general degradation of antenna gain. These are RF signal losses resulting from product design decisions and features. Collectively we will refer to these as unintended losses and all can have identical impact, which is to reduce the range of wireless products. By referring to them as unintended losses, we mean that they are a consequence of poor RF layout or antenna design and were not factored into the link budget calculation, which can be used early in the design to predict the range of a wireless device.
The RF engineer can prevent these problems and their disastrous consequences by optimizing the performance critical aspects of the design before the prototypes are built, and continuing the optimization and performance assessment in the lab when the hardware is available. This is not a long and drawn out process. It is a matter of simply involving the right expertise with access to the proper design, simulation, and test and measurement tools at the right times. The end result will be a product which provides the best possible wireless performance for your customers and shareholders, with predictable cost and schedule.
Common Sources of Unintended Loss
The contributions from all sources of unintended loss are cumulative, including the separate losses of each of the 2 radios participating in a wireless link. For example, if we have 2dB of mismatch loss and the antenna gain is degraded by 2dB due to the layout, the impact of 4dB must be considered. If two such identical radios are communicating, then the total impact of 8dB must be considered.
Antenna match refers to optimizing the impedance matching network classically located close to the antenna using a piece of test equipment called a RF Vector Network Analyser. The impedance matching network is typically composed of lumped element capacitors and/or inductors, which has values that must be chosen or transmission line stubs which must be trimmed. Once the impedance matching network is tuned based on precision laboratory measurement, subsequent product may be built using the values determined. The purpose of matching the antenna is to force it to resonate over the appropriate range of frequencies for the radio, and to couple as much energy as is possible between the 50 ohm antenna and transmit/receive circuitry.
Circuit Board Layout
If the antenna is mounted on or integrated into a circuit board, careful attention must be given to the layout and the Gerber files reviewed. Often times the antenna used is really only half of the antenna capability since the circuit board RF ground plane plays a key role in the antenna performance. Without the presence of the ground plane and proper control and checking of all the geometric positioning of the antenna and the matching and feed network, the design may be destined to provide poor wireless performance before it is fabricated. The board layout team must be given detailed guidance and instruction, including the positioning of vias critical to RF performance. Simulation tools as well as theoretical knowledge as to how signals behave on circuit boards are needed to get this part of the design right.
Integration of Antenna into Operating Environment
Your end product may use more than one circuit board or contain other large conductive objects such as shielded LAN or USB connectors, transformers or discrete wires and cables. All of these can profoundly impact the performance of your antenna as can proximity to materials such as plastics and conductors. The typical use case should be evaluated, including accessories. Proximity to the human body must be considered if the device is handheld or body worn. Integration of the antenna into the product enclosure refers to evaluating the entire product design with respect to the antenna(s), retuning the impedance matching network in the final assembled product since everything mentioned above can impact antenna performance. Tuning the board used for laboratory development is often different from the final product tuning!
Quantify Impact of Loss on Wireless Range
Free Space Path Loss
Once prototype hardware is built and the wireless link functioning in the lab, the easiest part of the link budget to modify is often the physical separation between the two radios. Technically, we are changing the free space path loss (FSPL). The FSPL gets smaller (less loss) when the radios are moved closer together and vice versa. Here is a handy version of the equation for FSPL:
The distance between the two radios is d (meters), and the frequency of interest is f (Hz).
If we plot path loss vs. separation distanced, the slope of the line is 20dB/decade or 6dB/octave for any range of separation distance d. Figure 1 shows the path loss in dB for 3 different commonly encountered frequencies and a single decade of distance d in meters from 100 to 1000 meters.
Figure 1 • Path Loss Over 1 Decade of Frequency.
Loss Compensation by Range Reduction
If the RF design has unintended loss not accounted for in the link budget, without changing any other variable, we can move the two radios closer together (reduce separation distance d) until they can maintain a wireless radio link. The effect of moving the radios closer together is to compensate for unanticipated loss by reducing the free space path loss defined earlier with an equation. Through inspection of the graph or mathematical analysis of the equation, we determine an approximate rule of thumb that regardless of the source of the loss or separation distance,
Every 1dB of unanticipated loss
Reduces wireless range by 10%!
We are making a linear approximation to quantities plotted on logarithmic scales, and this approximation is reasonably accurate for the final 5dB of link budget power while investigating the maximum separation distance. For example, you expected 300 meter range but your antenna gain is 2dB low, the 2 dB translates into an approximate 20% loss of or wireless range so you measure a range of (300 meters)*(80%)=240 meters. This is a range reduction of 60 meters. If the range is 50% of what you expected, you are compensating for exactly 6dB of unintended loss.
