nils-herrmann/clean_scirepeval huggingface dataset:
NONUNIFORMITY CORRECTION OF IMAGING ARRAYS USING THE CONSTANT STATISTICS CONSTRAINT

Parameter variations cause unavoidable nonuniformities in Infra-Red Focal Plane Arrays and other integrated sensors. Since these nonuniformities change slowly with time, calibrating sensors one-time only is not suitable and much more frequent calibration is required. We have developed an algorithm that continually calibrates an array of sensors that contains both gain and offset variations. This algorithm has been mapped to analog hardware and designed and fabricated with a 2um CMOS technology. Measured results from the chip show that the system achieves invariance to gain and offset variations of the input signal.
A time-based CMOS image sensor

A two-degree of freedom time-based CMOS image sensor is proposed in this paper for wide dynamic range imaging. Instead of reading analog voltages off chip, a time representation is used to record when the photodetector voltage drops below a time-varying threshold. The time measurements and the reference voltage waveform are used to reconstruct the image. Experimental results verify that the two-degree of freedom sampling technique is feasible for wide dynamic range imaging in video mode.
A low-power analog spike detector for extracellular neural recordings

This paper discusses a low-power spike detection circuit, which reduces bandwidth from neural recordings by only outputting a short pulse at each neural spike time. Communication bandwidth is dramatically reduced to the number of spikes. The principal idea is to use two low pass filters, one with a higher cutoff frequency to remove high frequency noise and the other with a lower cutoff frequency to create a local average. When the difference between the signal and the local average rises above a threshold, a spike is detected. The circuit uses subthreshold CMOS to keep the power consumption low enough for integration of many channels in an implanted device. This spike detection method shows promising results towards a robust and unsupervised algorithm that is lower power and more compact than existing spike detection methods.
An augmented error criterion for linear adaptive filtering: theory, algorithms and applications

Ever since its conception, the mean-squared error (MSE) criterion has been the workhorse of optimal linear adaptive filtering. However, it is a well-known fact that the MSE criterion is no longer optimal in situations where the data are corrupted by noise. Noise, being omnipresent in most of the engineering applications, can result in severe errors in the solutions produced by the MSE criterion. In this dissertation, we propose novel error criteria and the associated learning algorithms followed by a detailed mathematical analysis of these algorithms. Specifically, these criteria are designed to solve the problem of optimal filtering with noisy data. Firstly, we discuss a new criterion called augmented error criterion (AEC) that can provide unbiased parameter estimates even in the presence of additive white noise. Then, we derive novel, online sample-by-sample learning algorithms with varying degrees of complexity and performance that are tailored for real-world applications. Rigorous mathematical analysis of the new algorithms is presented. In the second half of this dissertation, we extend the AEC to handle correlated noise in the data. The modifications introduced will enable us to obtain optimal, unbiased parameter estimates of a linear system when the data are corrupted by correlated noise. Further, we achieve this without explicitly assuming any prior information about the noise statistics. The analytical solution is derived and an iterative stochastic algorithm is presented to estimate this optimal solution. 
The proposed criteria and the learning algorithms can be applied in many engineering problems. System identification and controller design problems are obvious areas where the proposed criteria can be efficiently used. Other applications include model-order estimation in the presence of noise and design of multiple local linear filters to characterize complicated nonlinear systems.
Integrated VLSI potentiostat for cyclic voltammetry in electrolytic reactions

This paper describes a CMOS integrated potentiostatic control circuit. The design maintains a constant bias potential between the reference and working electrodes for an amperometric chemical sensor. A technique of converting input currents into time for amperometric measurements is proposed. Redox currents ranging from 1pA to 200nA can be measured with a maximum non-linearity of /spl plusmn/0.1% over this range. The design can be used to generate a cyclic voltammogram for an electrochemical reaction by sweeping the voltages across the range specified by the user. Analog inputs are processed and digital outputs are generated without requiring a power-hungry A/D converter. A prototype chip has been fabricated in the 0.5/spl mu/m AMI CMOS process. Experimental results are reported showing the performance of the circuit as a chemical sensor.
Logic computation using coupled neural oscillators

This work presents our recent work in using neural oscillators inspired by olfactory cortex models for Boolean logic computation. Two such coupled oscillators are sufficient to implement all the Boolean primitives (including AND, OR and XOR). Although the same computational model is being studied as a content addressable memory, these results indicate that additional computational power exists in this class of oscillatory neural networks. Detailed configuration of the proposed structure, experimental results and potential applications are presented.
Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition.

Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor cepstral coefficients (HFCC), a modification of MFCC that uses the known relationship between center frequency and critical bandwidth from human psychoacoustics to decouple filter bandwidth from filter spacing. In this work, the authors introduce a variation of HFCC called HFCC-E in which filter bandwidth is linearly scaled in order to investigate the effects of wider filter bandwidth on noise robustness. Experimental results show an increase in signal-to-noise ratio of 7 dB over traditional MFCC algorithms when filter bandwidth increases in HFCC-E. An important attribute of both HFCC and HFCC-E is that the algorithms only differ from MFCC in the filter bank coefficients: increased noise robustness using wider filters is achieved with no additional computational cost.
A spike-based adaptive filter

We propose a spike-based adaptive filter with supervised learning. Unlike standard adaptive filters, here the optimal MSE solution is not unique for the spike-based system identification problem. The simplex method is introduced to select one of the many possible optimal solutions. In simulations, an LMS-based learning procedure is designed and, for faster convergence, we introduce a credit assignment method which penalizes all the weights contributing to the current error signal. Finally, we discuss issues regarding the implementation of the spike-based adaptive filter in an analog VLSI circuit.
AN INTEGRATE AND FIRE PIXEL WITH CONTRAST OUTPUTS FOR A CMOS IMAGER

This paper considers a novel pixel architecture inspired by the integrate and fire neurons in biological vision systems to improve the dynamic range and output bandwidth of CMOS imagers. An imager based on this pixel can operate in either image mode—outputting image data in order or illumination—or in contrast mode—outputting both contrast and image data in order of contrast. Simulation results for a 6 pixel array are presented, and shown to produce results consistent with a brief analysis. An array of 14 pixels has been submitted to MOSIS and is pending fabrication.
A HMM-Based System for Training of Second Language Aquisition

We describe a system for the training of Second Language Acquisition Pronunciation (SLAP) for non-native speakers. This speech recognition-based system is designed to mimic the valuable interactions between second-language students and a fluent teacher. When a student speaks a word into SLAP’s microphone, it is analyzed to determine the part of the word (if any) that is incorrectly pronounced. A fluent utterance of the word is then played back to the student with emphasis on the mispronounced part of the word. Just as a live teacher naturally does, the difficult part of the word is played back louder, extended in time and possibly with higher pitch. We demonstrate SLAP on a multisyllabic word to show typical performance.
Improving the filter bank of a classic speech feature extraction algorithm

The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (MFCC) algorithm. Introduced in 1980, the filter bank-based algorithm eventually replaced linear prediction cepstral coefficients (LPCC) as the premier front end, primarily because of MFCC's superior robustness to additive noise. However, MFCC does not approximate the critical bandwidth of the human auditory system. We propose a novel scheme for decoupling filter bandwidth from other filter bank parameters, and we demonstrate improved noise robustness over three versions of MFCC through HMM-based experiments with the English digits in various noise environments.
INFORMATION THEORETIC SELF-ORGANIZATION OF MULTIPLE AGENTS By RAJVIGNESH THOGULUA CHANDRASEKAR A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science INFORMATION THEORETIC SELF-ORGANIZATION OF MULTIPLE AGENTS By Rajvignesh Thogulua Chandrasekar August 2003  Jose C. Principe Major Department: Electrical and Computer Engineering This study intends to use the information theoretic interaction principle for selforganization of multiple agents. The impetus of this work comes from the recent increase in the application of swarm intelligence to versatile fields like defense, space and communication networks. One of the important problems in this exciting area is the coordination and control of individual agents in the swarm to produce desired collective behavior. Most of the available solutions for this problem are either heuristic or based on centralized approaches. Centralized approaches, often referred to as the leader–follower strategy, may not be applicable in many real-life situations. It may also lead to frailty in the overall system and increased costs. In this work, a decentralized approach to solve this problem using an information theoretic interaction between the agents is suggested. This technique uses entropy maximization to spread the agents uniformly over a particular region of interest and also to guide the collective towards a target. By minimizing the communication between the
SLAP: a system for the detection and correction of pronunciation for second language acquisition

We describe a system for the training of Second Language Acquisition Pronunciation (SLAP) for nonnative speakers. This speech recognition-based system is designed to mimic the valuable interactions between second-language students aid a fluent teacher. For our system, when a student speaks a word into SLAP's microphone, it is analyzed to determine the part of the word (if any) that is incorrectly pronounced. A fluent utterance of the word is then played back to the student with emphasis on the mispronounced part of the word. Just as a live teacher naturally does, the difficult part of the word is played back louder, extended in time and possibly with higher pitch. Current products on the market lack robustness and have no provision for playing back and emphasizing the incorrect portion. We demonstrate SLAP on a multisyllabic word to show typical performance.
A CMOS neural oscillator using negative resistance

