Academic literature on the topic 'Time-encoding of signals'
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Journal articles on the topic "Time-encoding of signals"
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Petkov, Christopher I., and Daniel Bendor. "Neuronal Mechanisms and Transformations Encoding Time-Varying Signals." Neuron 91, no. 4 (August 2016): 718–21. http://dx.doi.org/10.1016/j.neuron.2016.08.006.
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Florescu, Dorian, and Daniel Coca. "A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons." Neural Computation 27, no. 9 (September 2015): 1872–98. http://dx.doi.org/10.1162/neco_a_00764.
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Integrate-and-fire neurons are time encoding machines that convert the amplitude of an analog signal into a nonuniform, strictly increasing sequence of spike times. Under certain conditions, the encoded signals can be reconstructed from the nonuniform spike time sequences using a time decoding machine. Time encoding and time decoding methods have been studied using the nonuniform sampling theory for band-limited spaces, as well as for generic shift-invariant spaces. This letter proposes a new framework for studying IF time encoding and decoding by reformulating the IF time encoding problem as a uniform sampling problem. This framework forms the basis for two new algorithms for reconstructing signals from spike time sequences. We demonstrate that the proposed reconstruction algorithms are faster, and thus better suited for real-time processing, while providing a similar level of accuracy, compared to the standard reconstruction algorithm.
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Rijab, Khalida Shaaban, and Mohammed Abdul Redha Hussien. "Efficient electrocardiogram signal compression algorithm using dual encoding technique." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1529. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1529-1538.
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<span>In medical practices, the storage space of electrocardiogram (ECG) records is a major concern. These records can contain hours of recording, necessitating a large amount of storage space. This problem is commonly addressed by compressing the ECG signal. The proposed work deal with the ECG signal compression method for ECG signals using discrete wavelet transform (DWT). The DWT appeared as powerful tools to compact signals and shows a signal in another time-frequency representation. It is very appropriate in the elimination & removal of redundancy. The ECG signals are decomposed using DWT. After that, the coefficients that result from DWT are threshold depending on the energy packing efficiency (EPE) of the signal. The compression is achieved by the quantization and dual encoding techniques (run-length encoding & Huffman encoding). The dual encoding technique compresses data significantly. The result of the proposed method shows better performance with compression ratios and good quality reconstructed signals. For example, the compression ratio (CR)=20.6, 10.7 and 11.1 with percent root mean square difference (PRD)=1%, 0.9% and 1% for using different DWT (Haar, db2 and FK4) Respectively.</span>
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Cuadrado-Laborde, Christian. "Wavelength-division multiplexing Fresnel transform encoding of time-varying signals." Optical Engineering 47, no. 8 (August 1, 2008): 085004. http://dx.doi.org/10.1117/1.2968216.
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Kuo, Tung-Tai, Rong-Chin Lo, Ren-Guey Lee, Yuan-Hao Chen, and Shang-Hsien Cai. "ACTIVITY COMMAND ENCODING OF CEREBRAL CORTEX M1-EVOKED POTENTIALS OF THE SPRAGUE DAWLEY RAT USING TIME DELAY NEURAL NETWORKS." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (July 29, 2020): 2050034. http://dx.doi.org/10.4015/s1016237220500349.
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Understanding the neurons that transmit messages in the brain while we thinking, feeling, or acting is critical for research on the causes of neurological disease and treatment strategies. This research focuses on the primary motor cortex M1 region, which is involved in human motor function as an activity command center. Understanding this region can help us to determine the mechanism of movement control by the brain, with applicability to other activity mechanisms. A time delay neural network (TDNN) is a suitable model for studying brain signals. TDNN can analyze comprehensive information for a period of successive signals, which is similar to the transmission mechanism of the M1 region. Therefore, this study used a TDNN to build a three-stage encoding system corresponding to the signal type, type arrangement, and time sequence of the brainwave signal from the M1 region and the encoded results were defined as codes, symbols, and commands, respectively. This study aimed to understand the relationship between movement and the M1 region by decoding the signal when the rat undertakes an action. First, we recorded the M1 signal from three rat action types (walk, stand up, and shift head) and performed signal processing. This included using a nonlinear energy operator to find the response points of each action signal. The signals were separated into several sections according to the response time points and independent component analysis was then used to extract the features of the signal (the signal of interest). Finally, we found 16 representative sample signals through a dynamic dimension increasing algorithm to train a three-stage TDNN. We then input the remaining feature signals of interest into the three-stage TDNN for encoding and classification. The results showed an accuracy rate for the three actions of 51.4%, 80.0%, and 54.3%, which means that it is feasible to explain the brain signal of M1 from the free-moving animal using a three-stage TDNN encoding model.
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Adam, Karen, Adam Scholefield, and Martin Vetterli. "Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals." IEEE Transactions on Signal Processing 70 (2022): 105–16. http://dx.doi.org/10.1109/tsp.2021.3133709.
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Tealdi, Simone, Elsi Ferro, Carlo Cosimo Campa, and Carla Bosia. "microRNA-Mediated Encoding and Decoding of Time-Dependent Signals in Tumorigenesis." Biomolecules 12, no. 2 (January 26, 2022): 213. http://dx.doi.org/10.3390/biom12020213.
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microRNAs, pivotal post-transcriptional regulators of gene expression, in the past decades have caught the attention of researchers for their involvement in different biological processes, ranging from cell development to cancer. Although lots of effort has been devoted to elucidate the topological features and the equilibrium properties of microRNA-mediated motifs, little is known about how the information encoded in frequency, amplitude, duration, and other features of their regulatory signals can affect the resulting gene expression patterns. Here, we review the current knowledge about microRNA-mediated gene regulatory networks characterized by time-dependent input signals, such as pulses, transient inputs, and oscillations. First, we identify the general characteristic of the main motifs underlying temporal patterns. Then, we analyze their impact on two commonly studied oncogenic networks, showing how their dysfunction can lead to tumorigenesis.
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Cuadrado-Laborde, C., R. Duchowicz, R. Torroba, and E. E. Sicre. "Fractional Fourier transform dual random phase encoding of time-varying signals." Optics Communications 281, no. 17 (September 2008): 4321–28. http://dx.doi.org/10.1016/j.optcom.2008.04.066.
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Adam, Karen, Adam Scholefield, and Martin Vetterli. "Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding." IEEE Transactions on Signal Processing 68 (2020): 1105–19. http://dx.doi.org/10.1109/tsp.2020.2967182.
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Mahmood, Sawsan D., Maha A. Hutaihit, Tamara A. Abdulrazaq, Azmi Shawkat Abdulbaqi, and Nada Nasih Tawfeeq. "A Telemedicine based on EEG Signal Compression and Transmission." Webology 18, SI05 (October 30, 2021): 894–913. http://dx.doi.org/10.14704/web/v18si05/web18270.
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As a result of RLE and DWT, an effective technique for compressing and transmitting EEG signals was developed in this study. With low percent root-mean-square difference (PRD) values, this algorithm's compression ratio (CR) is high. The life database had 50 EEG patient records. In clinical and research contexts, EEG signals are often recorded at sample rates between 250 and 2000 Hz. New EEG data-collection devices, on the other hand, may record at sampling rates exceeding 20,000 Hz. Time domain (TD) and frequency domain (FD) analysis of EEG data utilizing DWT retains the essential and major features of EEG signals. The thresholding and quantization of EEG signal coefficients are the next steps in implementing this suggested technique, followed by encoding the signals utilizing RLE, which improves CR substantially. A stable method for compressing EEG signals and transmission based on DWT (discrete wavelet transform) and RLE (run length encoding) is presented in this paper in order to improve and increase the compression of the EEG signals. According to the proposed model, CR, PRD, PRDN (normalized percentage root mean square difference), QS (quality score), and SNR (signal to noise ratio) are averaged over 50 records of EEG data and range from 44.0% to 0.36 percent to 5.87 percent to 143 percent to 3.53 percent to 59 percent, respectively.
Dissertations / Theses on the topic "Time-encoding of signals"
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Devineni, Jaya Kartheek. "Ambient Backscatter Communication Systems: Design, Signal Detection and Bit Error Rate Analysis." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/105041.
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The success of the Internet-of-Things (IoT) paradigm relies on, among other things, developing energy-efficient communication techniques that can enable information exchange among billions of battery-operated IoT devices. With its technological capability of simultaneous information and energy transfer, ambient backscatter is quickly emerging as an appealing solution for this communication paradigm, especially for the links with low data rate requirements. However, many challenges and limitations of ambient backscatter have to be overcome for widespread adoption of the technology in future wireless networks. Motivated by this, we study the design and implementation of ambient backscatter systems, including non-coherent detection and encoding schemes, and investigate techniques such as multiple antenna interference cancellation and frequency-shift backscatter to improve the bit error rate performance of the designed ambient backscatter systems.
First, the problem of coherent and semi-coherent ambient backscatter is investigated by evaluating the exact bit error rate (BER) of the system. The test statistic used for the signal detection is based on the averaging of energy of the received signal samples. It is important to highlight that the conditional distributions of this test statistic are derived using the central limit theorem (CLT) approximation in the literature. The characterization of the exact conditional distributions of the test statistic as non-central chi-squared random variable for the binary hypothesis testing problem is first handled in our study, which is a key contribution of this particular work. The evaluation of the maximum likelihood (ML) detection threshold is also explored which is found to be intractable. To overcome this, alternate strategies to approximate the ML threshold are proposed. In addition, several insights for system design and implementation are provided both from analytical and numerical standpoints.
Second, the highly appealing non-coherent signal detection is explored in the context of ambient backscatter for a time-selective channel. Modeling the time-selective fading as a first-order autoregressive (AR) process, we implement a new detection architecture at the receiver based on the direct averaging of the received signal samples, which departs significantly from the energy averaging-based receivers considered in the literature. For the proposed setup, we characterize the exact asymptotic BER for both single-antenna (SA) and multi-antenna (MA) receivers, and demonstrate the robustness of the new architecture to timing errors. Our results demonstrate that the direct-link (DL) interference from the ambient power source leads to a BER floor in the SA receiver, which the MA receiver can avoid by estimating the angle of arrival (AoA) of the DL. The analysis further quantifies the effect of improved angular resolution on the BER as a function of the number of receive antennas.
