Timing Signal Generator for High Frequency Radar

This paper presents the design and implementation of a new digital-to-time converter (DTC). The obtained resolution is 1.02 ps, and the dynamic range is about 590 ns. The experimental results indicate that the measured differential nonlinearity (DNL) and integral nonlinearity (INL) are −0.17~+0.13 LSB and −0.35~+0.62 LSB, respectively. This DTC builds coarse and fine Vernier delay lines constructed by programmable delay lines (PDLs) to ensure high performance delay. Benefited by the close-loop feedback mechanism of the PDLs’ control module, the presented DTC has excellent voltage and temperature stability. What is more, the proposed DTC can be implemented in a single field programmable gate array (FPGA) chip.

The goal of this paper is to implement the secretion mechanism of the Thyroid Hormone (TH) based on bio-mathematical differential eqs. (DE) on an FPGA chip. Hardware Descriptive Language (HDL) is used to develop a behavioral model of the mechanism derived from the DE. The Thyroid Hormone secretion mechanism is simulated with the interaction of the related stimulating and inhibiting hormones. Synthesis of the simulation is done with the aid of CAD tools and downloaded on a Field Programmable Gate Arrays (FPGAs) Chip. The chip output shows identical behavior to that of the designed algorithm through simulation. It is concluded that the chip mimics the Thyroid Hormone secretion mechanism. The chip, operating in real-time, is computer-independent stand-alone system.

Various modulation techniques are usually utilized to enhance the radar performance, or to add more capabilities to the radar that otherwise would not have been possible. Digital generation methods of frequency modulated continuous wave signals include direct waveform synthesis and frequency synthesis. The implementation of waveform generation and upconversion method are described in this paper with a simplified digital implementation technique using FPGAs (Field Programmable Gate Array). The design follows a systematic approach following steps of timing generation from external encoder, baseband modulated waveform generation, upconversion of the waveform to intermediate frequency, filtering and finally frequency synthesis using external DDS (Direct Digital Synthesizer). The design is also capable of generating BITE waveform for test checking in the system. Our design is capable of generation of waveform for stationary as well as non-stationary radars. For non-stationary radars, the waveform is so generated that the doppler effect due to the movement of the radar is compensated. The paper explains more on this. This work mainly focuses on the fact that the total design has been implemented on a single FPGA and the implementation solution utilizes the efficient FPGA resources so as to meet the timings in crucial applications. Moreover, the paper enhances the features of the design with simulation and real time results used to validate the design.

Regular Expressions (RegExes) are widely used in various applications to identify strings of text. Their flexibility, however, increases the complexity of the detection system and often limits the detection speed as well as the total number of RegExes that can be detected using limited resources. The two classical detection methods, Deterministic Finite Automaton (DFA) and Non-Deterministic Finite Automaton (NFA), have the potential problems of prohibitively large memory requirements and a large number of concurrent operations, respectively. Although recent schemes addressing these problems to improve DFA and NFA are promising, they are inherently limited by their scalability, since they follow the state transition model in DFA and NFA, where the state transitions occur per each character of the input. We recently proposed a scalable RegEx detection system called Lookahead Finite Automata (LaFA) to solve these problems with three novel ideas: 1. Provide specialized and optimized detection modules to increase resource utilizations. 2. Systematically reordering the RegEx detection sequence to reduce number of concurrent operations. 3. Sharing states among automata for different RegExes to reduce resource requirements. In this paper, we propose an efficient hardware architecture and prototype design implementation based on LaFA. Our proof-of-concept prototype design is built on a fraction of a single commodity Field Programmable Gate Array (FPGA) chip and can accommodate up to twenty-five thousand (25k) RegExes. Using only 7% of the logic area and 25% of the memory on a Xilinx Virtex-4 FX100, the prototype design can achieve 2-Gbps (gigabits-per-second) detection throughput with only one detection engine. We estimate that 34-Gbps detection throughput can be achieved if the entire resources of a state-of-the-art FPGA chip are used to implement multiple detection engines.

Face detection has been widely used in many fields such as security, intelligent human interface etc., and concerns on how to meet the criterion of miniature and real-time have gradually become independent research topics. Traditionally, systems of face detection/recognition are either implemented in very specific hardware or software system. This paper tailors an algorithm of face detection using skin color and presents an efficient implementation on a single Field Programmable Gate Array (FPGA) chip. The implementation exploits the inherit parallelism of hardware and takes advantages of hardware multiply-accumulate units and shift register(RAM-Based) units on FPGA. The entire system uses a single FPGA coupled to a pair of SDRAM chips, and no extra parts. The system was implemented and tested with face detection on a 1280×1024 frame taking 16.66 ms (60 frames per second ). This system can be used as a reference for lightweight embedded vision systems.