A CMOS neural oscillator using negative resistance has been designed and fabricated in an AMI 0.5 /spl mu/m double poly technology through MOSIS. The proposed neural oscillator consists of a nonlinear resistor with negative resistance as well as standard OTAs and capacitors. Simulations of a network of oscillators demonstrate cooperative computation. Measurements of the fabricated oscillator chip confirm the input-gated oscillatory behavior of the cell.
Design of a reduced KII set and network in analog VLSI

This paper focuses on the analog VLSI design of a reduced KII set and network, which are basic building blocks in the computational model of the olfactory cortex proposed by Walter Freeman. Previous implementation issues were discussed in 2001, but this paper will present recent developments in the circuit design of individual blocks in a reduced KII set as well as a system architecture for the KII network. The coupling coefficients are built into the chip and the shape of the nonlinear block is controlled externally. To improve the efficiency of the nonlinear block and to reduce the problem of massive interconnections in the KII network, a mixed-signal design to multiplex nonlinear blocks is proposed. Simulation results and chip measurement are presented for the reduced KII set.
Increased mfcc filter bandwidth for noise-robust phoneme recognition

Many speech recognition systems use mel-frequency cepstral coefficient (mfcc) feature extraction as a front end. In the algorithm, a speech spectrum passes through a filter bank of mel-spaced triangular filters, and the filter output energies are log-compressed and transformed to the cepstral domain by the OCT. The spacing of filter bank center frequencies mimics the known warped-frequency characteristics of the human auditory system, yet the bandwidths of these filters is not chosen through biological inspiration. Instead they are set by aligning endpoints of the triangle, which is itself an arbitrary shape. It is surprising that for such a popular speech recognition front end, proper analysis or optimization of the filter bandwidths has not been performed. With complex cochlear models, realistic filter shapes that more closely approximate critical bands are used. And these filters, compared to the filters used in mfcc, are considerably wider and overlap with neighboring filters more. We have extended this filter characteristic to the mfcc algorithm and found that the increased filter bandwidth improves recognition performance in clean speech and provides added noise robustness as well.
Energy redistribution speech intelligibility enhancement, vocalic and transitional cues

Two novel speech enhancement algorithms are presented that automatically increase intelligibility in noisy environments while maintaining the signal power and naturalness of the original speech. These energy redistribution (ER) algorithms move signal energy to targeted regions of relatively high information content that are crucial for intelligibility. The boosted regions are originally of low energy and therefore usually the first segments lost with the addition of environmental noise. The ER voiced/unvoiced (ERVU) method transfers energy from voiced speech to regions of unvoiced speech, while the ER spectral transition (ERST) method moves energy from spectrally stationary regions to spectrally transitional regions. Hand‐held cell phones and public address systems are expected to be the dominant applications for these techniques. Standard noise reduction methods such as spectral subtraction are assumed to have already been applied to the voice signal before broadcast. Using human listening tests, it was ...
The changing roles of analog and digital signal processing in CMOS image sensors

Throughout the years digital signal processing has been encroaching more and more into what had previously been exclusive analog circuit domains. Here we briefly summarize various lines of research in CMOS imaging sensing technology paying special attention to the gradual transition from analog to digital processing. The transition from continuous-time continuous-value pixels to digital (discrete-time binary-value) pixels could be viewed as the natural final step in the evolution of these imaging circuits in order to achieve wide dynamic range with acceptable signal to noise ratio. After data is readoff a digital pixel imager chip, digital signal processing is required to reconstruct the image for display.
Human factor cepstral coefficients

Automatic speech recognition (ASR) is an emerging field with the goal of creating a more natural man/machine interface. The single largest obstacle to widespread use of ASR technology is robustness to noise. Since human speech recognition greatly outperforms current ASR systems in noisy environments, ASR systems seek to improve noise robustness by drawing on biological inspiration. Most ASR front ends employ mel frequency cepstral coefficients (mfcc) which is a filter bank‐based algorithm whose filters are spaced on a linear‐log frequency scale. Although center frequency is based on a perceptually motivated frequency scale, filter bandwidth is set by filter spacing and not through biological motivation. The coupling of filter bandwidth to other filter bank parameters (frequency range, number of filters) has led to variations of the original algorithm with different filter bandwidths. In this work, a novel extension to mfcc is introduced which decouples filter bandwidth from the rest of the filter bank par...
Efficient Object Recognition Using Color