Third, the advantages of utilizing Manchester encoding for the data transmission in the context of non-coherent ambient backscatter have been explored. Specifically, encoding is shown to simplify the detection procedure at the receiver since the optimal decision rule is found to be independent of the system parameters. Through extensive numerical results, it is further shown that a backscatter system with Manchester encoding can achieve a signal-to-noise ratio (SNR) gain compared to the commonly used uncoded direct on-off keying (OOK) modulation, when used in conjunction with a multi-antenna receiver employing the direct-link cancellation.
Fourth, the BER performance of frequency-shift ambient backscatter, which achieves the self-interference mitigation by spatially separating the reflected backscatter signal from the impending source signal, is investigated. The performance of the system is evaluated for a non-coherent receiver under slow fading in two different network setups: 1) a single interfering link coming from the ambient transmission occurring in the shifted frequency region, and 2) a large-scale network with multiple interfering signals coming from the backscatter nodes and ambient source devices transmitting in the band of interest. Modeling the interfering devices as a two dimensional Poisson point process (PPP), tools from stochastic geometry are utilized to evaluate the bit error rate for the large-scale network setup.Doctor of Philosophy
The emerging paradigm of Internet-of-Things (IoT) has the capability of radically transforming the human experience. At the heart of this technology are the smart edge devices that will monitor everyday physical processes, communicate regularly with the other nodes in the network chain, and automatically take appropriate actions when necessary. Naturally, many challenges need to be tackled in order to realize the true potential of this technology. Most relevant to this dissertation are the problems of powering potentially billions of such devices and enabling low-power communication among them. Ambient backscatter has emerged as a useful technology to handle the aforementioned challenges of the IoT networks due to its capability to support the simultaneous transfer of information and energy. This technology allows devices to harvest energy from the ambient signals in the environment thereby making them self-sustainable, and in addition provide carrier signals for information exchange. Using these attributes of ambient backscatter, the devices can operate at very low power which is an important feature when considering the reliability requirements of the IoT networks. That said, the ambient backscatter technology needs to overcome many challenges before its widespread adoption in IoT networks. For example, the range of backscatter is limited in comparison to the conventional communication systems due to self-interference from the power source at a receiver. In addition, the probability of detecting the data in error at the receiver, characterized by the bit error rate (BER) metric, in the presence of wireless multipath is generally poor in ambient backscatter due to double path loss and fading effects observed for the backscatter link. Inspired by this, the aim of this dissertation is to come up with new architecture designs for the transmitter and receiver devices that can improve the BER performance. The key contributions of the dissertation include the analytical derivations of BER which provide insights on the system design and the main parameters impacting the system performance. The exact design of the optimal detection technique for a communication system is dependent on the channel behavior, mainly the time-varying nature in the case of a flat fading channel. Depending on the mobility of devices and scatterers present in the wireless channel, it can either be described as time-selective or time-nonselective. In the time-nonselective channels, coherent detection that requires channel state information (CSI) estimation using pilot signals can be implemented for ambient backscatter. On the other hand, non-coherent detection is preferred when the channel is time-selective since the CSI estimation is not feasible in such scenarios. In the first part of this dissertation, we analyze the performance of ambient backscatter in a point-to-point single-link system for both time-nonselective and time-selective channels. In particular, we determine the BER performance of coherent and non-coherent detection techniques for ambient backscatter systems in this line of work. In addition, we investigate the possibility of improving the BER performance using multi-antenna and coding techniques. Our analyses demonstrate that the use of multi-antenna and coding can result in tremendous improvement of the performance and simplification of the detection procedure, respectively. In the second part of the dissertation, we study the performance of ambient backscatter in a large-scale network and compare it to that of the point-to-point single-link system. By leveraging tools from stochastic geometry, we analytically characterize the BER performance of ambient backscatter in a field of interfering devices modeled as a Poisson point process.
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Rudresh, Sunil. "Sampling of Structured Signals: Techniques and Imaging Applications." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4616.
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The celebrated Shannon sampling theorem is a key mathematical tool that allows one to seamlessly switch between the continuous-time and discrete-time representations of bandlimited signals. Sampling and reconstruction of signals that are not bandlimited has been addressed within several sampling frameworks, each suitably designed to accommodate a particular class of signals. The design of these sampling frameworks stems from the careful observation of the implicit structure present in the signals. My thesis focuses on the sampling of a class of signals called finite-rate-of-innovation (FRI) signals --- these signals are not necessarily bandlimited, but are completely specified by a finite number of parameters per unit interval of time. In the case of FRI sampling, we consider signals that are a sum-of-weighted and time-shifted (SWTS) pulses, asymmetric pulse trains, and modulated signals. We also consider sampling of FRI signals that are 2-D counterparts of the 1-D FRI signals of the SWTS form. Further, we address two alternatives to the uniform sampling mechanism: (i) time-encoding of FRI signals, which is a neuromorphic sampling scheme that results in nonuniformly spaced samples; and (ii) unlimited sampling of signals, which involves reconstruction of signal from its modulo measurements. We also demonstrate super-resolution reconstruction in imaging applications such as ultrasound, sonar, and ground penetrating radar.