In this paper, we introduce a new development system for creating real-time image processing hardware using custom computing machines with multiple Field Programmable Gate Array (FPGA) chips. Three distinct processes are accomplished within the development system: design entry, verification, and translation. A library of modules that implement common low-level machine vision functions is used to create complex designs based on a dataflow graph representation. The library's low-level image processing modules contain both gate-level and chip-level hardware components, of which the gate-level components are compiled into the functionality of available FPGA chips. Standard interfaces are established for input/output of the modules, allowing for the creation of sophisticated software support tools. Experimental results verify the utility of this development system for easily creating real-time machine vision hardware using multiple FPGA-based custom computing machines.

Raman spectrometer has been widely used as an identification tool for analyzing material structure and composition in many fields. However, Raman scattering echo signal is very weak, about dozens of photons at most in one laser plus signal. Therefore, it is a great challenge to design a Raman spectrum data acquisition system which could accurately receive the weak echo signal. The system designed in this paper receives optical signals with the principle of photon counter and could detect single photon. The whole system consists of a photoelectric conversion module H7421-40 and a photo counting card including a field programmable gate array (FPGA) chip and a PCI9054 chip. The module H7421-40 including a PMT, an amplifier and a discriminator has high sensitivity on wavelength from 300nm to 720nm. The Center Wavelength is 580nm which is close to the excitation wavelength (532nm), QE 40% at peak wavelength, Count Sensitivity is 7.8*10⁵(S^-1PW^-1) and Count Linearity is 1.5MHZ. In FPGA chip, the functions are divided into three parts: parameter setting module, controlling module, data collection and storage module. All the commands, parameters and data are transmitted between FPGA and computer by PCI9054 chip through the PCI interface. The result of experiment shows that the Raman spectrum data acquisition system is reasonable and efficient. There are three primary advantages of the data acquisition system: the first one is the high sensitivity with single photon detection capability; the second one is the high integrated level which means all the operation could be done by the photo counting card; and the last one is the high expansion ability because of the smart reconfigurability of FPGA chip.

