Photonic Compact Chip That Seamlessly Converts Light Into Microwaves (NIST, et al.)


A new technical paper titled "Photonic chip-based low-noise microwave oscillator" was published by researchers at NIST, University of Colorado Boulder, California Institute of Technology, UCSB, University of Virginia and Yale University. Abstract "Numerous modern technologies are reliant on the low-phase noise and exquisite timing stability of microwave signals. Substantial progress has b... » read more

Designing Low Power Radar Processors


A technical paper titled “Ellora: Exploring Low-Power OFDM-based Radar Processors using Approximate Computing” was published by researchers at University of California Irvine, University of Wisconsin-Madison, and TCS Research. Abstract: "In recent times, orthogonal frequency-division multiplexing (OFDM)-based radar has gained wide acceptance given its applicability in joint radar-communic... » read more

Radar Transceivers: How To Connect The Antennas


There are different antenna options for transmitting and receiving a radar signal. This blog will focus on how to connect a monolithic microwave integrated circuit (MMIC) radar transceiver to the antennas in a way that guarantees an efficient transfer of the signals. mmWave MMIC to antenna interfaces At millimeter-wave (mmWave) frequencies, any transitions between two different transmission l... » read more

New Approaches To Sensors And Sensing


Sensors are becoming more intelligent, more complex, and much more useful. They are being integrated with other sensors in sensor fusion, so a smart doorbell may only wake up when it’s imperative to see who’s at the door, and a microphone may only send alerts when there are cries for help or sounds of glass breaking. Kim Lee, senior director of system applications engineering at Infineon, t... » read more

How Many Sensors For Autonomous Driving?


With the cost of sensors ranging from $15 to $1,000, carmakers are beginning to question how many sensors are needed for vehicles to be fully autonomous at least part of the time. Those sensors are used to collect data about the surrounding environment, and they include image, lidar, radar, ultrasonic, and thermal sensors. One type of sensor is not sufficient, because each has its limitation... » read more

Designing Crash-Proof Autonomous Vehicles


Autonomous vehicles keep crashing into things, even though ADAS technology promises to make driving safer because machines can think and react faster than human drivers. Humans rely on seeing and hearing to assess driving conditions. When drivers detect objects in front of the vehicle, the automatic reaction is to slam on the brakes or swerve to avoid them. Quite often drivers cannot react q... » read more

What’s At Stake In System Design?


What You Will Gain From This eBook: Power and Signal Integrity Insights into harmonic balancing and crosstalk analysis Learning about loop gain and transmission rates Examining the necessity of power-aware systems Electromagnetic Analysis Knowledge about the state of electromagnetics in wireless networks Insight into RADAR and LiDAR EM profiles Tips for bending, meshin... » read more

Week In Review: Auto, Security, Pervasive Computing


Automotive Ambarella will use Samsung's 5nm process technology for its new CV3-AD685 automotive AI central domain controller, bringing "new levels of AI acceleration, system integration and power efficiency to ADAS and L2+ through L4 autonomous vehicles.” Renesas introduced four technologies for automotive communication gateway SoCs: (1) an architecture that dynamically changes... » read more

Research Bits: Feb. 21


High-quality ‘chirps’ for automotive, industrial mmWave radar Imec demonstrated a low-power phase-locked loop (PLL) that generates high-quality frequency-modulated continuous-wave (FMCW) signals for mmWave radar, which can be used in short-range automotive and industrial radar applications. The FMCW radars popular in healthcare, automotive, and industrial send out sinusoidal waves that get... » read more

Simulating Reality: The Importance Of Synthetic Data In AI/ML Systems For Radar Applications


Artificial intelligence and machine learning (AI/ML) are driving the development of next-generation radar perception. However, these AI/ML-based perception models require enough data to learn patterns and relationships to make accurate predictions on new, unseen data and scenarios. In the field of radar applications, the data used to train these models is often collected from real-world meas... » read more

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