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Kurchuk, Mariya. "Signal Encoding and Digital Signal Processing in Continuous Time." Thesis, 2011. https://doi.org/10.7916/D85T3SFB.
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This work investigates signal encoding in, and architectures of, digital signal processing systems that function in continuous time (CT). Unlike conventional digital signal processors (DSPs), which rely on a clock to dictate the sampling times of an analog-to-digital converter (ADC) and to provide the tap delay timing, CT DSPs function entirely in continuous time, without a sampling or a synchronizing clock. The samples of a CT DSP system are generated and processed only when some measure of the input signal crosses a predetermined threshold. The effective sampling rate and the dynamic power dissipation of a CT digital system automatically adapt to the activity of the input signal. The properties of signals sampled in continuous time are investigated in this thesis. A technique for reducing the effective sampling rate of a CT system is presented, in which the digital signal encoding is varied by adjusting the resolution according to a property of the input. A variable-resolution system leads to a decrease in the number of samples generated, a reduction in the power dissipation and a reduction in the effective chip area of a CT DSP, all without sacrificing in-band performance. The properties of several asynchronous signal-driven sampling techniques are analyzed and compared. The architecture and signal encoding of CT DSPs for signals in the lower gigahertz frequency range are investigated, with consideration of speed and accuracy limitations in the context of submicron CMOS technologies. A per-edge digital signal encoding technique is developed, which bypasses timing problems of processing high-speed digital signals; the properties of per-edge encoded signals are discussed. The design considerations of a low-resolution per-edge-encoded gigahertz-range CT DSP are discussed and an implementation for a possible application is detailed. A prototype chip has been fabricated in ST 65 nm CMOS technology, which has a compact processor core area of 0.073 mm^2. The implemented CT digital processor achieves SNDR of over 20 dB with 3 bits of resolution and a maximum usable -3 dB bandwidth of 0.8 GHz to 3.2 GHz. The processor can be configured as a one-tap to six-tap CT FIR filter and has an active power dissipation that varies from 0.27 mW to 9.5 mW, depending on the amplitude and frequency of the input signal.
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Sharma, Neeraj Kumar. "Information-rich Sampling of Time-varying Signals." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/4126.
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Confrontation with signal non-stationarity is a rule rather than an exception in the analysis of natural signals, such as speech, animal vocalization, music, bio-medical, atmospheric, ans seismic signals. Interestingly, our auditory system analyzes signal non-stationarity to trigger our perception. It does this with a performance which is unparalleled when compared to any man-made sound analyzer. Non-stationary signal analysis is a fairly challenging problem in the expanse of signal processing. Conventional approaches to analyze non-stationary signals are based on short-time quasi- stationary assumptions. Typically, short-time signal segments are analyzed using one of several transforms, such as Fourier, chirplets, and wavelets, with a predefined basis. However, the quasi-stationary assumption is known to be a serious limitation in recognizing fine temporal and spectral variations in natural signals. An accurate analysis of embedded variations can provide for more insightful understanding of natural signals.
Motivated from the sensory mechanisms associated with the peripheral auditory system, this thesis proposes an alternate approach to analyze non-stationary signals. The approach builds on the intuition (and findings from auditory neuroscience literature) that a sequence of zero-crossings (ZCs) of a sine -wave provides its frequency information. Building over this, we hypothesize that sampling an arbitrary signal at some signal specific time instants, instead of uniform Nyquist-rate sampling, can obtain a compact and informative dataset for representation of the signal. The information-richness of the dataset can be quantified by the accuracy to characterize the time-varying attributes of the signal using the sample dataset.
We systematically analyze this hypothesis for synthetic signals modeled by time-varying sinusoids and their additive mixtures. A restricted but rich class of non-stationary signals can be modeled using time-varying sinusoids. These sinusoids are characterized by their instantaneous-amplitude (IA) and instantaneous -frequency (IF) variations. It is shown that using ZCs of the signal and its higher-order derivatives, referred to as higher-order ZCs (HoZCs), we can obtain an accurate estimate of IA and IF variations of the sinusoids contained in the signal. The estimation is verified on synthetic signals and natural signal recordings of vocals and birdsong. On comparison of the approach with empirical mode decomposition, a popular technique for non-stationary signal analysis, and we show that the proposed approach has both improved precision and resolution.
Building on the above finding on information-richness in the HoZCs instant, we evaluate signal reconstruction using this dataset. The sampling density of this dataset is time-varying in a manner adapting to the temporally evolving spectral content of the signal. Reconstruction is evaluated for speech and audio signals. It is found that for the same number of captured samples, HoZCs corresponding to the first derivative of the signal (extrema samples) provide maximum information compared to other derivatives. This is found to be true even in a comparison of signals reconstructed from an equal number of randomly sampled measurements.
Based on these ideas we develop an analysis-modification-synthesis technique for a purely non-stationary modeling of speech signals. This is unlike the existing quasi-stationary analysis techniques. Instead, we propose to model the time- varying quasi -harmonic nature of speech signals. The proposed technique is not constrained by signal duration which helps to avoid blocking artifacts, and at the same time also provides fine temporal resolution of the time-varying attributes. The objective and subjective evaluations show that the technique has better naturalness post modification. It allows controlled modification of speech signals, and can provide for synthesizing novel speech stimuli for probing perception.
Books on the topic "Time-encoding of signals"
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Kurchuk, Mariya. Signal Encoding and Digital Signal Processing in Continuous Time. [New York, N.Y.?]: [publisher not identified], 2011.
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and, Bruno. A Multisensory Perspective. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198725022.003.0001.
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Perception may be defined as the cognitive process that lets us know what is out there, based on incoming sensory signals. Standard textbook accounts often emphasize five modular ‘senses’ encoding such signals. In the perspective presented in this book, instead, perception is inherently multisensory and linked to exploratory action. Perceptual processes do not merely encode incoming sensory signals, they actively explore the environment, seeking informative stimulation from potential multisensory sources and they combine available signals through several multisensory interactions. Studying perception within a multisensory, rather than modular perspective, requires a systemic approach, and this book illustrates how this notion can be successfully applied to eight domains of perception in natural conditions: knowing our own body, controlling its movements, perceiving inanimate objects, perceiving edible objects, understanding the intriguing phenomenon known as synaesthesia, attending to objects in multisensory conditions, perceiving space, and perceiving time.
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Paton, Susana, Enrique Prefasi, Dietmar Straeussnigg, Luis Hernandez, and Pieter Rombouts. Time Encoding Circuits and Systems for Data Conversion and Signal Processing. Elsevier Science & Technology Books, 2019.
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Lopes da Silva, Fernando H., and Eric Halgren. Neurocognitive Processes. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0048.
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Transmembrane neuronal currents that embody cognition in the cortex produce magnetoencephalographic and electroencephalographic signals. Frequency-domain analysis reveals standard rhythms with consistent topography, frequency, and cognitive correlates. Time-domain analysis reveals average event-related potentials and field (ERP/ERF) components with consistent topography, latency, and cognitive correlates. Standard rhythms and ERP/ERF components underlie perceiving stimuli; evaluating whether stimuli match predictions, and taking appropriate action when they do not; encoding stimuli to permit semantic processing and then accessing lexical representations and assigning syntactic roles; maintaining information in primary memory; preparing to take an action; and closing processing of an event–response sequence. Sustained mental processes are associated with theta and gamma. Consolidating memories appears to occur mainly during replay of specific firing patterns during sleep spindles and slow oscillations. Biophysical, neuroanatomical, and neurophysiological factors interact to render cognitive rhythms and components particularly sensitive to the large-scale modulatory processes that sequence and integrate higher cortical processing.
Book chapters on the topic "Time-encoding of signals"
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Gielen, Georges, Luis Hernandez-Corporales, and Pieter Rombouts. "Time Based and VCO-ADCs from a Signal Processing Perspective." In Time-encoding VCO-ADCs for Integrated Systems-on-Chip, 3–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88067-5_1.
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Roy, Rohan Basu, Arani Roy, Amitava Mukherjee, Alekhya Ghosh, Soham Bhattacharyya, and Mrinal K. Naskar. "Sparse Encoding Algorithm for Real-Time ECG Compression." In Recent Trends in Signal and Image Processing, 31–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8863-6_4.
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Yolcu, Cem, Magnus Herberthson, Carl-Fredrik Westin, and Evren Özarslan. "Magnetic Resonance Assessment of Effective Confinement Anisotropy with Orientationally-Averaged Single and Double Diffusion Encoding." In Mathematics and Visualization, 203–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_10.
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AbstractPorous or biological materials comprise a multitude of micro-domains containing water. Diffusion-weighted magnetic resonance measurements are sensitive to the anisotropy of the thermal motion of such water. This anisotropy can be due to the domain shape, as well as the (lack of) dispersion in their orientations. Averaging over measurements that span all orientations is a trick to suppress the latter, thereby untangling it from the influence of the domains’ anisotropy on the signal. Here, we consider domains whose anisotropy is modeled as being the result of a Hookean (spring) force, which has the advantage of having a Gaussian diffusion propagator while still confining the spatial range for the diffusing particles. In fact, this confinement model is the effective model of restricted diffusion when diffusion is encoded via gradients of long durations, making the model relevant to a broad range of studies aiming to characterize porous media with microscopic subdomains. In this study, analytical expressions for the powder-averaged signal under this assumption are given for so-called single and double diffusion encoding schemes, which sensitize the MR signal to the diffusive displacement of particles in, respectively, one or two consecutive time intervals. The signal for one-dimensional diffusion is shown to exhibit power-law dependence on the gradient strength while its coefficient bears signatures of restricted diffusion.
