High Performance LNAs and Mixers for Direct Conversion Receivers in BiCMOS and CMOS Technologies

The trend in cellular chipset design today is to incorporate support for a larger number of frequency bands for each new chipset generation. If the chipset also supports receiver diversity two low noise amplifiers (LNAs) are required for each frequency band. This is however associated with an increase of off-chip components, i.e. matching components for the LNA inputs, as well as complex routing of the RF input signals. If balanced LNAs are implemented the routing complexity is further increased. The first presented work in this thesis is a novel multiband low noise single ended LNA and mixer architecture. The mixer has a novel feedback loop suppressing both second order distortion as well as DC-offset. The performance, verified by Monte Carlo simulations, is sufficient for a WCDMA application. The second presented work is a single ended multiband LNA with programmable integrated matching. The LNA is connected to an on-chip tunable balun generating differential RF signals for a differential mixer. The combination of the narrow band input matching and narrow band balun of the presented LNA is beneficial for suppressing third harmonic downconversion of a WLAN interferer. The single ended architecture has great advantages regarding PCB routing of the RF input signals but is on the other hand more sensitive to common mode interferers, e.g. ground, supply and substrate noise. An analysis of direct conversion receiver requirements is presented together with an overview of different LNA and mixer architectures in both BiCMOS and CMOS technology

Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle

Navigation of mobile robots using artificial intelligence technique.

The ability to acquire a representation of the spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. This document presents a computer vision method and related algorithms for the navigation of a robot in a static environment. Our environment is a simple white colored area with black obstacles and robot (with some identification mark-a circle and a rectangle of orange color which helps in giving it a direction) present over it. This environment is grabbed in a camera which sends image to the desktop using data cable. The image is then converted to the binary format from jpeg format using software which is then processed in the computer using MATLAB. The data acquired from the program is then used as an input for another program which controls the robot drive motors using wireless controls. Robot then tries to reach its destination avoiding obstacles in its path. The algorithm presented in this paper uses the distance transform methodology to generate paths for the robot to execute. This paper describes an algorithm for approximately finding the fastest route for a vehicle to travel one point to a destination point in a digital plain map, avoiding obstacles along the way. In our experimental setup the camera used is a SONY HANDYCAM. This camera grabs the image and specifies the location of the robot (starting point) in the plain and its destination point. The destination point used in our experimental setup is a table tennis ball, but it can be any other entity like a single person, a combat unit or a vehicle

Digitally-Enhanced Software-Defined Radio Receiver Robust to Out-of-Band Interference

A software-defined radio (SDR) receiver with improved robustness to out-of-band interference (OBI) is presented. Two main challenges are identified for an OBI-robust SDR receiver: out-of-band nonlinearity and harmonic mixing. Voltage gain at RF is avoided, and instead realized at baseband in combination with low-pass filtering to mitigate blockers and improve out-of-band IIP3. Two alternative “iterative” harmonic-rejection (HR) techniques are presented to achieve high HR robust to mismatch: a) an analog two-stage polyphase HR concept, which enhances the HR to more than 60 dB; b) a digital adaptive interference cancelling (AIC) technique, which can suppress one dominating harmonic by at least 80 dB. An accurate multiphase clock generator is presented for a mismatch-robust HR. A proof-of-concept receiver is implemented in 65 nm CMOS. Measurements show 34 dB gain, 4 dB NF, and 3.5 dBm in-band IIP3 while the out-of-band IIP3 is + 16 dBm without fine tuning. The measured RF bandwidth is up to 6 GHz and the 8-phase LO works up to 0.9 GHz (master clock up to 7.2 GHz). At 0.8 GHz LO, the analog two-stage polyphase HR achieves a second to sixth order HR > dB over 40 chips, while the digital AIC technique achieves HR > 80 dB for the dominating harmonic. The total power consumption is 50 mA from a 1.2 V supply

Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

Optimization of video capturing and tone mapping in video camera systems

Image enhancement techniques are widely employed in many areas of professional and consumer imaging, machine vision and computational imaging. Image enhancement techniques used in surveillance video cameras are complex systems involving controllable lenses, sensors and advanced signal processing. In surveillance, a high output image quality with very robust and stable operation under difficult imaging conditions are essential, combined with automatic, intelligent camera behavior without user intervention. The key problem discussed in this thesis is to ensure this high quality under all conditions, which specifically addresses the discrepancy of the dynamic range of input scenes and displays. For example, typical challenges are High Dynamic Range (HDR) and low-dynamic range scenes with strong light-dark differences and overall poor visibility of details, respectively. The detailed problem statement is as follows: (1) performing correct and stable image acquisition for video cameras in variable dynamic range environments, and (2) finding the best image processing algorithms to maximize the visualization of all image details without introducing image distortions. Additionally, the solutions should satisfy complexity and cost requirements of typical video surveillance cameras. For image acquisition, we develop optimal image exposure algorithms that use a controlled lens, sensor integration time and camera gain, to maximize SNR. For faster and more stable control of the camera exposure system, we remove nonlinear tone-mapping steps from the level control loop and we derive a parallel control strategy that prevents control delays and compensates for the non-linearity and unknown transfer characteristics of the used lenses. For HDR imaging we adopt exposure bracketing that merges short and long exposed images. To solve the involved non-linear sensor distortions, we apply a non-linear correction function to the distorted sensor signal, implementing a second-order polynomial with coefficients adaptively estimated from the signal itself. The result is a good, dynamically controlled match between the long- and short-exposed image. The robustness of this technique is improved for fluorescent light conditions, preventing serious distortions by luminance flickering and color errors. To prevent image degradation we propose both fluorescent light detection and fluorescence locking, based on measurements of the sensor signal intensity and color errors in the short-exposed image. The use of various filtering steps increases the detector robustness and reliability for scenes with motion and the appearance of other light sources. In the alternative algorithm principle of fluorescence locking, we ensure that light integrated during the short exposure time has a correct intensity and color by synchronizing the exposure measurement to the mains frequency. The second area of research is to maximize visualization of all image details. This is achieved by both global and local tone mapping functions. The largest problem of Global Tone Mapping Functions (GTMF) is that they often significantly deteriorate the image contrast. We have developed a new GTMF and illustrate, both analytically and perceptually, that it exhibits only a limited amount of compression, compared to conventional solutions. Our algorithm splits GTMF into two tasks: (1) compressing HDR images (DRC transfer function) and (2) enhancing the (global) image contrast (CHRE transfer function). The DRC subsystem adapts the HDR video signal to the remainder of the system, which can handle only a fraction of the original dynamic range. Our main contribution is a novel DRC function shape which is adaptive to the image, so that details in the dark image parts are enhanced simultaneously while only moderately compressing details in the bright areas. Also, the DRC function shape is well matched with the sensor noise characteristics in order to limit the noise amplification. Furthermore, we show that the image quality can be significantly improved in DRC compression if a local contrast preservation step is included. The second part of GTMF is a CHRE subsystem that fine-tunes and redistributes the luminance (and color) signal in the image, to optimize global contrast of the scene. The contribution of the proposed CHRE processing is that unlike standard histogram equalization, it can preserve details in statistically unpopulated but visually relevant luminance regions. One of the important cornerstones of the GTMF is that both DRC and CHRE algorithms are performed in the perceptually uniform space and optimized for the salient regions obtained by the improved salient-region detector, to maximize the relevant information transfer to the HVS. The proposed GTMF solution offers a good processing quality, but cannot sufficiently preserve local contrast for extreme HDR signals and it gives limited improvement low-contrast scenes. The local contrast improvement is based on the Locally Adaptive Contrast Enhancement (LACE) algorithm. We contribute by using multi-band frequency decomposition, to set up the complete enhancement system. Four key problems occur with real-time LACE processing: (1) "halo" artifacts, (2) clipping of the enhancement signal, (3) noise degradation and (4) the overall system complexity. "Halo" artifacts are eliminated by a new contrast gain specification using local energy and contrast measurements. This solution has a low complexity and offers excellent performance in terms of higher contrast and visually appealing performance. Algorithms preventing clipping of the output signal and reducing noise amplification give a further enhancement. We have added a supplementary discussion on executing LACE in the logarithmic domain, where we have derived a new contrast gain function solving LACE problems efficiently. For the best results, we have found that LACE processing should be performed in the logarithmic domain for standard and HDR images, and in the linear domain for low-contrast images. Finally, the complexity of the contrast gain calculation is reduced by a new local energy metric, which can be calculated efficiently in a 2D-separable fashion. Besides the complexity benefit, the proposed energy metric gives better performance compared to the conventional metrics. The conclusions of our work are summarized as follows. For acquisition, we need to combine an optimal exposure algorithm, giving both improved dynamic performance and maximum image contrast/SNR, with robust exposure bracketing that can handle difficult conditions such as fluorescent lighting. For optimizing visibility of details in the scene, we have split the GTMF in two parts, DRC and CHRE, so that a controlled optimization can be performed offering less contrast compression and detail loss than in the conventional case. Local contrast is enhanced with the known LACE algorithm, but the performance is significantly improved by individually addressing "halo" artifacts, signal clipping and noise degradation. We provide artifact reduction by new contrast gain function based on local energy, contrast measurements and noise estimation. Besides the above arguments, we have contributed feasible performance metrics and listed ample practical evidence of the real-time implementation of our algorithms in FPGAs and ASICs, used in commercially available surveillance cameras, which obtained awards for their image quality

The Cleo Rich Detector

We describe the design, construction and performance of a Ring Imaging Cherenkov Detector (RICH) constructed to identify charged particles in the CLEO experiment. Cherenkov radiation occurs in LiF crystals, both planar and ones with a novel ``sawtooth''-shaped exit surface. Photons in the wavelength interval 135--165 nm are detected using multi-wire chambers filled with a mixture of methane gas and triethylamine vapor. Excellent pion/kaon separation is demonstrated.Comment: 75 pages, 57 figures, (updated July 26, 2005 to reflect reviewers comments), to be published in NIM

Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface

This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio and spike information preservation. For the latter, we use metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data compression ratio of $50-100$ can be achieved, $5-18\times$ more than prior work, by selective transmission of event pulses corresponding to neural spikes. A correlation coefficient of $\approx0.9$ and spike detection accuracy of over $90\%$ for the worst-case analysis involving $10K$-channel simulated recording and typical analysis using $100$ or $384$-channel real neural recordings. We also analyze the collision handling capability and scalability of the proposed pipeline.Comment: 14 pages, 8 figures, IEEE Transaction submission manuscript. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl