350 research outputs found

    Discrete-Time Mixing Receiver Architecture for RF-Sampling Software-Defined Radio

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    A discrete-time (DT) mixing architecture for RF-sampling receivers is presented. This architecture makes RF sampling more suitable for software-defined radio (SDR) as it achieves wideband quadrature demodulation and wideband harmonic rejection. The paper consists of two parts. In the first part, different downconversion techniques are classified and compared, leading to the definition of a DT mixing concept. The suitability of CT-mixing and RF-sampling receivers to SDR is also discussed. In the second part, we elaborate the DT-mixing architecture, which can be realized by de-multiplexing. Simulation shows a wideband 90° phase shift between I and Q outputs without systematic channel bandwidth limitation. Oversampling and harmonic rejection relaxes RF pre-filtering and reduces noise and interference folding. A proof-of-concept DT-mixing downconverter has been built in 65 nm CMOS, for 0.2 to 0.9 GHz RF band employing 8-times oversampling. It can reject 2nd to 6th harmonics by 40 dB typically and without systematic channel bandwidth limitation. Without an LNA, it achieves a gain of -0.5 to 2.5 dB, a DSB noise figure of 18 to 20 dB, an IIP3 = +10 dBm, and an IIP2 = +53 dBm, while consuming less than 19 mW including multiphase clock generation

    Palmo : a novel pulsed based signal processing technique for programmable mixed-signal VLSI

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    In this thesis a new signal processing technique is presented. This technique exploits the use of pulses as the signalling mechanism. This Palmo 1 signalling method applied to signal processing is novel, combining the advantages of both digital and analogue techniques. Pulsed signals are robust, inherently low-power, easily regenerated, and easily distributed across and between chips. The Palmo cells used to perform analogue operations on the pulsed signals are compact, fast, simple and programmable

    Mixed-Signal Neural Network Implementation with Programmable Neuron

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    This thesis introduces implementation of mixed-signal building blocks of an artificial neural network; namely the neuron and the synaptic multiplier. This thesis, also, investigates the nonlinear dynamic behavior of a single artificial neuron and presents a Distributed Arithmetic (DA)-based Finite Impulse Response (FIR) filter. All the introduced structures are designed and custom laid out

    Multiband Analog-to-Digital Conversion

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    The current trend in the world of digital communications is the design of versatile devices that may operate using several different communication standards in order to increase the number of locations for which a particular device may be used. The signal is quantized early on in the reciever path by Analog-to-Digital Converters (ADCs), which allows the rest of the signal processing to be done by low complexity, low power digital circuits. For this reason, it is advantageous to create an architecture that can quantize different bandwidths at different frequencies to suit several different communication protocols. This thesis outlines the design of an architecture that uses multiple ADCs in parallel to quantize several different bandwidths of a wideband signal. A multirate filter bank is then applied to approximate perfect reconstruction of the wideband signal from its subband parts. This highly flexible architecture is able to quantize signals of varying bandwidths at a wide range of frequencies by using identical hardware in every channel, which also makes for a simple design. A prototype for the quantizer used in each channel, a frequency-selective fourth-order sigma-delta (CA ) ADC, was designed and fabricated in a 0.5 pm CMOS process. This device uses a switched-capacitor technique to implement the frequency selection in the front-end of the CA ADC in each channel. Running at a 5MHz sample rate, the device can select any of the first sixteen 156.25kHz wide bands for conversion. Testing results for this fabricated part are also presented

    An Evaluation of the S2Ia switched-current architecture for (delta)(sigma) modulator ADCs

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    Switched-Current (SI) is a design methodology by which discrete time, current mode, analog circuits can be implemented using standard digital CMOS processes, allowing the addition of analog signal processing circuits, analog to digital converters (ADCs), digital to analog converters (DACs) and other analog and mixed-signal circuits to otherwise digital only microchips without the need and expense of any extra fabrication steps. SI circuits operate by employing a secondary effect in CMOS circuits, a transistor\u27s gate capacitance, to store charge and thus form a current memory cell. A current memory cell is one of the basic building blocks found in most SI circuits and is usually the distinguishing feature of the various approaches to SI circuit design. Delta Sigma Modulators (DSMs) are discrete time, mixed-signal circuits making them well suited to implementation using the SI methodology. These circuits can form the basis of either an ADC or DAC and thus provide a good example of the SI technique employing a particular current memory cell implementation. For this work, a First Order DSM-based ADC was designed and simulated to verify the feasibility of a variant of the S2I Switched-Current Memory Cell architecture, the S2Ia Switched-Current Memory Cell, in a low-voltage, digital, 0.5/j.m CMOS process. The A D C design was targeted towards voiceband (4kHz bandwidth) applications over which it achieved a 6-bit resolution and separately attained a greater than 80kHz bandwidth. Extension of the First Order DSM employed in this design to a Second Order DSM would increase the resolution to at least 8-bits without sacrificing bandwidth. Although potentially less accurate than the S2I Switched-Current Memory Cell, a S2Ia cell has the advantage of requiring only two clock signals to the S2I cell\u27s four. Further, for cascades of S2Ia cells the number of clock signals remains two while a S2I cell cascade requires six separate clock signals. S2Ia-based circuits therefore require less complex clock generation circuitry and fewer clock lines

