Showing papers on "Digital signal processing published in 2013"

Discrete Signal Processing on Graphs

Abstract: In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered time samples or pixels. DSP, discrete signal processing, provides a comprehensive, elegant, and efficient methodology to describe, represent, transform, analyze, process, or synthesize these well ordered time or image signals. This paper extends to signals on graphs DSP and its basic tenets, including filters, convolution, z-transform, impulse response, spectral representation, Fourier transform, frequency response, and illustrates DSP on graphs by classifying blogs, linear predicting and compressing data from irregularly located weather stations, or predicting behavior of customers of a mobile service provider.

Low-Power Digital Signal Processing Using Approximate Adders

Abstract: Low power is an imperative requirement for portable multimedia devices employing various signal processing algorithms and architectures. In most multimedia applications, human beings can gather useful information from slightly erroneous outputs. Therefore, we do not need to produce exactly correct numerical outputs. Previous research in this context exploits error resiliency primarily through voltage overscaling, utilizing algorithmic and architectural techniques to mitigate the resulting errors. In this paper, we propose logic complexity reduction at the transistor level as an alternative approach to take advantage of the relaxation of numerical accuracy. We demonstrate this concept by proposing various imprecise or approximate full adder cells with reduced complexity at the transistor level, and utilize them to design approximate multi-bit adders. In addition to the inherent reduction in switched capacitance, our techniques result in significantly shorter critical paths, enabling voltage scaling. We design architectures for video and image compression algorithms using the proposed approximate arithmetic units and evaluate them to demonstrate the efficacy of our approach. We also derive simple mathematical models for error and power consumption of these approximate adders. Furthermore, we demonstrate the utility of these approximate adders in two digital signal processing architectures (discrete cosine transform and finite impulse response filter) with specific quality constraints. Simulation results indicate up to 69% power savings using the proposed approximate adders, when compared to existing implementations using accurate adders.

Statistical Signal Processing

Abstract: This thesis proposes an alternate paradigm for designing computers using continuoustime analog circuits. Digital computation sacrifices continuous degrees of freedom. A principled approach to recovering them is to view analog circuits as propagating probabilities in a message passing algorithm. Within this framework, analog continuous-time circuits can perform robust, programmable, high-speed, low-power, cost-effective, statistical signal processing. This methodology will have broad application to systems which can benefit from low-power, high-speed signal processing and offers the possibility of adaptable/programmable high-speed circuitry at frequencies where digital circuitry would be cost and power prohibitive. Many problems must be solved before the new design methodology can be shown to be useful in practice: Continuous-time signal processing is not well understood. Analog computational circuits known as “soft-gates” have been previously proposed, but a complementary set of analog memory circuits is still lacking. Analog circuits are usually tunable, rarely reconfigurable, but never programmable. The thesis develops an understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time, and explores the use of linear and nonlinear circuits for analog memory. An exemplary embodiment called the Noise Lock Loop (NLL) using these design primitives is demonstrated to perform direct-sequence spread-spectrum acquisition and tracking functionality and promises order-of-magnitude wins over digital implementations. A building block for the construction of programmable analog gate arrays, the “soft-multiplexer” is also proposed. Thesis Supervisor: Neil Gershenfeld Title: Associate Professor

Indicia reading system employing digital gain control

Abstract: A scanning code symbol reading system includes an analog scan data signal processor for producing digitized data signals, wherein during each laser beam scanning cycle, a light collection and photo-detection module generates an analog scan data signal corresponding to a laser scanned code symbol, an analog scan data signal processor/digitizer processes the analog scan data signal to generate digital data signals corresponding thereto, and a synchronized digital gain control module automatically processes the digitized data signals in response to start of scan (SOS) signals generated by a SOS detector. The synchronized digital gain control module generates digital control data which is transmitted to the analog scan data signal processor for use in controlling the gain of a signal processing stage in the light collection and photo-detection module and/or analog scan data signal processor, during the corresponding laser beam scanning cycle.