Other Loss Compensation Techniques
Standard coping mechanisms include turning up the transmitter power to compensate for an underperforming RF design. This may appear to work well in the lab, however as we increase transmit power, we also increase the amplitude of spurious emissions and harmonics which often lead to failure when the FCC or ETSI compliance tests are performed. This is similar to stepping on the gas if you have a flat tire. You may move forward for a while, but you will get emissions that you weren’t counting on such as your tire flying apart. If you do not have timely access to RF and antenna engineering capabilities when you need it, Peak Gain Wireless is ready to help with the expertise and equipment to solve these types of problems the right way. We can prevent these problems if we are involved early in the design or define and solve the problem if hardware is already complete.
What does this all mean? Many factors impact the wireless link budget. Examples include antenna selection, design, impedance matching and final product integration. If an antenna has not been properly designed, tuned and optimized in the final product enclosure, it is not uncommon to have a total unintended loss of 2 to 6dB. Since the impact is 10% range reduction per dB loss, this translates into a 20% to 50% range reduction. These types of problems can often be predicted, understood and designed out through EM simulation or the knowledge and insight of an experienced RF engineer with access to the right tools.
About the Author:
BRL Test is committed to being your one stop shop for EMC equipment. Ophir RF amplifiers have been trusted by the EMC community since 1992. Products range in frequency from 10 kHz to 18 GHz, with power levels from 1 Watt to 24 kilowatts. Made in the USA. Multi year warranties are a testiment to their quality construction and longevity.
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The Krone-Hite 6620 provides precision phase measurements with a typical accuracy of 0.02° and a resolution of 0.01° over most of the frequency range. It will accept a wide range of input signal levels from 10mVrms to 320V rms and input waveforms including sine, triangle, square and pulses. A 5 digit LED display provides continuous direct readout of phase angles between 0.00° and 360.00° or ±180°. These two ranges can be manually or automatically selected. The 6620 employs a technique that eliminates phase reading errors usually associated with component drift called Automatic Meter Correct (AMC). AMC provides instant correction of phase readings for zero and full scale errors, making phase measurements more accurate and reliable. The Model 6620 provides a RELATIVE phase measurement mode which allows the monitoring of phase deviations without having to make unwanted calculations. Also provided are an automatic selection of input voltage range, front panel indicators to indicate a too low/high input voltage range, and an analog output for use with an external meter or strip chart recorder. Part No. RK-316 permits the installation of the Model 6620 into a standard 19″ rack spacing.
Tektronix MSO4054 Oscilloscope, 500 MHz, 4+16 CH, 5GS/s. Can Anyone beat BRL Test’s Price at $5,490? If so send us your quote so we can beat it.
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As an embedded design engineer, you are faced with the challenge of ever increasing system complexity. A typical embedded design may incorporate various analog signals, high- and lowspeed serial digital communication and microprocessor buses, just to name a few. Serial protocols such as I2C and SPI are used frequently for chip-to-chip communication, but parallel buses are still used in many applications. Microprocessors, FPGAs, Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs) are all examples of ICs that present unique measurement challenges in today’s embedded designs. The MSO4000 Series Mixed Signal Oscilloscopes offer the addition of 16 digital channels. These channels are tightly integrated into the oscilloscope’s user interface, simplifying operation and making it possible to solve mixed signal issues more easily. Next Generation Digital Waveform Display In a continued effort to make mixed signal oscilloscopes easy to use, the MSO4000 Series has redefined the way you view digital waveforms. One common problem shared by both logic analyzers and mixed signal oscilloscopes is determining if data is a one or a zero when zoomed in far enough that the digital trace stays flat all the way across the display. The MSO4000 has color-coded the digital traces, displaying ones in green and zeros in blue. The MSO4000 has multiple transition detection hardware. When the system detects multiple transitions, the user will see a white edge on the display. White edges indicate that more information is available by zooming in or acquiring at faster sampling rates. In most cases zooming in will reveal the pulse that was not viewable at the previous settings. If the white edge is still present after zooming in as far as possible, this indicates that increasing your sample rate on the next acquisition will reveal higher frequency information than your previous settings could acquire. Channel setup on an MSO can often be time-consuming as compared to the traditional oscilloscope. This process often includes probing the device-under-test, labeling the channels and positioning the channels on screen. The MSO4000 simplifies this process by allowing the user to group digital waveforms. By simply placing digital waveforms next to each other, they form a group. Once a group is formed, you can position all the channels contained in that group together. This greatly reduces the normal setup time associated with positioning channels individually
A mesh of electrodes draped over the cortex could be the future of brain-machine interfaces
By Nitish V. Thakor
Last year, an epilepsy patient awaiting brain surgery at the renowned Johns Hopkins Hospital occupied her time with an unusual activity. While doctors and neuroscientists clustered around, she repeatedly reached toward a video screen, which showed a small orange ball on a table. As she extended her hand, a robotic arm across the room also reached forward and grasped the actual orange ball on the actual table. In terms of robotics, this was nothing fancy. What made the accomplishment remarkable was that the woman was controlling the mechanical limb with her brain waves.