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"Compression of Bursty Signals Using Time Encoding Modulator." In International Conference on Software Technology and Engineering, 3rd (ICSTE 2011), 159–64. ASME Press, 2011. http://dx.doi.org/10.1115/1.859797.paper23.
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Festag, Sven, and Cord Spreckelsen. "Semantic Anomaly Detection in Medical Time Series." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210059.
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The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based on recurrent neural networks with gated recurrent units were used for the semantic encoding of such time frames. A subsequent cluster analysis conducted in the code space served as the decision mechanism labelling samples as anomalies or normal intervals, respectively. The cluster ensemble method called cluster-based similarity partitioning proved itself well suited for this task when used in combination with density-based spatial clustering of applications with noise. The best performing system reached an adjusted Rand index of 0.11 on real-world ECG signals labelled by medical experts. This corresponds to a precision and recall regarding the detection task of around 0.72. The new general approach outperformed several state-of-the-art outlier recognition methods and can be applied to all kinds of (medical) time series data. It can serve as a basis for more specific detectors that work in an unsupervised fashion or that are partially guided by medical experts.
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Katsigiannis, Stamos, Georgios Papaioannou, and Dimitris Maroulis. "A Real-Time Video Encoding Scheme Based on the Contourlet Transform." In Design and Architectures for Digital Signal Processing. InTech, 2013. http://dx.doi.org/10.5772/51735.
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Witschey, Walter RT, and Michael Markl. "Blood flow and phase contrast CMR." In The EACVI Textbook of Cardiovascular Magnetic Resonance, edited by Massimo Lombardi, Sven Plein, Steffen Petersen, Chiara Bucciarelli-Ducci, Emanuela R. Valsangiacomo Buechel, Cristina Basso, and Victor Ferrari, 146–63. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198779735.003.0018.
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Flow-sensitive cardiovascular magnetic resonance (CMR) is a widespread non-invasive imaging method for the clinical evaluation of blood flow in cardiovascular disease. The basic principle of phase contrast magnetic resonance imaging (MRI) is the use of bipolar gradients to encode blood velocity in the magnetic resonance (MR) signal phase. The most common type of flow-encoded scan two-dimensional (2D) cine phase contrast CMR with single-direction velocity encoding is clinically used to quantify cardiovascular flow and velocities. Trade-offs between resolution (temporal and spatial) and acquisition time are illustrated in the context of patient examination, considering high-velocity jet flow, patient breath-hold duration, respiratory motion artefacts, and patient comfort. In addition, the chapter describes how the velocity-to-noise ratio and aliasing behaviour of flow measurements are affected by the velocity-encoding sensitivity (VENC). An advantage of phase contrast MR is that flow encoding may be performed in all three spatial dimensions, improving peak velocity measurement accuracy. Several clinical applications (aortic stenosis, coarctation, and ventricular shunting) and best practices are explained in detail with illustrations. Analysis and post-processing of phase contrast data are summarized. The progressive development of advanced phase contrast techniques is discussed by adding incremental complexity, starting with 2D phase contrast (2D spatial and one-dimensional velocity) and ending with four-dimensional flow encoding (three-dimensional spatial and velocity). Methods to accelerate phase contrast, such as parallel imaging, are briefly discussed. Finally, the chapter concludes with a summary of emerging topics for accelerated scanning and special applications such as compressed sensing, real-time phase contrast, and ultra-short echo time imaging.
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Céard-Falkenberg, Felix, Konstantin Kuznetsov, Alexander Prange, Michael Barz, and Daniel Sonntag. "pEncode: A Tool for Visualizing Pen Signal Encodings in Real-Time." In HHAI2022: Augmenting Human Intellect. IOS Press, 2022. http://dx.doi.org/10.3233/faia220217.
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Many features have been proposed for encoding the input signal from digital pens and touch-based interaction. They are widely used for analyzing and classifying handwritten texts, sketches, or gestures. Although they are well defined mathematically, many features are non-trivial and therefore difficult to understand for a human. In this paper, we present an application that visualizes a subset from 114 digital pen features in real-time while drawing. It provides an easy-to-use interface that allows application developers and machine learning practitioners to learn how digital pen features encode their inputs, helps in the feature selection process, and enables rapid prototyping of sketch and gesture classifiers.
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Rosu, Marius, and Sever Pasca. "WBAN Based Long Term ECG Monitoring." In Wearable Technologies, 952–71. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch043.
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Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.
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Kamble, Shailesh D., Nileshsingh V. Thakur, and Preeti R. Bajaj. "Fractal Coding Based Video Compression Using Weighted Finite Automata." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, 232–52. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch011.