A real-time prototype system for identifying soda cans uses histogram indexing with only 16 colors, with excellent results. The final implementation identified cans almost perfectly, even on public display with many people attempting to fool it. Results from preliminary versions are presented, with a complete description of the circumstances used in training and testing the system. The final system required only 1.8 kilobytes of storage space for the database, 37 kilobytes of space for the program, and 70 milliseconds to process each image, proving its suitability for implementation on an autonomous
Self-organizing features for regularized standardization of brain images

Normal variability of anatomy is a key issue in standardization. By reducing normal variability, functional activity from multiple subjects can be overlayed to study localization, and variability outside normal ranges can be used to report abnormalities. Most of the existing global standardization methods fail to align individual anatomic structures. We propose a semi-automatic, feature-based standardization technique to complement these global methods. Benefits of our method are speed and accuracy in local alignment. The method consists of three phases: In phase one, templates are generated from the atlas structures, using Self-Organizing Maps (SOMs). The parameters of each SOM are determined using a new topology evaluation technique. In phase two, the atlas templates are reconfigured using points from individual features, to establish a one-to-one correspondence between the atlas and individual structures. During training, a regularization procedure can be optionally invoked to guarantee smoothness in areas where the discrepancy between the atlas and individual feature is high. In the final phase, difference vectors are generated using the corresponding points of the atlas and individual structure. The whole image is warped by interpolation of the difference vectors through Gaussian radial basis functions, which are determined by minimizing the membrane energy. Results are demonstrated on simulated features, as well as selected sulci in brain MRIs.
Design and implementation of a biologically realistic olfactory cortex in analog VLSI

This paper reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context, we motivate the use of nonconvergent dynamics for the signal to symbol translator. We then describe a biologically realistic model of the olfactory system proposed by W. Freeman (1975) that has locally stable dynamics but is globally chaotic. We show how we can discretize Freeman's model using digital signal processing techniques, providing an alternative to the more conventional Runge-Kutta integration. This analysis leads to a direct mixed-signal (analog amplitude/discrete time) implementation of the dynamical building block that simplifies the implementation of the interconnect. We present results of simulations and measurements obtained from a fabricated analog VLSI chip.
Self-organizing features for regularized image standardization

To the girls in our family: my mom, who made learning a lifelong quest, my sister, who discovered the champion within. In celebration of our triple graduation, with a MS in political sciences, an MBA, and a PhD in computer sciences, at the turn of this millennium. iv ACKNOWLEDGMENTS During the course of my dissertation, I received tremendous support. Dr. John Harris deserves the best compliments for supervision. After I started working with him, a dissertation so boring and monstrous became a challenge, a quest, of which I enjoyed every minute. His technical judgement helped put a lot of quality into this work. I am greatly indebted for all his efforts. Dr. U«ur Hal²c² from Middle East Technical University was a big influence when I made the decision to pursue PhD. studies. I would like to thank her for being a wonderful role model. Dr. Dawn Bowers always helped in hardships, as a friend, mentor, and boss. I am grateful to Dr. Bruce Crosson for his support, and Dr. Richard Briggs for his valuable time spent in the collection of the brain images used in this study. I have learned a lot from them and I feel priviledged to have been a part of their research groups for a long time. During times when I needed anatomical guidance, Dr. Tiana Leonard was always available. I appreciate her patience in continually explaining to us engineers the extent of anatomical variability. By her help, I learned that the precision expected in brain anatomy by engineers is nonexistent. I am also indebted to Dr. Sartaj Sahni, who did not hesitate to take charge at a very difficult time. And I would like to thank Dr. Jose Principe for serving on my committee. v Some hidden heroes were also with me throughout this study. our graduate secretary, and Dr. Doug Dankel, our graduate coordinator, helped tremendously with official procedures and problems. As a last word, I would like to mention that completing my doctoral studies has been more of an emotional challenge than a technical one. Without the emotional support of my husband, my mom, my dad, my sister, and most importantly my son, Tu÷UD , would not have accomplished this feat. Erhan's perseverance, along with all my family's confidence, restored my willingness to fight back at difficult times.
An analog front-end speech processor using the ratio spectrum

We have combined the standard front-end filter bank with a feature extraction process to produce a model that requires dramatically less hardware (or software). This new model, based on the frequency-sampled ratio spectrum, can be interpreted as a small set of constant-Q filters whose center frequencies adapt to locations of high signal energy, The resulting feature vectors are shown to outperform several competing techniques for phoneme recognition, Results from fabricated CMOS analog VLSI circuits illustrate a hardware efficient method to sample the ratio spectrum.