Building the machines that advance scientific knowledge is one of the most significant challenges of our time. So-called Big Science instruments are unique; the expertise required to build them is multidisciplinary, and few countries can build and maintain them. Since resources and budgets are not unlimited, these require efficient solutions. Big Science machines take a decade to plan, a decade to build, and at least a decade to operate. One such machine is the largest fusion reactor project, ITER, which promises to be the next step in fusion research and has a big challenge in system integration. To mitigate the integration effort, ITER has invested in standardization, both in software and hardware, introducing standard instrumentation software named Nominal Device Support (NDSv3); Micro Telecommunication Computing Architecture (MTCA) as the long-term hardware standard choice, and EPICS as a general-purpose distributed control system. These control systems are essential in the management of the facility. They provide functions such as communication protocols, process management, monitoring, alarms, and human-machine interfaces. But also, more advanced access to databases or data acquisition systems. The latter is typically managed by a Field Programmable Gate Array (FPGA), of which facilities can easily have thousands of these FPGAs managing millions of signals within the control system environment. Nevertheless, these FPGA data acquisition (DAQ) systems continue to be labor-intensive. Standardized, modular, and open-source frameworks have been proposed to solve this problem, however, the FPGA programming expertise requirement remained. This thesis proposes a complete technology stack that considers these DAQ systems from the control system to the hardware. It provides a methodology using the Open Computing Language (OpenCL) to reduce the FPGA programming effort. The FPGA devices are programmed using C/C++-like environments, which are much simpler than traditional Hardware Description Languages (HDLs). The software, named IRIO-OpenCL, is written in C++ following ITER's NDSv3 model, which abstracts and generalizes the development of software device drivers and simplifies the interface to EPICS. IRIO-OpenCL is specifically designed to simplify the integration of intelligent instrumentation systems, allowing the integration of algorithms into the FPGA without modifying large parts of the supporting library. The MTCA standard was chosen to maintain modularity. The chassis backplane connects to the devices via Ethernet and PCIe, enabling standard communications. The Advanced Mezzanine Card is the typical MTCA FPGA-based DAQ carrier board. It connects the FPGA to the chassis backplane and typically provides FPGA Mezzanine Connectors (FMCs) for plugging in analog-to-digital (ADC) carrier boards. An ADC connected via the FMC interface samples the analog signals passed to the OpenCL processing kernels. Using OpenCL, the FPGA kernels communicate with the host machine in a standardized way. To demonstrate the applicability of this methodology, reference problems were selected from the literature and implemented, both with traditional and deep learning (DL) solutions. The methodology elaborates on connecting the OpenCL kernels for any use case, and the results show examples of kernel sets. The neutron flux measurement problem is solved at 1GS/s in real time using pulse counting and Campbell techniques, using 40% of the target Arria10 FPGA. The DL application example is based on a neutron/gamma discrimination neural network (NN) that achieves a classification rate of up to 1.3 MEvents/s using less than 25% of the target resource. RESUMEN Construir máquinas que avancen el conocimiento científico es uno de los desafíos más significativos de nuestro tiempo. Los instrumentos de la llamada Big Science son únicos; las competencias requeridas para su construcción son multidisciplinares, y pocos países pueden construirlos y mantenerlos. Dado que los recursos y presupuestos no son ilimitados, estos requieren soluciones eficientes. Las máquinas de Gran Ciencia tardan una década en planificarse, una década en construirse y al menos una década en operarse. Una de estas máquinas es el proyecto del reactor de fusión más grande, ITER, que promete ser el siguiente paso en la investigación de fusión y presenta un gran desafío en la integración de sistemas. Para mitigar el esfuerzo de integración, ITER ha invertido en estandarización, tanto en software como en hardware, introduciendo un software de instrumentación estándar llamado Nominal Device Support v3 (NDSv3); Micro Telecommunication Computing Architecture (MTCA) como la elección estándar de hardware a largo plazo, y EPICS como un sistema de control distribuido de propósito general. Estos sistemas de control son esenciales en la gestión de la instalación ya que proporcionan funciones como protocolos de comunicación, gestión de procesos, monitorización, alarmas e interfaces de usuario, además de acceso avanzado a bases de datos o sistemas de adquisición de datos. Este último es típicamente gestionado por un Field Programmable Gate Array (FPGA), de los cuales las instalaciones pueden tener fácilmente miles de unidades gestionando millones de señales dentro del entorno del sistema de control. Estos sistemas de adquisición de datos (DAQ) con FPGA siguen requiriendo programación experta y laboriosa. Se han propuesto marcos estandarizados, modulares y de código abierto para resolver este problema, sin embargo, la necesidad de experiencia en