The experiment in that Baltimore hospital room demonstrated a new approach in brain-machine interfaces(BMIs), which measure electrical activity from the brain and use the signal to control something. BMIs come in many shapes and sizes, but they all work fundamentally the same way: They detect the tiny voltage changes in the brain that occur when neurons fire to trigger a thought or an action, and they translate those signals into digital information that is conveyed to the machine.
To sense what’s going on in the brain, some systems use electrodes that are simply attached to the scalp to record the electroencephalographic signal. These EEG systems record from broad swaths of the brain, and the signal is hard to decipher. Other BMIs require surgically implanted electrodes that penetrate the cerebral cortex to capture the activity of individual neurons. These invasive systems provide much clearer signals, but they are obviously warranted only in extreme situations where doctors need precise information. The patient in the hospital room that day was demonstrating a third strategy that offers a compromise between those two methods. The gear in her head provided good signal quality at a lower risk by contacting—but not penetrating—the brain tissue.
The patient had a mesh of electrodes inserted beneath her skull and draped over the surface of her brain. These electrodes produced anelectrocorticogram (ECoG), a record of her brain’s activity. The doctors hadn’t placed those electrodes over her cerebral cortex just to experiment with robotic arms and balls, of course. They were trying to address her recurrent epileptic seizures, which hadn’t been quelled by medication. Her physicians were preparing for a last-resort treatment: surgically removing the patch of brain tissue that was causing her seizures.
Seizures result from abnormal patterns of activity in a faulty part of the brain. If doctors can precisely locate the place where those patterns originate, they can remove the responsible brain tissue and bring the seizures under control. To prepare for this woman’s surgery, doctors had cut through her scalp, her skull, and the tough membrane called the dura mater to insert a flexible grid of electrodes on the surface of her brain. By recording the electrical activity those electrodes registered over several days, the neurologists would identify the trouble spot.
The woman went on to have a successful surgery. But before the procedure, science received a valuable bonus: the opportunity to record neural activity while the patient was conscious and under observation. Working with my collaborator at the Johns Hopkins University School of Medicine, Nathan Crone, my team of biomedical engineers has done this about a dozen times in the past few years. These recordings are increasingly being used to probe human brain function and are producing some of the most exciting data in neuroscience.
As a patient moves or speaks under carefully controlled conditions, we record the ECoG signals and learn how the brain encodes intentions and thoughts. Now we are beginning to use those signals to control computers, robots, and prostheses. The woman in the hospital room didn’t need any mind-controlled mechanical devices herself, but she was helping us develop technology that could one day allow paralyzed patients to control robotic limbs of their own.
The machine at the end of a brain-machine interface could be anything: Over the past few decades, researchers have experimented with using neural signals to control a computer cursor, a wheelchair, and even a car. The dream of building a brain-controlled prosthetic limb, however, has received particular attention.
In 2006 the U.S. Defense Advanced Research Projects Agency (DARPA) bankrolled a massive effort to build a cutting-edge prosthetic arm and to control it with brain signals. In the first phase of this Revolutionizing Prosthetics program, the Johns Hopkins Applied Physics Laboratorydeveloped a remarkable piece of machinery called the Modular Prosthetic Limb, which boasts 26 degrees of freedom through its versatile shoulder, elbow, wrist, and fingers.
To give amputees control of the mechanical arm, researchers first tried out existing systems that register the electrical activity in the muscles of the limb stump and transmit those signals to the prosthesis. But such systems offer very limited control, and amputees don’t find them intuitive to use. So DARPA issued its next Revolutionizing Prosthetics challenge in 2009, asking researchers to control the state-of-the-art prosthetic arm directly with signals from the brain.
Several investigators answered that call by using brain implants with penetrating electrodes. At Duke University, in Durham, N.C., and theUniversity of Pittsburgh, researchers had already placed microelectrodes in the brains of monkeys, using the resulting signals to make a robotic arm reachand grasp. Neuroscientists at Brown University, in Providence, R.I., had implanted similar microelectrodes in the cortex of a paralyzed man and showed that he could control a computer cursor using neural signals. Another paralyzed patient who underwent this procedure at Brown recently controlled a robotic arm: She used it to raise a bottle to her lips to take a drink, performing her first independent action in 14 years.
That work certainly demonstrated the feasibility of building a “neural prosthesis.” But using penetrating electrodes poses significant challenges. Scar tissue builds up around the electrodes and can reduce signal quality over time. Also, the hardware, including electrode arrays and low-power transmitters that send the signal out through the skull, must operate reliably for many years. Finally, these first demonstrations did not produce smooth, quick, or dexterous movements. Some neuroscientists suggested that many more electrodes should be implanted—but doing so would heighten the risk of damaging brain tissue.
In light of those concerns, the United States’ National Institutes of Healthchallenged researchers to build a neural prosthesis with a less invasive control mechanism. The ideal would be a system based on EEG signals, simply using electrodes attached to the scalp. Unfortunately, the brain signals that external electrodes pick up are blurred and attenuated by their passage through the skull and scalp. This led our team to investigate the middle road: the use of ECoG signals.
ECoG systems provide a better signal-to-noise ratio than EEG, and the data includes high-frequency components that EEG can’t easily capture. ECoG systems also do a better job of extracting the most useful information from the brain, as an electrode placed over the motor cortex can specifically listen in on the electrical activity most relevant for controlling a prosthetic arm. Similarly, electrodes draped over the brain areas associated with speech can capture signals associated with verbal communication.
Raw ECoG signals appear to be a confused mess of squiggly lines with little discernible pattern. To make sense of the data, our team performs a spectral analysis to deconstruct the signal and find oscillations at certain specific frequencies. These are the brain waves you may have heard about. Neuroscientists have learned that different oscillation frequencies are associated with specific mental states, such as deep sleep, focused attention, or meditative contemplation.
Just imagine what neural prostheses could do for people who are severely paralyzed or for patients in the late stages of amyotrophic lateral sclerosis (also known as Lou Gehrig’s disease). These patients are essentially “locked in,” with intact brains but no ability to control their bodies, or even to speak. Could their intentions, translated into ECoG signals, be captured and relayed to robotic limbs?
Here’s a simple example of how an ECoG-based neural prosthesis could work. Researchers have previously shown that an imagined movement modulates the brain’s electrical activity in the mu band, which has a frequency of about 10 to 13 hertz. Thus, the paralyzed subject would imagine moving a limb, electrodes would capture the mu-band activity, and the BMI would use the signal to trigger an action, such as closing a robotic hand. Since we can use up to 64 electrodes placed about a centimeter apart and spread over a wide area of the brain, we have a lot of data to work with. As we develop better algorithms that can identify the key signals that code for movement, we can build systems that don’t just trigger an action but offer more fine-tuned control.
Our team took the first step toward building such a system in 2011, when we painstakingly matched up brain signals with particular movements. Our subject was a 12-year-old boy awaiting surgery for his seizure disorder. In the experiment, the boy reached forward and grasped one of the wooden blocks set before him, then released it and withdrew his hand. When we looked at the data from just a few electrodes that had been placed over his brain’s sensorimotor cortex, we discovered that oscillations in the high gamma band, with frequencies between 70 and 150 Hz, correlated well with the boy’s actions. We also found that the signal in a lower frequency band changed in predictable ways when he wiggled his fingers.
The next step was to couple the electrical signal to the machinery. We demonstrated that under carefully controlled conditions, epileptic patients with ECoG electrodes placed on their brains could indeed command the Modular Prosthetic Limb to perform simple actions, like reaching and grasping. While this was a considerable accomplishment, we struggled to decode the neural signals reliably and to get the prosthetic limb to move smoothly.
In the end, we decided that it just may not be realistic to expect an ECoG-controlled prosthesis to perform an entirely natural limb movement, such as picking up a pot of coffee and pouring some into a cup. After all, a typical person uses a combination of vision, touch, motor control, and cognitive processing to perform this mundane action. So last year our team began exploring another strategy. We built a hybrid BMI that combines brain signals with input from other sensors to help accomplish the task at hand.
Several epileptic volunteers with ECoG arrays tested our novel system. The first, the woman described at the beginning of this article, focused her eyes on the image of a ball on a computer screen; the computer was streaming video from a setup across the room. An eye-tracking system recorded the direction of her gaze to locate the object she wanted to manipulate. Then, as she reached toward the screen, her ECoG electrodes recorded neural signals associated with that action. All this information was relayed to a robotic arm across the room, which was equipped with a Microsoft Kinect to help it recognize objects in three-dimensional space. When the arm received the signal to reach for the ball, its path-planning software calculated the necessary movements, orientations, and grasp configuration to smoothly pick up the ball and drop it in a trash can. The results were encouraging: In 20 out of 28 trials, this woman’s brain signals successfully triggered the robotic arm, which then completed the entire task.