Digital Signal Processing: Fundamentals and Applications

Abstract: Digital Signal Processing, Second Edition enables electrical engineers and technicians in the fields of biomedical, computer, and electronics engineering to master the essential fundamentals of DSP principles and practice. Many instructive worked examples are used to illustrate the material, and the use of mathematics is minimized for easier grasp of concepts. As such, this title is also useful to undergraduates in electrical engineering, and as a reference for science students and practicing engineers. The book goes beyond DSP theory, to show implementation of algorithms in hardware and software. Additional topics covered include adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as PCM, u-law, ADPCM, and multi-rate DSP and over-sampling ADC. New to this edition: MATLAB projects dealing with practical applications added throughout the bookNew chapter (chapter 13) covering sub-band coding and wavelet transforms, methods that have become popular in the DSP fieldNew applications included in many chapters, including applications of DFT to seismic signals, electrocardiography data, and vibration signalsAll real-time C programs revised for the TMS320C6713 DSKCovers DSP principles with emphasis on communications and control applicationsChapter objectives, worked examples, and end-of-chapter exercises aid the reader in grasping key concepts and solving related problemsWebsite with MATLAB programs for simulation and C programs for real-time DSP

Embedded system for construction of small footprint speech recognition with user-definable constraints

Abstract: Techniques disclosed herein include systems and methods that enable a voice trigger that wakes-up an electronic device or causes the device to make additional voice commands active, without manual initiation of voice command functionality. In addition, such a voice trigger is dynamically programmable or customizable. A speaker can program or designate a particular phrase as the voice trigger. In general, techniques herein execute a voice-activated wake-up system that operates on a digital signal processor (DSP) or other low-power, secondary processing unit of an electronic device instead of running on a central processing unit (CPU). A speech recognition manager runs two speech recognition systems on an electronic device. The CPU dynamically creates a compact speech system for the DSP. Such a compact system can be continuously run during a standby mode, without quickly exhausting a battery supply.

Analog Signal Processing: A Possible Alternative or Complement to Dominantly Digital Radio Schemes

Abstract: Today's exploding demand for faster, more reliable, and ubiquitous radio systems in communication, instrumentation, radar, and sensors poses unprecedented challenges in microwave and millimeter-wave engineering. Recently, the predominant trend has been to place an increasing emphasis on digital signal processing (DSP). However, while offering device compactness and processing flexibility, DSP suffers fundamental drawbacks, such as high-cost analog-digital conversion, high power consumption, and poor performance at high frequencies.

A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals

Abstract: Low-power sensing technologies have emerged for acquiring physiologically indicative patient signals. However, to enable devices with high clinical value, a critical requirement is the ability to analyze the signals to extract specific medical information. Yet given the complexities of the underlying processes, signal analysis poses numerous challenges. Data-driven methods based on machine learning offer distinct solutions, but unfortunately the computations are not well supported by traditional DSP. This paper presents a custom processor that integrates a CPU with configurable accelerators for discriminative machine-learning functions. A support-vector-machine accelerator realizes various classification algorithms as well as various kernel functions and kernel formulations, enabling range of points within an accuracy-versus-energy and -memory trade space. An accelerator for embedded active learning enables prospective adaptation of the signal models by utilizing sensed data for patient-specific customization, while minimizing the effort from human experts. The prototype is implemented in 130-nm CMOS and operates from 1.2 V-0.55 V (0.7 V for SRAMs). Medical applications for EEG-based seizure detection and ECG-based cardiac-arrhythmia detection are demonstrated using clinical data, while consuming 273 μJ and 124 μJ per detection, respectively; this represents 62.4t and 144.7t energy reduction compared to an implementation based on the CPU. A patient-adaptive cardiac-arrhythmia detector is also demonstrated, reducing the analysis-effort required for model customization by 20 t.

Practical Implementation of Spatial Modulation

Abstract: In this work we seek to characterise the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental test bed. Two National Instruments (NI)-PXIe devices are used for the system testing, one for the transmitter and one for the receiver. The digital signal processing that formats the information data in preparation of transmission is described along with the digital signal processing that recovers the information data. In addition, the hardware limitations of the system are also analysed. The average bit error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under line of sight (LoS) channel conditions.

Practical Implementation of Spatial Modulation

Abstract: In this paper, we seek to characterize the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental testbed. Two National Instruments (NI) PXIe devices are used for the system testing: one for the transmitter and one for the receiver. The digital signal processing (DSP) that formats the information data in preparation for transmission is described, along with the DSP that recovers the information data. In addition, the hardware limitations of the system are also analyzed. The average bit-error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under the line-of-sight (LoS) channel conditions.

Compressive Sensing Based Separation of Nonstationary and Stationary Signals Overlapping in Time-Frequency

Abstract: A compressive sensing (CS) approach for nonstationary signal separation is proposed. This approach is motivated by challenges in radar signal processing, including separations of micro-Doppler and main body signatures. We consider the case where the signal of interest assumes sparse representation over a given basis. Other signals present in the data overlap with the desired signal in the time and frequency domains, disallowing conventional windowing or filtering operations to be used for desired signal recovery. The proposed approach uses linear time-frequency representations to reveal the data local behavior. Using the L-statistics, only the time-frequency (TF) points that belong to the desired signal are retained, whereas the common points and others pertaining only to the undesired signals are deemed inappropriate and cast as missing samples. These samples amount to reduced frequency observations in the TF domain. The linear relationship between the measurement and sparse domains permits the application of CS techniques to recover the desired signal without significant distortion. We focus on sinusoidal desired signals with sparse frequency-domain representation but show that the analysis can be straightforwardly generalized to nonsinusoidal signals with known structures. Several examples are provided to demonstrate the effectiveness of the proposed approach.

DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement: A Survey of the State of the Art

Abstract: As speech processing devices like mobile phones, voice controlled devices, and hearing aids have increased in popularity, people expect them to work anywhere and at any time without user intervention However, the presence of acoustical disturbances limits the use of these applications, degrades their performance, or causes the user difficulties in understanding the conversation or appreciating the device A common way to reduce the effects of such disturbances is through the use of single-microphone noise reduction algorithms for speech enhancement The field of single-microphone noise reduction for speech enhancement comprises a history of more than 30 years of research In this survey, we wish to demonstrate the significant advances that have been made during the last decade in the field of discrete Fourier transform domain-based single-channel noise reduction for speech enhancementFurthermore, our goal is to provide a concise description of a state-of-the-art speech enhancement system, and demonstrate the relative importance of the various building blocks of such a system This allows the non-expert DSP practitioner to judge the relevance of each building block and to implement a close-to-optimal enhancement system for the particular application at hand Table of Contents: Introduction / Single Channel Speech Enhancement: General Principles / DFT-Based Speech Enhancement Methods: Signal Model and Notation / Speech DFT Estimators / Speech Presence Probability Estimation / Noise PSD Estimation / Speech PSD Estimation / Performance Evaluation Methods / Simulation Experiments with Single-Channel Enhancement Systems / Future Directions

Speed Sensorless Induction Motor Drive With Predictive Current Controller

Abstract: Today, speed sensorless modes of operation are becoming standard solutions in the area of electric drives. This paper presents a speed sensorless control system of an induction motor with a predictive current controller. A closed-loop estimation system with robustness against motor parameter variation is used for the control approach. The proposed algorithm has been implemented using field-programmable gate arrays (FPGAs) and a floating-point digital signal processor (DSP). Both computational elements have been integrated on a single board SH65L type and interfaced to the power electronic converter, and the use of proper FPGA and DSP optimizes the cost and computational properties. The novelty of the presented solution is the integration of a simple observer for both speed/flux and current control purposes, and the obtained results have been improved in comparison to the previous works. An overview of the test bench consisting of a digital control board, as well as computational algorithms and system benchmarks, is presented. All the tests were performed experimentally for 5.5-kW electric drives.

A 400G optical wireless integration delivery system

Abstract: We experimentally demonstrate a record 400G optical wireless integration system simultaneously delivering 2 × 112 Gb/s two-channel polarization-division-multiplexing 16-ary quadrature amplitude modulation (PDM-16QAM) signal at 37.5 GHz wireless carrier and 2 × 108 Gb/s two-channel PDM quadrature phase shift keying (PDM-QPSK) signal at 100 GHz wireless carrier, adopting two millimeter-wave (mm-wave) frequency bands, two orthogonal antenna polarizations, multiple-input multiple-output (MIMO), photonic mm-wave generation and advanced digital signal processing (DSP). In the case of no fiber transmission, the bit error ratios (BERs) for both the 112 Gb/s PDM-16QAM signal after 1.5 m wireless delivery at 37.5 GHz and the 108 Gb/s PDM-QPSK signal after 0.7 m wireless delivery at 100 GHz are below the pre-forward-error-correction (pre-FEC) threshold of 3.8 × 10−3. To our knowledge, this is the first demonstration of a 400G optical wireless integration system in mm-wave frequency bands and also a capacity record of wireless delivery.

Window Functions and Their Applications in Signal Processing

Abstract: 1. Fourier analysis techniques for signal processing -- 2. Pitfalls in the computation of DFT -- 3. Review of window functions -- 4. Performance comparison of data windows -- 5. Discrete-time windows and their figures of merit -- 6. Time-domain and frequency-domain implementations of windows -- 7. FIR filter design using windows -- 8. Application of windows in spectral analysis -- 9. Applications of windows.