programación FPGA no se ha podido eludir. Esta tesis propone una pila de tecnologías que considera estos sistemas DAQ desde el sistema de control hasta el hardware. Proporciona una metodología utilizando el Open Computing Language (OpenCL) para reducir el esfuerzo de programación FPGA. Los dispositivos FPGA se programan utilizando entornos similares a C/C++, que son mucho más simples que los Lenguajes de Descripción de Hardware (HDL) tradicionales. El software, llamado IRIO-OpenCL, está escrito en C++ siguiendo el modelo de NDSv3 de ITER, que abstrae y generaliza el desarrollo de controladores de dispositivos de software y simplifica la interfaz con EPICS. IRIO-OpenCL está específicamente diseñado para simplificar la integración de sistemas de instrumentación inteligentes, permitiendo la integración de algoritmos en el FPGA sin modificar grandes partes de la biblioteca de soporte. El estándar MTCA fue elegido para mantener la modularidad. El backplane del chasis se conecta a los dispositivos a través de Ethernet y PCIe, habilitando comunicaciones estándar. La Advanced Mezzanine Card es la placa portadora DAQ FPGA típica de MTCA, que conecta el FPGA al backplane del chasis y típicamente proporciona FPGA Mezzanine Connectors (FMC) para conectar placas con conversores analógico-digitales (ADC). Un ADC conectado a través de la interfaz FMC muestrea las señales analógicas pasadas a los núcleos de procesamiento OpenCL. Utilizando OpenCL, los núcleos FPGA se comunican con la máquina anfitriona de una manera estandarizada. Para demostrar la aplicabilidad de esta metodología, se seleccionaron e implementaron problemas de referencia de la literatura, tanto con soluciones tradicionales como de Deep Learning (DL). La metodología elabora ejemplos de conexión de núcleos OpenCL para cualquier caso de uso, y los resultados muestran ejemplos de conjuntos de núcleos. El problema de medición del flujo de neutrones se resuelve a 1GS/s en tiempo real utilizando técnicas de conteo de pulsos y Campbell, utilizando el 40% del objetivo FPGA Arria10. El ejemplo de aplicación DL se basa en una red neuronal para la discriminación neutrones/gammas que logra una tasa de clasificación de hasta 1.3 MEventos/s utilizando menos del 25% de la FPGA objetivo. LABURPENA Ezagutza zientifikoa aurreratzen duten makinak eraikitzea gure garaiko erronka garrantzitsuenetako bat da. Big Science deritzonaren makinak bakarrak dira; beharrezko esperientzia diziplina anitzekoa da, eta herrialde gutxik eraiki eta mantendu ditzakete. Baliabideak eta aurrekontuak mugagabeak ez direnez, konponbide eraginkorrak behar dira. Zientzia Handi makinak hamarkada bat behar dute diseinatuak izateko, hamarkada bat eraikitzeko eta gutxienez hamarkada bat jarduteko. Makina horietako bat fusio-erreaktore handienaren proiektua da, ITER, fusio-ikerketan hurrengo urratsa izango dena eta sistemak integratzeko erronka handia aurkezten duena. Integrazio-ahalegina arintzeko, ITERek estandarizazioan inbertitu du, bai softwarean bai - eta EPICS helburu orokorreko kontrol-sistema banatua. Kontrol-sistema horiek funtsezkoak dira instalazioa kudeatzeko. Komunikazio-protokoloak, prozesuen kudeaketa, monitorizazioa, alarmak eta pertsona-makina interfazeak bezalako funtzioak ematen dituzte, datubaseetarako edo datuak eskuratzeko sistemetarako sarbide aurreratua izateaz gain. Azken hori "Field Programmable Gate Array" (FPGA) batek kudeatzen du, milaka FPGA konektatuz makina bakoitzean eta kontrol-sistemaz milioika seinale kudeatuz. FPGA duten datuak eskuratzeko sistemek (DAQ) programazio aditua eta neketsua eskatzen jarraitzen dute. Problema hori ebazteko marko estandarizatuak, modularrak eta kode irekikoak proposatu dira, hala ere, FPGA hardware programazio espezializatuaren beharra ezin izan da ukatu. Tesi honek DAQ sistema hauek kontrol sistematik hardwareraino kontsideratuz teknologia bilduma bat programazio-ahalegina murrizten du. FPGA gailuak C/C++ antzeko inguruneak erabiliz programatzen dira, - OpenCL izenekoa, C++n idatzita dago, ITERren "Nominal Device Support v3" (NDSv3) eredua jarraituz. Honek software-gailuen garapena diseinu abstraktu eta orokorrez EPICSekiko interfazea sinplifikatzen du. IRIOOpenCL berariaz diseinatuta dago instrumentazio-sistema adimendunen integrazioa sinplifikatzeko, eta algoritmoak FPGAn integratzea ahalbidetzen du, software zati handiak aldatu gabe. Modularitateari eusteko, MTCA estandarra aukeratu da. Txasisaren backplanea Ethernet eta PCIe bidez konektatzen da gailuetara, komunikazio estandarrak gaituz. "Advanced Mezzanine Card" MTCAren ohiko DAQ FPGA plaka eramailea da. FPGA txasisaren backplanera konektatzen du eta, normalean, "FPGA Mezzanine Connectors"ak (FMC) ditu plaka bihurgailu analogiko-digitalak (ADC) konektatzeko. ADCak, FMC interfazearen bidez konektatuta, OpenCL prozesatze-nukleoetara igarotako seinale analogikoak bihurtzen ditu. OpenCL erabiliz, FPGA nukleoak ordenagailuarekin modu estandarizatuan komunikatzen dira. Metodologia horren aplikagarritasuna egiaztatzeko, erreferentziazko adibideak literaturatik hautatu eta nukleoen konexio-adibideak egiten ditu, edozein kasutan erabiltzeko, eta emaitzek nukleo multzoen adibideak erakusten dituzte. Neutroi-fluxuaren neurketak 1GS/s-ko ebazten dira real time errendimenduaz, pultsuak eta Campbell zenbatzeko teknikak erabiliz, Arria10 FPGAren % 40 erabiliz. Azkenik, DL aplikazioaren adibidea, neutroi/gamma sailkapena, sare neuronal batean oinarritzen da, eta FPGAren baliabideen % 25 baino gutxiago erabiliz 1.3 MSailkapen/s sailkapen-tasa lortzen du.