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Main objective of the proposed work is to develop an approach for video coding based on Fractal coding using the weighted finite automata (WFA). The proposed work only focuses on reducing the encoding time as this is the basic limitation why the Fractal coding not becomes the practical reality. WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image based on the idea of fractal that the image has self-similarity in itself. The plane WFA (applied on every frame), and Plane FC (applied on every frame) coding approaches are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, Traffic, Paris, Bus, Akiyo, Mobile, Suzie etc. and on the recorded video, namely, Geometry and Circle. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio, compression percentage, bits per pixel and Peak Signal to Noise Ratio (PSNR). Though the initial number of states is 256 for every frame of all the types of videos, but we got the different number of states for different frames and it is quite obvious due to minimality of constructed WFA for respective frame. Based on the obtained results, it is observed that the number of states is more in videos namely, Traffic, Bus, Paris, Mobile, and Akiyo, therefore the reconstructed video quality is good in comparison with other videos namely, Circle, Suzie, and Geometry.
Conference papers on the topic "Time-encoding of signals"
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Rzepka, Dominik, Dariusz Koscielnik, and Marek Miskowicz. "Clockless signal-dependent compressive sensing of multitone signals using time encoding machine." In 2017 3rd International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP). IEEE, 2017. http://dx.doi.org/10.1109/ebccsp.2017.8022811.
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Kamath, Abijith Jagannath, and Chandra Sekhar Seelamantula. "Multichannel Time-Encoding of Finite-Rate-of-Innovation Signals." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096150.
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Shevchenko, A., V. Zamyatin, and I. Bondarenko. "Impulse Formation by Spatial-Time Phase Encoding." In 2006 3rd International Conference on Ultrawideband and Ultrashort Impulse Signals. IEEE, 2006. http://dx.doi.org/10.1109/uwbus.2006.307213.
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Jutamulia, Suganda, Shinji Toyoda, Akihito Fujita, and Eiichi Ito. "Real-time analog electronic pseudocolor encoding based on an optical processing method." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.tuaa3.
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Pseudocolor display is developed to increase the visually distinguishable levels. Pseudocolor display can be generated by an electronic digital system or an optical processor.1,2 The principle of the optical method is as follows: Positive and Negative images of the gray-level input are en coded with two primary colors, for example, red and blue. Then the product of the positive image and the negative image is encoded with the third primary color, in this case, green. The superim position of these three color-encoded images results in a pseudocolor image of the gray-level input. We develop an analog electronic system in which a monochrome gray-level video signal is directly transformed into red, green, and blue video signals similar to the optical method mentioned above. If the gray-level intensity signal is denoted by I(t), where 0 ≤ I(t) ≤ 1, color signals are generated as follows: R(t) = I(t), B(t)= 1−I(t), and G(t) = I(t)[1−I(t)], where R(t), B(t), and G(t) are red, blue, and green signals, respectively.
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Adam, Karen, Adam Scholefield, and Martin Vetterli. "Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682361.
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Kromka, Jozef, Ondrej Kovac, and Jan Saliga. "Lossless real-time signal encoding for two-channel signals: A case study on ECG." In 26th IMEKO TC4 International Symposium and 24th International Workshop on ADC/DAC Modelling and Testing. Budapest: IMEKO, 2023. http://dx.doi.org/10.21014/tc4-2023.06.
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Kamath, Abijith Jagannath, and Chandra Sekhar Seelamantula. "Differentiate-and-Fire Time-Encoding of Finite-Rate-of-Innovation Signals." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746159.
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Alexandru, Roxana, and Pier Luigi Dragotti. "Time encoding and decoding of multidimensional signals with finite rate of innovation." In 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf53345.2021.9723165.
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Kong, Xiangming, Peter Petre, Roy Matic, Anna C. Gilbert, and Martin J. Strauss. "An analog-to-information converter for wideband signals using a time encoding machine." In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE). IEEE, 2011. http://dx.doi.org/10.1109/dsp-spe.2011.5739250.
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"Optical encoding and multiplexing of detector signals with dual threshold time-over-threshold." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829194.
Full textReports on the topic "Time-encoding of signals"
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Elizur, Abigail, Amir Sagi, Gideon Hulata, Clive Jones, and Wayne Knibb. Improving Crustacean Aquaculture Production Efficiencies through Development of Monosex Populations Using Endocrine and Molecular Manipulations. United States Department of Agriculture, June 2010. http://dx.doi.org/10.32747/2010.7613890.bard.