Digital Coherent Receivers for Long-Reach Optical Access Networks

Abstract: The relative merits of coherent-enabled optical access network architectures are explored, with a focus on achievable capacity, reach and split ratio. We review the progress in implementing the particular case of the ultra dense wavelength division multiplexed (UDWDM) passive optical network (PON), and discuss some challenges and solutions encountered. The applicability of digital signal processing (DSP) to coherent receivers in PONs is shown through the design and implementation of parallelized, low-complexity application-specific digital filters. In this work, we focus on mitigating the impact of local oscillator laser (LO) relative intensity noise (RIN) on receiver sensitivity, and propose an algorithm which compensates for this impairment. This phenomenon is investigated theoretically and then experimentally by evaluating the sensitivity of a coherent receiver incorporating different tunable light sources; a low-RIN external cavity laser (ECL) and a monolithically integrated digital supermode distributed Bragg reflector (DS-DBR) laser. It is shown that the RIN of the signal laser does not significantly contribute to the degradation of the receiver sensitivity. Finally, a 10 Gbit/s coherent PON is demonstrated using a DS-DBR laser as the LO laser. It is found that a receiver sensitivity of -38.8 dBm is achievable assuming the use of hard-decision forward error correction.

Power Systems Signal Processing for Smart Grids

Abstract: With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to solve problems in the power system. Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples. Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art and to develop new tools. It presents: - an overview on the power system and electric signals, with description of the basic concepts of DSP commonly found in power system problems - the application of several signal processing tools to problems, looking at power signal estimation and decomposition, pattern recognition techniques, detection of the power system signal variations

ESSENTIA: an open-source library for sound and music analysis

Abstract: We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.

A Scalable Architecture for Distributed Transmit Beamforming with Commodity Radios: Design and Proof of Concept

Abstract: We describe a fully-wireless prototype of distributed transmit beamforming on a software-defined radio platform. Distributed beamforming is a cooperative transmission technique that can achieve orders of magnitude increases in range or energy efficiency of wireless communication systems. However, this technique requires precise synchronization of the radio frequency signal from each transmitter. The significance of our prototype is in demonstrating that this requirement can be satisfied using digital signal processing methods on commodity hardware with low-quality oscillators. Our synchronization approach scales to large numbers of transmitters: each transmitter runs independent algorithms based on periodically transmitted feedback packets from the receiver. A key simplification is the decoupling of the algorithms for frequency locking and beamsteering at each transmitter, even though both processes use the same feedback packets. Frequency locking employs an Extended Kalman filter to track the local oscillator offset between a transmitter and the receiver, using frequency offset measurements based on the feedback packet waveform, while the phase adjustments for beamsteering are determined using a one-bit feedback algorithm based on the feedback packet it payload. Our prototype demonstrates that distributed transmit beamforming can be incorporated into wireless networks without requiring hardware innovations, and provides open-source building blocks for future research and development.

Advanced Optical Communication Systems and Networks

Abstract: Providing straightforward practical guidance, this highly accessible resource presents today's most advanced topics on optical communications and networking. You get the latest details on 5th generation photonic systems that can be readily applied to your projects in the field, Moreover, the book provides valuable, time-saving tools for network simulation and modeling. You find in-depth coverage of optical signal transmission systems and networks. In addition to optical communications fundamentals with detailed description of the optical components and optical signal properties (modulation, propagation and detection), the book includes coverage of a wide range of critical methods and techniques, such as MIMO (multiple-input and multiple-output) by employing spatial modes in few-mode and multicore optical fibers, OFDM (orthogonal frequency-division multiplexing) utilized to enhance the spectral efficiency and to enable elastic optical networking schemes, and advanced modulation and coding schemes to approach the Shannon's channel capacity limit. You find detailed discussions on the basic principles and applications of high-speed digital signal processing. The book also describes the most relevant post-detection compensation techniques, including linear equalizers, adaptive equalizers, maximum-likelihood sequence detectors, blind equalizers, turbo equalizers, digital back-propagation, Wiener filtering, and Volterra series based equalization. Other key topics include advanced concepts on coded-modulation, LDPC-coded turbo equalization, polarization-time coding, spatial-domain-based modulation and coding, and multidimensional signaling. This comprehensive book includes a complete set of problems at the end of each chapter to help you master the material. Supplementary Materials: A solutions manual is available and reserved for instructors only. To request a copy of the Solutions Manual, please fax your request on your departmental letterhead to Chris Stanfa at 781-769-6334.

11 × 5 × 9.3Gb/s WDM-CAP-PON based on optical single-side band multi-level multi-band carrier-less amplitude and phase modulation with direct detection.