The soft computing algorithms are being nowadays used for various multi input multi output complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application. The conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGA have higher speed and smaller size for real time application than the VLSI design. The challenges are finding an architecture that minimizes the hardware cost, maximizing the performance, accuracy. The goal of this work is to realize the hardware implementation of neural network using FPGA. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL)and is implemented in FPGA chip. MATLAB ANN programming and tools are used for training the ANN. The trained weights are stored in different RAM, and is implemented in FPGA. The design was tested on a FPGA demo board. Keywords- Backpropagation, field programmable gate array (FPGA) hardware implementation, multilayer perceptron, pressure sensor, Xilinx FPGA.

The MIL-STD-188-220 protocol is the interconnection standard between DMTDs in battlefield of America DOD. It's the only standardized protocol in tactical mobile networks by now. The protocol models and performance simulations are widely discussed, but mostly based on software simulations and rarely in the implementation issues. This paper is mainly focused on the protocol implementations and system performance analysis. The implementation is a MIL-STD-220C compatible device, which completing the entire data link layer operation and a part of network layer operation in a single FPGA(field programmable gate array) chip to achieve minimum response time and stable performance. The communication adapter function diagram, working flowchart and performance figures are listed.

This article discusses the current state and development trends of microwave frequency synthesizers for test-and-measurement instruments. The discussed trends are also applicable for other synthesizer types used as modules in more complex systems. Test-and-measurement synthesizer market is estimated at $300 with an annual growth rate about 3%.A modern trend for the development of frequency synthesizers is associated with low phase noise, low spurious and fast switching speed. Important parameters also include operating frequency range, frequency resolution, modulation capabilities as well as size and cost characteristics. The main architectures based on direct analog, direct digital and indirect synthesis are evaluated. As of today, traditional indirect phase-lock-loop (PLL) architectures still dominate. On the other hand, direct analog synthesis is the most advanced approach that demonstrates extremely fast switching speed and low phase noise. Nevertheless, future developments are associated with direct digital synthesis (DDS) due to the rapid progress in solid-state technologies. DDS are currently available as special integrated circuits (IC) or can be build using field-programmable logic arrays (FPGA) and high-frequency digital-to-analog converters (DAC). Parallel (multichannel) digital structures coupled with high-frequency DACs allow generating output signals at microwave frequencies. Further improvements are possible using two or more DACs in an interleaved data mode. Wider frequency coverage through several tens of gigahertz and lower spur characteristics are expected.The article notes that modern frequency synthesizers allow generating complex signals using vector IQ-modulation. The modulation bandwidth is constantly increases through hundreds of megahertz or even several gigahertz. Further advances in microwave frequency synthesizers are associated with new reference types such as sapphire-loaded cavity oscillators with combined frequency stabilization as well as optoelectronic methods.

The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.

This paper presents a design of large dynamic range accurate digitally programmable delay line with 250-ps resolution on a single field programmable gate array (FPGA) chip. This design adopts time-to-digital conversion (TDC) technology, counter-based delay technology and small range digitally programmable delay line technology. When working with an oscillator with frequency accuracy of plusmn1 ppm, and when the delay range is within 0~0.1 ms, the delay accuracy of our design can reach plusmn350 ps

A single chip FPGA-based solution for controlling of multi-unit PMSM motor with time-division multiplexing scheme

The mitigation of single event upset (SEU) in SRAM based Field Programmable Gate Array (FPGA) is increasingly important as it is widely used in radiation environments such as space. As D flip-flop (DFF) and memory (including Block RAM and Configuration RAM) are the key elements in FPGAs, it is crucial to develop radiation hardening techniques for enhanced reliability of the DFF and memory. A novel hardened memory design for FPGA is proposed with multi-bit upset (MBU) protection. We further developed two prototype FPGA chips, one with and the other without SEU hardening for comparison. The FPGA chips are fabricated in a standard 0.13μm CMOS process and have a volume of 3 million equivalent logic gates. In contrast to the base FPGA, the SEU cross section of the memory in the hardened FPGA is at least three orders of magnitude lower. Also, no SEU upsets are observed in the DFF of the hardened FPGA.