Would this patient have done even better if we’d implanted electrodes in her brain rather than just draping the electrodes over the surface? Perhaps, but at a greater risk of brain trauma. Also, penetrating electrodes register only the local activity of individual cells or small clusters of neurons, whereas ECoG electrodes pick up activity across broader zones. ECoG systems may therefore be able to capture a richer picture of the brain activity taking place during the planning and execution of an action.
ECoG systems also hold the promise of being able to convey both motor and sensory signals. If a prosthetic limb has sensors that register when it touches an object, it could in principle send that sensory feedback to a patient by stimulating the brain through the ECoG electrodes. Similar stimulation is already done routinely in patients prior to epilepsy surgery in order to map the brain regions responsible for sensation. However, the sensations elicited have typically been very crude. In the future, more refined brain stimulation, using smaller electrodes and more precise activation patterns, may be able to better simulate tactile feedback. The goal is to use two-way communication between brain and prosthesis to help a user deftly control the limb.
While it might be tempting to test ECoG systems with severely paralyzed patients—the intended beneficiaries of this neuroprosthetic research—it is imperative to demonstrate that such systems can reliably restore meaningful function before exposing patients to the risks of surgery. For this reason, the clinical circumstances of patients preparing for epilepsy surgery represent an important opportunity to develop technology that will benefit a very different group of patients. We’ve found that many epilepsy patients are glad they can help others while they’re hospital-bound and under observation for the seizures that will provide guidance to their surgeons. Our hope is that these experiments will lead to a technology so clearly useful that we will feel well justified in trying it in paralyzed patients.
Thinking about the cost-benefit concerns gives a sense of déjà vu. I came to Johns Hopkins in the early 1980s, when doctors there had just implanted the first heart defibrillator in a patient. All the same doubts were aired. Was the technology too invasive? Would it be reliable? Would it provide enough benefit to justify the expense? But defibrillators rapidly proved their worth, and today more than 100,000 are implanted every year in the United States alone. The medical community may be nearing the same juncture with brain-machine interfaces, which might well be an accepted part of clinical medicine in just a couple of decades.
So far I’ve discussed the possibility of using ECoG signals to control prosthetic limbs, but there’s another fascinating possibility: Capturing these signals could also help people who have lost the ability to speak. For some people who have suffered a stroke or injury, the brain can still conceive words and generate speech commands, but the signals don’t make it to the mouth. When ECoG electrodes are placed over the language areas of the brain, including the regions that govern the muscles of speech articulation, the resulting signals presumably carry information pertaining to both language generation and the physical production of words. A speech prosthesis could decode those signals and send commands to a device that would give voice to the patient’s intended sentences.
Early research shows progress in understanding the brain’s commands to the mouth muscles. In one study, University of California researchers in San Francisco and Berkeley used an ECoG system to record activity in the motor cortex as their subjects patiently recited syllables such as “ba,” “da,” and “ga.” The resulting measurements showed distinct patterns for different consonants. For example, certain electrodes showed activity during the production of the “b” sound, which requires closure of the lips. Other electrodes registered activity during the “d” sound, in which the tongue hits the roof of the mouth. Still others saw action during the “g” sound, which involves the back of the mouth.
What would it take to build a speech prosthesis? First, we would need to improve our recording hardware. Today’s ECoG systems use only a few dozen electrodes on the cortex; clearly, a much higher density of electrodes would produce a better signal. We have already tried out new microelectrode ECoG systems in human patients that can pack 16 electrodes onto a 9-by-9-millimeter array.
Because speech production surely involves many brain regions, we’ll also have to improve our signal analysis to decipher neural activity, not just in one area but across large regions of the brain. We’ll need better spatial and temporal resolution to determine the exact sequence in which groups of neurons across the cortex fire to produce, say, the simple sound “ba.” Once we’ve managed to map individual phonemes or syllables, we can work toward understanding fluid speech by decoding a succession of brain commands.
Controlling a robot with a thought, speaking without making a sound: With ECoG systems, these magical feats now appear well within the realm of feasibility. By casting a net of electrodes over the surface of the brain, it’s possible to capture echoes of the ideas and commands that swirl below, in the currents of the mind.
This article originally appeared in print as “Catching Brain Waves in a Net.”