Abstract: We propose and demonstrate a novel WDM-CAP-PON based on optical single-side band (OSSB) multi-level multi-band carrier-less amplitude and phase modulation (MM-CAP). To enable high-speed transmission with simplified optical network unit (ONU)-side digital signal processing, 4-level 5 sub-bands CAP-16 is used here, which is generated by the digital to analogue converter (DAC). Optical single-side band (OSSB) technology is applied to extend the transmission distance against the spectrum fading effect. As a proof of concept, the experiment successfully demonstrates 11 WDM channels, 55 sub-bands, for 55 users with 9.3-Gb/s per user (after removing 7% overhead for forward error correction (FEC)) in the downstream over 40-km SMF.

Fully Electronic $E$ -Band Personnel Imager of 2 m $^2$ Aperture Based on a Multistatic Architecture

Abstract: The demand on advanced personnel screening systems led to the development of several active and passive imagers. Among them, active multistatic imaging ensures high image quality and allows fully electronic screening. An active E-band imager is presented in this paper, which measures in real-time and delivers images of 30-dB dynamic range. Imaging of humans is achieved by optimizing the acquisition time using a dedicated digital signal processing solution. This paper introduces the system hardware, the calibration procedure, and several measurement results demonstrating high quality imaging capabilities.

A Framework for Invertible, Real-Time Constant-Q Transforms

Abstract: Audio signal processing frequently requires time-frequency representations and in many applications, a non-linear spacing of frequency bands is preferable. This paper introduces a framework for efficient implementation of invertible signal transforms allowing for non-uniform frequency resolution. Non-uniformity in frequency is realized by applying nonstationary Gabor frames with adaptivity in the frequency domain. The realization of a perfectly invertible constant-Q transform is described in detail. To achieve real-time processing, independent of signal length, slice-wise processing of the full input signal is proposed and referred to as sliCQ transform. By applying frame theory and FFT-based processing, the presented approach overcomes computational inefficiency and lack of invertibility of classical constant-Q transform implementations. Numerical simulations evaluate the efficiency of the proposed algorithm and the method's applicability is illustrated by experiments on real-life audio signals .

System and method for digital signal processing

Abstract: The present invention provides methods and systems for digitally processing audio signals. Some embodiments receive an audio signal and converting it to a digital signal. The gain of the digital signal may be adjusted a first time, using a digital processing device located between a receiver and a driver circuit. The adjusted signal can be filtered with a first low shelf filter. The systems and methods may compress the filtered signal with a first compressor, process the signal with a graphic equalizer, and compress the processed signal with a second compressor. The gain of the compressed signal can be adjusted a second time. These may be done using the digital processing device. The signal may then be output through an amplifier and driver circuit to drive a personal audio listening device. In some embodiments, the systems and methods described herein may be part of the personal audio listening device.

Diffusion Maps for Signal Processing: A Deeper Look at Manifold-Learning Techniques Based on Kernels and Graphs

Abstract: Signal processing methods have significantly changed over the last several decades. Traditional methods were usually based on parametric statistical inference and linear filters. These frameworks have helped to develop efficient algorithms that have often been suitable for implementation on digital signal processing (DSP) systems. Over the years, DSP systems have advanced rapidly, and their computational capabilities have been substantially increased. This development has enabled contemporary signal processing algorithms to incorporate more computations. Consequently, we have recently experienced a growing interaction between signal processing and machine-learning approaches, e.g., Bayesian networks, graphical models, and kernel-based methods, whose computational burden is usually high.

On discrete cosine transform

Abstract: The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

Spectral Efficiency-Adaptive Optical Transmission Using Time Domain Hybrid QAM for Agile Optical Networks

Abstract: We report the transmission of time domain hybrid QAM (TDHQ) signals for agile optical networks. A continuous tradeoff between spectral efficiency and achievable distance by mixing modulation formats including QPSK, 8QAM, and 16QAM is demonstrated in two scenarios: 1) 28 Gbaud non-return-to-zero (NRZ) signal for fixed 50 GHz grid systems; 2) superchannel transmission at date rates of up to 1.15 Tb/s and spectral efficiencies of up to 7.68 b/s/Hz. The TDHQ signal is generated using high-speed digital-to-analog converters (DACs) at the transmitter, and low-complexity digital signal processing (DSP) is proposed for processing the TDHQ signals at the receiver. Moreover, the nonlinearity tolerance of hybrid QAM signals with different configurations is investigated.

Sampling and Sampled-Data Models: The Interface Between the Continuous World and Digital Algorithms

Abstract: Modern signal processing and control algorithms are invariably implemented digitally, yet most real-world systems evolve in continuous time. Hence, the interaction between sampling and the behavior of continuous-time systems is an important ingredient in all real-world signals and systems problems.

SIFER: Scale-Invariant Feature Detector with Error Resilience

Abstract: We present a new method to extract scale-invariant features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed feature detection algorithm on image registration applications over a wide range of testbench conditions.