    Analog signal processing on a reconfigurable platform

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    The Cooperative Analog/Digital Signal Processing (CADSP) research group's approach to signal processing is to see what opportunities lie in adjusting the line between what is traditionally computed in digital and what can be done in analog. By allowing more computation to be done in analog, we can take advantage of its low power, continuous domain operation, and parallel capabilities. One setback keeping Analog Signal Processing (ASP) from achieving more wide-spread use, however, is its lack of programmability. The design cycle for a typical analog system often involves several iterations of the fabrication step, which is labor intensive, time consuming, and expensive. These costs in both time and money reduce the likelihood that engineers will consider an analog solution. With CADSP's development of a reconfigurable analog platform, a Field-Programmable Analog Array (FPAA), it has become much more practical for systems to incorporate processing in the analog domain. In this Thesis, I present an entire chain of tools that allow one to design simply at the system block level and then compile that design onto analog hardware. This tool chain uses the Simulink design environment and a custom library of blocks to create analog systems. I also present several of these ASP blocks, covering a broad range of functions from matrix computation to interfacing. In addition to these tools and blocks, the most recent FPAA architectures are discussed. These include the latest RASP general-purpose FPAAs as well as an adapted version geared toward high-speed applications.M.S.Committee Chair: Hasler, Paul; Committee Member: Anderson, David; Committee Member: Ghovanloo, Maysa

    Matrix Transform Imager Architecture for On-Chip Low-Power Image Processing

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    Camera-on-a-chip systems have tried to include carefully chosen signal processing units for better functionality, performance and also to broaden the applications they can be used for. Image processing sensors have been possible due advances in CMOS active pixel sensors (APS) and neuromorphic focal plane imagers. Some of the advantages of these systems are compact size, high speed and parallelism, low power dissipation, and dense system integration. One can envision using these chips for portable and inexpensive video cameras on hand-held devices like personal digital assistants (PDA) or cell-phones In neuromorphic modeling of the retina it would be very nice to have processing capabilities at the focal plane while retaining the density of typical APS imager designs. Unfortunately, these two goals have been mostly incompatible. We introduce our MAtrix Transform Imager Architecture (MATIA) that uses analog floating--gate devices to make it possible to have computational imagers with high pixel densities. The core imager performs computations at the pixel plane, but still has a fill-factor of 46 percent - comparable to the high fill-factors of APS imagers. The processing is performed continuously on the image via programmable matrix operations that can operate on the entire image or blocks within the image. The resulting data-flow architecture can directly perform all kinds of block matrix image transforms. Since the imager operates in the subthreshold region and thus has low power consumption, this architecture can be used as a low-power front end for any system that utilizes these computations. Various compression algorithms (e.g. JPEG), that use block matrix transforms, can be implemented using this architecture. Since MATIA can be used for gradient computations, cheap image tracking devices can be implemented using this architecture. Other applications of this architecture can range from stand-alone universal transform imager systems to systems that can compute stereoscopic depth.Ph.D.Committee Chair: Hasler, Paul; Committee Member: David Anderson; Committee Member: DeWeerth, Steve; Committee Member: Jackson, Joel; Committee Member: Smith, Mar

    Passive Switched Capacitor RF Front Ends for Spectrum Sensing in Cognitive Radios

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    This paper explores passive switched capacitor based RF receiver front ends for spectrum sensing. Wideband spectrum sensors remain the most challenging block in the software defined radio hardware design. The use of passive switched capacitors provides a very low power signal conditioning front end that enables parallel digitization and software control and cognitive capabilities in the digital domain. In this paper, existing architectures are reviewed followed by a discussion of high speed passive switched capacitor designs. A passive analog FFT front end design is presented as an example analog conditioning circuit. Design methodology, modeling, and optimization techniques are outlined. Measurements are presented demonstrating a 5 GHz broadband front end that consumes only 4 mW power
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