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Background Most of Australian prawn aquaculture production is based on P. monodon. However, the Australian industry is under intense competition from lower priced overseas imports. The availability of all-female monosex populations, by virtue of their large size and associated premium prize, will offer competitive advantage to the industry which desperately needs to counteract competitors within this market. As for the redclaw production in Israel, although it is at its infancy, the growers realized that the production of males is extremely advantageous and that such management strategy will change the economic assumptions and performances of this aquaculture to attract many more growers. Original objectives (as in original proposal) Investigating the sex inheritance mechanism in the tiger prawn. Identification of genes expressed uniquely in the androgenic gland (AG) of prawns and crayfish. The above genes and/or their products will be used to localize the AG in the prawn and manipulate the AG activity in both species. Production of monosex populations through AG manipulation. In the prawn, production of all-female populations and in the crayfish, all-male populations. Achievements In the crayfish, the AG cDNA library was further screened and a third AG specific transcript, designated Cq-AG3, had been identified. Simultaneously the two AG specific genes, which were previously identified, were further characterized. Tissue specificity of one of those genes, termed Cq-AG2, was demonstrated by northern blot hybridization and RNA in-situ hybridization. Bioinformatics prediction, which suggested a 42 amino acid long signal anchor at the N-terminus of the deduced Cq-AG2, was confirmed by immunolocalization of a recombinant protein. Cq-IAG's functionality was demonstrated by dsRNA in-vivo injections to intersex crayfish. Cq-IAGsilencing induced dramatic sex-related alterations, including male feature feminization, reduced sperm production, extensive testicular apoptosis, induction of the vitellogeningene expression and accumulation of yolk proteins in the ovaries. In the prawn, the AG was identified and a cDNA library was created. The putative P. monodonAG hormone encoding gene (Pm-IAG) was identified, isolated and characterized for time of expression and histological localization. Implantation of the AG into prawn post larvae (PL) and juveniles resulted in phenotypic transformation which included the appearance of appendix masculina and enlarged petasma. The transformation however did not result in sex change or the creation of neo males thus the population genetics stage to be executed with Prof. Hulata did not materialized. Repeated AG implantation is currently being trialed. Major conclusions and Implications, both scientific and agricultural Cq-IAG's involvement in male sexual differentiation had been demonstrated and it is strongly suggested that this gene encodes an AG hormone in this crayfish. A thorough screening of the AG cDNA library shows Cq-IAG is the prominent transcript within the library. However, the identification of two additional transcripts hints that Cq-IAG is not the only gene mediating the AG effects. The successful gene silencing of Cq-IAG, if performed at earlier developmental stages, might accomplish full and functional sex reversal which will enable the production of all-male crayfish populations. Pm-IAG is likely to play a similar role in prawns. It is possible that repeated administration of the AG into prawn will lead to the desired full sex reversal, so that WZ neo males, crossed with WZ females can result in WW females, which will form the basis for monosex all-female population.
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Harman, Gary E., and Ilan Chet. Enhancement of plant disease resistance and productivity through use of root symbiotic fungi. United States Department of Agriculture, July 2008. http://dx.doi.org/10.32747/2008.7695588.bard.
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The objectives of the project were to (a) compare effects ofT22 and T-203 on growth promotion and induced resistance of maize inbred line Mol7; (b) follow induced resistance of pathogenesis-related proteins through changes in gene expression with a root and foliar pathogen in the presence or absence of T22 or T-203 and (c) to follow changes in the proteome of Mol? over time in roots and leaves in the presence or absence of T22 or T-203. The research built changes in our concepts regarding the effects of Trichoderma on plants; we hypothesized that there would be major changes in the physiology of plants and these would be reflected in changes in the plant proteome as a consequence of root infection by Trichoderma spp. Further, Trichoderma spp. differ in their effects on plants and these changes are largely a consequence of the production of different elicitors of elicitor mixtures that are produced in the zone of communication that is established by root infection by Trichoderma spp. In this work, we demonstrated that both T22 and T-203 increase growth and induce resistance to pathogens in maize. In Israel, it was shown that a hydrophobin is critical for root colonization by Trichoderma strains, and that peptaibols and an expansin-like protein from Ttrichoderma probably act as elicitors of induced resistance in plants. Further, this fungus induces the jasmonate/ethylene pathway of disease resistance and a specific cucumber MAPK is required for transduction of the resistance signal. This is the first such gene known to be induced by fungal systems. In the USA, extensive proteomic analyses of maize demonstrated a number of proteins are differentially regulated by T. harzianum strain T22. The pattern of up-regulation strongly supports the contention that this fungus induces increases in plant disease resistance, respiratory rates and photosynthesis. These are all very consistent with the observations of effects of the fungus on plants in the greenhouse and field. In addition, the chitinolytic complex of maize was examined. The numbers of maize genes encoding these enzymes was increased about 3-fold and their locations on maize chromosomes determined by sequence identification in specific BAC libraries on the web. One of the chitinolytic enzymes was determined to be a heterodimer between a specific exochitinase and different endochitinases dependent upon tissue differences (shoot or root) and the presence or absence of T. harzianum. These heterodimers, which were discovered in this work, are very strongly antifungal, especially the one from shoots in the presence of the biocontrol fungus. Finally, RNA was isolated from plants at Cornell and sent to Israel for transcriptome assessment using Affymetrix chips (the chips became available for maize at the end of the project). The data was sent back to Cornell for bioinformatic analyses and found, in large sense, to be consistent with the proteomic data. The final assessment of this data is just now possible since the full annotation of the sequences in the maize Affy chips is just now available. This work is already being used to discover more effective strains of Trichoderma. It also is expected to elucidate how we may be able to manipulate and breed plants for greater disease resistance, enhanced growth and yield and similar goals. This will be possible since the changes in gene and protein expression that lead to better plant performance can be elucidated by following changes induced by Trichoderma strains. The work was in, some parts, collaborative but in others, most specifically transcriptome analyses, fully synergistic.