About the Author
Nitish V. Thakor, director of the Laboratory for Neuroengineering at Johns Hopkins University, embraces the fundamental scientific challenge of mapping electrical activity in the human brain. But what gets him out of bed in the morning, he says, are the clinical applications. He hopes the brain-machine interfaces he’s developing in collaboration with colleagues at the Hopkins School of Medicine will one day let paralyzed patients control robotic limbs with their brainwaves.
Before silicon got its own valley,this mild-mannered element had to vanquish many other contenders to prove itself the premier semiconductor technology. It did so in the 1950s and 1960s. Today, carbon is poised at a similar crossroads, with carbon-based technologies on the verge of transforming computing and boosting battery-storage capacities. Already, researchers have used these technologies to demonstrate paper-thin batteries, unbreakable touch screens, and terabit-speed wirelesscommunications. And on the farther horizon they envision such carbon-enabled wonders as space elevators,filters that can make seawater drinkable, bionic organs, and transplantable neurons.
Whatever miracles emerge from Carbon Valley, its carbon-tech titans will surely think fondly upon their field’s founding mother, Mildred Dresselhaus. This MIT professor of physics and engineering has, since the early 1960s, been laying the groundwork for networks of nanometer-scale carbon sheets, lattices, wires, and switches. Future engineers will turn these things, fabricated from carbon-based materials such as graphene, into the systems that will carry computing into its next era.
Now, after a half century of quiet work, she is accumulating accolades. This past November, in a ceremony at the White House, President Obama awarded her the Presidential Medal of Freedom, the U.S. government’s highest civilian honor. “Her influence is all around us, in the cars we drive, the energy we generate, the electronic devices that power our lives,” Obama said.
And this June, the IEEE will confer upon Dresselhaus its highest accolade, the IEEE Medal of Honor, for her “leadership and contributions across many fields of science and engineering.” She is the first female Medal of Honor recipient in the award’s nearly century-long history. (Before the IEEE’s formation, the Medal of Honor was presented by the Institute of Radio Engineers, which merged with the American Institute of Electrical Engineers in 1963 to form the IEEE.)
While Dresselhaus has blazed a path for researchers eager to exploit the magic of carbon computing, for most of her 84 years her own pathway has been anything but obvious. It was muddled by a world that had trouble accommodating a visionary engineering researcher who was also a caring and thoughtful mentor—as well as a mother of four (and today a grandmother of five).
The daughter of destitute Eastern European émigrés, a product of Great Depression and World War II–era New York City schools and their melting-pot culture, Dresselhaus (née Spiewak) as a child imagined that the only career open to her was that of schoolteacher. Even that was a bit of a stretch, given the time and place: The kids in her neighborhood and in her struggling primary school in the Bronx were mostly uninterested in their studies. But a mysterious force soon intervened. It was music.
Both her grandfather and great-grandfather served as town cantors in her father’s ancestral village of Dzialoszyce, Poland. So when her older brother, Irving, began playing the violin with uncommon grace at age 4, his gift wasn’t a complete surprise. Their parents secured a scholarship for him at New York City’s prestigious Greenwich House music school. And when Mildred was herself 4 or 5, she began studying music there, too. Although she stopped taking lessons at Greenwich House at 13, she has never abandoned her beloved violin. Dresselhaus still plays every day. “I had his hand-me-down violin,” she says. “I inherited all the things he left behind.”
And it was music that brought her into contact with more ambitious peers at the Greenwich House school. “It was obvious—education was important,” she says she realized not long after arriving at the school, in 1934 or ’35. “That was the most important lifelong thing I learned at the music school.”
She would probably have again followed her brother’s footsteps several years later, into the legendary Bronx High School of Science, but in those days Bronx Science was for boys only. So she set her sights on Hunter College High School, a New York City preparatory school for girls. While studying for her entrance exam, she discovered to her delight how easily math came to her. “My interest was inspired by studying—by myself and motivated by myself—math for the entrance exam to Hunter High,” she says.
At Hunter, she did so well in math and science that a poem in Dresselhaus’s senior yearbook pays tribute to her abilities: “Any equation she can solve / Every problem she can resolve / Mildred equals brains plus fun / In math and science, she’s second to none.” She went on to study at Hunter College, where, during her second year, another important force entered into her life.
“Rosalyn Yalow’s [physics] course got me more into focusing on the science profession,” Dresselhaus says of the course she loved most at Hunter, which was taught by a medical physicist who would soon herself decamp for a research career and ultimately share the 1977 Nobel Prize in Physiology or Medicine. “That’s where I really got started. And Rosalyn insisted that I go to graduate school. She was a person who used to tell you what you were doing.”
