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Analog charge-coupled devices and sampled data filtering have developed into mature disciplines. Each discipline offers the other wide systems advantages where alone it would be impractical. This letter explores these advantages in a number of implementations against the scale of wide low-cost applications.
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Technique monitors structural flaws in 105-in. diameter tanks. Tank surface is divided into many areas and each area is sectioned into 20 equilateral triangles that form icosahedron. Twelve transducers are equally positioned on tank surface at vertex of each triangle. Transducers monitor area for flaws by detecting any increase in acoustical activity.
The design of MOS multipliers with CAD was studied. In particular the Fourier analysis capability of the CAD program was used to analyze the harmonic distortion rejection, DR, of the multiplier.
Logarithmic amplifier used in data processing of analog signals
Here, we present a 16-channel front-end readout board for the OpenPET electronics system. A major task in developing a nuclear medical imaging system, such as a positron emission computed tomograph (PET) or a single-photon emission computed tomograph (SPECT), is the electronics system. While there are a wide variety of detector and camera design concepts, the relatively simple nature of the acquired data allows for a common set of electronics requirements that can be met by a flexible, scalable, and high-performance OpenPET electronics system. The analog signals from the different types of detectors used in medical imaging share similar characteristics, which allows for a common analog signal processing. The OpenPET electronics processes the analog signals with Detector Boards. Here we report on the development of a 16-channel Detector Board. Each signal is digitized by a continuously sampled analog-to-digital converter (ADC), which is processed by a field programmable gate array (FPGA) to extract pulse height information. A leading edge discriminator creates a timing edge that is "time stamped" by a time-to-digital converter (TDC) implemented inside the FPGA. In conclusion, this digital information from each channel is sent to an FPGA that services 16 analog channels, and then information from multiple channels is processed by this FPGA to perform logic for crystal lookup, DOI calculation, calibration, etc.
Described herein are various technologies relating to processing a coherent signal. A receiver is configured to receive an analog signal, and process the analog signal to generate an input analog signal. The input analog signal has been modulated according to a suitable modulation sequence. A coherent signal sampler coherently samples the modulation sequence, and the result of such sampling is in turn used to demodulate the input analog signal. The resultant signal is then passed to a sigma delta modulator, where it is converted to digital form.
Analog modulation, frequency-division multiplexing and signal processing for multiple-access satellite communication
The paper reports the development and initial testing of a digital resolver to replace existing analog signal processing instrumentation. Radiometers, mounted directly on one of the fully articulated blades, are electrically connected through a slip ring to analog signal processing circuitry. The measured signals are periodic with azimuth angle and are resolved into harmonic components, with 0 deg over the tail. The periodic nature of the helicopter blade motion restricts the frequency content of each flapping and yaw signal to the fundamental and harmonics of the rotor rotational frequency. A minicomputer is employed to collect these data and then plot them graphically in real time. With this and other information generated by the instrumentation, a helicopter test pilot can then adjust the helicopter model's controls to achieve the desired aerodynamic test conditions.
Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged digital quantum resources, such as discrete-variable (DV) systems like qubits, rather than analog quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals—specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.
This paper describes ongoing research and development of CMOS active pixel image sensors for low cost commercial applications. A number of sensor designs have been fabricated and tested in both p-well and n-well technologies. Major elements in the development of the sensor include on-chip analog signal processing circuits for the reduction of fixed pattern noise, on-chip timing and control circuits and on-chip analog-to-digital conversion (ADC). Recent results and continuing efforts in these areas will be presented.
A high-speed edge-detecting line scan smart camera was developed. The camera is designed to operate as a component in a NASA Glenn Research Center developed inlet shock detection system. The inlet shock is detected by projecting a laser sheet through the airflow. The shock within the airflow is the densest part and refracts the laser sheet the most in its vicinity, leaving a dark spot or shadowgraph. These spots show up as a dip or negative peak within the pixel intensity profile of an image of the projected laser sheet. The smart camera acquires and processes in real-time the linear image containing the shock shadowgraph and outputting the shock location. Previously a high-speed camera and personal computer would perform the image capture and processing to determine the shock location. This innovation consists of a linear image sensor, analog signal processing circuit, and a digital circuit that provides a numerical digital output of the shock or negative edge location. The smart camera is capable of capturing and processing linear images at over 1,000 frames per second. The edges are identified as numeric pixel values within the linear array of pixels, and the edge location information can be sent out from the circuit in a variety of ways, such as by using a microcontroller and onboard or external digital interface to include serial data such as RS-232/485, USB, Ethernet, or CAN BUS; parallel digital data; or an analog signal. The smart camera system can be integrated into a small package with a relatively small number of parts, reducing size and increasing reliability over the previous imaging system..
Small, lightweight, low-power magnetometer measures three-dimensional magnetic field. Includes three toroidal cores - one for each dimension. Exhibits high sensitivity, low zero-point drift, and low noise. Magnetometer circuit includes driver circuit and three analog signal-processing circuits. Output of analog signal-processing circuit proportional to one of components of external magnetic field.
This final report describes the accomplishments of the General Purpose Intelligent Sensor Interface task of the Applications of Artificial Intelligence to Space Station grant for the period from October 1, 1987 through September 30, 1988. Portions of the First Biannual Report not revised will not be included but only referenced. The goal is to develop an intelligent sensor system that will simplify the design and development of expert systems using sensors of the physical phenomena as a source of data. This research will concentrate on the integration of image processing sensors and voice processing sensors with a computer designed for expert system development. The result of this research will be the design and documentation of a system in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, television camera selection, voice recognition hardware selection, or analog signal processing. The user will be able to access data from video or voice sensors through standard LISP statements without any need to know about the sensor hardware or software.
We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 µm\textsuperscript{2} in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e\textsuperscript{-} and a total dispersion of $\sim$100e\textsuperscript{-} The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4\% – 75.4\% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm\textsuperscript{2} staying within the experimental constraints.
We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 $\mu$m$^2$ in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e$^-$ and a total dispersion of $\sim$100e$^-$ The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4% - 75.4% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm$^2$ staying within the experimental constraints.
Digital signal processing techniques were employed to investigate the joint use of charge division and risetime analyses for the resistive anode (RA) coupled to a microchannel plate detector (MCP). In contrast to the typical approach of using the relative charge at each corner of the RA, this joint approach results in a significantly improved position resolution. A conventional charge division analysis utilizing analog signal processing provides a measured position resolution of 170 μm (FWHM). By using the correlation between risetime and position we were able to obtain a measured resolution of 92μm (FWHM), corresponding to an intrinsic resolution of 64μm (FWHM) for a single Z-stack MCP detector.
Typically an analog signal from a space system is sampled, quantized by Analog-to-Digital (A/D) conversion, merged into a bit stream, communicated to a ground station, received by the ground station, and processed by the ground station to extract useful information for dissemination to the users. The cost of each of these steps is reduced as the number of quantization steps is reduced in the A/D converter. The number of quantization steps should be as small as possible without losing the required information content. This report deals specifically with the accuracy of averages as a function of the number of quantized samples used to compute the averages with the noise on the analog signal as a parameter. For example, the success of the Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) Demonstration depends upon temporally averaging multiple samples in an effort to reduce noise to a sufficiently low level such that temperature profile sounding is made possible. A tutorial description of this process is presented.