Bolstered by Yalow’s effusive letters of recommendation, Dresselhaus was admitted to Radcliffe College in 1951 for graduate studies, an admission deferred so that she could attend the University of Cambridge on a Fulbright fellowship.
“Radcliffe had no [science] classes,” Dresselhaus explains. “The classes were at Harvard. But the exams were at Radcliffe. Women didn’t take their exams with the men. I had to take my exams by myself in a different room. It was a very complex situation and not a very comfortable one.”
During her first year at Harvard, Dresselhaus realized she was growing weary of the university and a bit restless. She’d discovered that the best place in the country to study physics was at the University of Chicago, home to Manhattan Project veteran and Nobel laureate Enrico Fermi. So in 1953, after finishing her master’s degree at Radcliffe, she was off to Illinois.
At Chicago, too, Dresselhaus was often the only woman in her classes. But the learning environment wasn’t as stifling. And it was at Chicago, she says, where she first really began to learn to think like a physicist, thanks to Fermi himself. Although by then famous for his role in the Manhattan Project, Fermi headed up a small and intimate physics department. In Dresselhaus’s incoming class in 1953, for instance, there were just 11 physics students.
Fermi was an early riser, as was Dresselhaus, and they lived along the same walking route to campus. So she, along with other students, faculty, and acolytes, timed their morning commute so they could stroll along with the legendary physicist.
“He was a methodical guy; he always did the same thing every day,” Dresselhaus says. On the morning walks, for example, Fermi would talk about the issues on his mind—sometimes related to the day’s lecture, sometimes not. And when Fermi gave his talks, he’d first hand the class copies of his notes. “He didn’t want people taking notes while he [lectured]. He wanted people to listen. He’d give you the notes. The lecture [notes] didn’t have many pages. Very concise.”
Fermi, who died in November 1954, during Dresselhaus’s second year at Chicago, still had an outsized influence on the young woman during her brief time in his orbit. “He developed in me the mind-set that we should be interested in everything,” she says, “because we never know where the next big breakthrough in science will occur.”
In the fall of 1955, Dresselhaus began her Ph.D. project, investigating the microwave properties of a superconductor in a magnetic field. The novel and hybrid nature of her investigation—involving low-temperature and solid-state physics, electrical engineering, and materials science—meant she couldn’t just order the parts for her research out of a catalog.
She found much of what she needed, though, under the university’s football stands, where more than a dozen years before, Fermi had led a group that had created the world’s first man-made nuclear-fission chain reaction. There, a mountain of surplus equipment was free for the taking. Repurposing a warehouse worth of materials, she grew superconducting wire for her experiments, built microwave equipment, and even produced liquid helium.
Dresselhaus says she’d developed that kind of gumption because her primary school teachers were terrible. “They were sufficiently bad that if you wanted to learn something, you taught yourself,” she says. “That was terrific training.”
While at Chicago, she met her future husband, fellow graduate student Gene Dresselhaus. They married in May 1958 and moved to Ithaca, N.Y., where she was a National Science Foundation postdoctoral fellow and Gene had an entry-level faculty position in the physics department at Cornell University. There Dresselhaus also met another celebrity scientist, albeit one whose great fame would come years later—Richard Feynman. At the time, Feynman was developing the equations that would become the quantum theory of electrodynamics.
“He gave a lecture now and then,” she says. “And if there’s a Feynman lecture, you go to it. It’s always interesting, looking at things you’ve heard about before but from a totally different perspective.”
Also in 1959, the Dresselhauses welcomed their first child, Marianne. And despite the stimulating Feynman lectures Dresselhaus occasionally attended, Cornell wasn’t exactly a female academic’s dream in those days. Early on, a faculty member told her point blank that no woman would ever be permitted to lecture to his engineering students.
So in 1960 the two Dresselhauses went to MIT’s Lincoln Laboratory. There she moved out of superconductors, her thesis topic, and began looking instead at magnetic and optical properties of graphite, bismuth, and other so-called semimetals. This field, she says, wasn’t popular or very competitive at the time, which gave her the latitude she needed to have four children (one daughter and three sons) through 1964. As a working mother, however, she encountered some bumps in her career progress.
One Lincoln Lab colleague,H. Eugene Stanley (now a professor of physics, chemistry, biomedical engineering, and physiology at Boston University) recalls the day after Dresselhaus delivered her youngest child, Eliot, in 1964.
“When she had her fourth kid,” recalls Stanley, “she brought him to work the day after he was born. She was there around noon or 1 o’clock with the baby in tow. But because Lincoln Lab was a government lab, you either had to have clearance or have a badge. They wouldn’t let the kid in. She was furious! I didn’t see her angry that often, but I saw her angry that day.”
Dresselhaus crossed from Lincoln Lab to parent institution MIT in 1967, accepting a visiting professorship in electrical engineering, a position that became permanent the following year. She added a joint appointment in physics in 1983.
“When I first came to MIT, the [physics] department was only interested in high-energy physics,” she says of a field that was then consumed with colliding subatomic particles at ever-higher energies. She adds that more quotidian fields of physics, from materials science to engineering physics, were on the back burner at the time. “It’s all totally different now.… There’s a big shortage of people [who] have a physics background and engineering also.”
On a snowy day in the middle of one of Cambridge’s harshest winters ever, Dresselhaus holds forth in her MIT office on her favorite topic. “Consider a simple sheet of carbon atoms, also known as graphite,” she begins.
She pulls down a well-worn ball-and-stick-model from atop one of her cabinets. “Carbon’s crystal structure is such that the in-plane force is the strongest in nature,” she says. “But across the plane it’s very weak. So it allows separation of layers very easily.”
A pencil’s graphite flakes off easily without disintegrating, and yet it can still cling to rough, fibrous surfaces like paper. Individual sheets of graphite, in other words, are as tough as diamond. But as a group they’re as flaky as phyllo dough.
Throughout the 1960s, 1970s, and 1980s, Dresselhaus and her graduate students investigated the properties of both graphite and carbon intercalation compounds—that is, sheets of graphite sandwiching individual bromine or potassium atoms, which were captured like olives between slices of bread. Her group also laid the foundation for the discovery and exploitation of nanotechnological wonder materials, such as the tiny carbon spheres known as buckminsterfullerenes, the cylindrical carbon pipes called nanotubes, and the single-atom-thick sheets of carbon called graphene.
Variations or combinations of these carbon structures could yield body armor stronger than Kevlar, ultrathin membranes with pores small enough to filter the salt from seawater, and even bionic implants that can give new hope to those with serious spinal-cord or organ damage. Used as electrodes in batteries or capacitors,graphene and nanotubes offer promise as a kind of ultimate energy storage system. Their charge capacities would exceed those of traditional batteries, and their charge times (on, say, an electric-vehicle battery) would be shorter than the time it takes to pump a tankful of gasoline.
And as a possible substrate for next-generation electronics, graphene has few competitors today. Its high conductivity (better than silver’s) and its single-atom thickness make robust, molecule-size graphene circuit components boasting terahertz computing speeds a tantalizing if far-future possibility. “Graphene is not going to replace silicon; it’s going to do different things,” Dresselhaus says.
Although well into her 80s, Dresselhaus is at her MIT office every day, including weekends and holidays, often as early as 6 a.m. Her enthusiasm for her work, which these days includes studying optical, electric, and vibrational properties of graphene, carbon nanotubes, and other nanomaterials seems undiminished. “I am excited by my present research and am not yet anxious to stop working,” she says, simply.
As she has for much of her MIT career, Dresselhaus also mentors young people, especially women starting careers in STEM. She has supervised the theses of more than 60 doctoral students and shepherded many more colleagues and associates through career transitions and inflection points.
“One time at MIT, she told me she was working with this great [Ph.D.] student named Shirley Ann Jackson,” says Laura Roth, Dresselhaus’s former colleague at Harvard and Lincoln Lab. “And now she’s president of Rensselaer Polytechnic Institute.” (Jackson has herself earned 52 honorary degrees and been called by Time magazine “perhaps the ultimate role model for women in science.”)
Says Gang Chen, head of the mechanical engineering department at MIT, “Four women from my own group…benefitted from Millie’s support during their stay at MIT. On several occasions, Millie volunteered to talk to my female students, giving them individual career advice.
“On one hand, it seems to be quite late for the first woman to receive the IEEE Medal of Honor,” Chen adds. “On the other hand, no one is more fitting than Millie, and she has set a truly high bar. I am sure Millie’s receiving this honor will inspire more women in IEEE to strike high.”
This article originally appeared in print as “The Queen of Carbon.”
About the Author
An IEEE Spectrum contributing editor, Mark Anderson has covered advances in carbon nanotechnology for us and other publications. In profiling the field’s doyenne, he found 84-year-old Mildred Dresselhaus’s seven-day-a-week work ethic a true inspiration. “I arrived at her MIT office on the morning of a snowstorm to do the interview,” he recalls, “and she was ready to go.”
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