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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.
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Belkić, Dževad, and Karen Belkić. "Feasibility study for applying the lower-order derivative fast Padé transform to measured time signals." Journal of Mathematical Chemistry 58, no. 1 (November 27, 2019): 146–77. http://dx.doi.org/10.1007/s10910-019-01077-2.
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AbstractMagnetic resonance spectroscopy (MRS), as a powerful and versatile diagnostic modality in physics, chemistry, medicine and other basic and applied sciences, depends critically upon reliable signal processing. It provides time signals by encoding, but cannot quantify on its own. Mathematical methods do so. The signal processor of choice for MRS is the fast Padé transform (FPT). The spectrum in the FPT is the unique polynomial quotient for the given Maclaurin expansion. The parametric FPT (parameter estimator) performs quantification of time signals encoded with MRS by explicitly solving the spectral analysis problem. Thus far, the non-parametric FPT (shape estimator) could not quantify. However, the non-parametric derivative fast Padé transform (dFPT) can quantify despite performing shape estimation alone. The dFPT was successfully benchmarked on synthesized MRS time signals for derivative orders ranging from 1 to 50. It simultaneously improved resolution (by splitting apart tightly overlapped peaks) and enhanced signal-to-noise ratio (by suppressing the background baseline). The same advantageous features of improving both resolution and signal-to-noise ratio are presently found to be upheld with encoded MRS time signals. Moreover, it is demonstrated that the dFPT hugely outperforms the derivative fast Fourier transform even for derivatives of orders as low as four. The clinical implications are discussed.
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Yang, Ziyu, Maoshen Jia, Wenbei Wang, and Jiaming Zhang. "Multi-Stage Encoding Scheme for Multiple Audio Objects Using Compressed Sensing." Cybernetics and Information Technologies 15, no. 6 (December 1, 2015): 135–46. http://dx.doi.org/10.1515/cait-2015-0074.
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Abstract Object-based audio techniques have become common since they provide the flexibility for personalized rendering. In this paper a multi-stage encoding scheme for multiple audio objects is proposed. The scheme is based on intra-object sparsity. In the encoding phase the dominant Time Frequency (TF) instants of all active object signals are extracted and divided into several stages to form the multistage observation signals for transmission. In the decoding phase the preserved TF instants are recovered via Compressed Sensing (CS) technique, and further used for reconstructing the audio objects. The evaluations validated that the proposed encoding scheme can achieve scalable transmission while maintaining perceptual quality of each audio object.
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Tomashevich, Stanislav. "Video encoding with adaptive coding procedure based on multiagent model." Cybernetics and Physics, Volume 8, 2019, Number 2 (September 30, 2019): 83–86. http://dx.doi.org/10.35470/2226-4116-2019-8-2-83-86.
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The paper proposes the use of an adaptive coding algorithm for processing and transmitting video signals. Encoding process of video signals intend to divide the color of each pixel into three components (red, green and blue). The whole video frame could be represented as networked system where each pixel is a independent agent. In this case, agents are understood as a system having any dynamics. Thus, the movement of a pixel is described by a change in the intensity of its RGB components. This approach allows us to consider the process of intensity change as the trajectory of the dynamic system and apply observation and coding methods to it. The proposed approach allows to encode signals in real time without their prior complete analysis. Depending on the coding rate and the nature of the signal, it is possible to save the amount of information transmitted as a result of the algorithm. Observer is used to get estimation of colors signals and multiagent model is used to get to accurate estimation process.
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Kreyndelin, V. B., and L. A. Varukina. "A method of modulating signals with space-time encoding using non-linear iteration algorithm." Radioelectronics and Communications Systems 52, no. 8 (August 2009): 413–18. http://dx.doi.org/10.3103/s0735272709080032.
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Fu, Ziyang, Chen Huang, Li Zhang, Shihui Wang, and Yan Zhang. "Deep Learning Model of Sleep EEG Signal by Using Bidirectional Recurrent Neural Network Encoding and Decoding." Electronics 11, no. 17 (August 24, 2022): 2644. http://dx.doi.org/10.3390/electronics11172644.
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Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time and frequency domains was realized using a convolutional neural network to expand the scope of feature extraction and preserve the original EEG feature information to the maximum extent possible. Finally, the time-series information was mined using the encoding–decoding module of the BiRNN, and the automatic discrimination of the sleep staging of the EEG signal was realized using the SoftMax function. The model was cross-validated using Fpz-Cz single-channel EEG signals from the Sleep-EDF dataset for 19 nights, and the results demonstrated that the proposed model can achieve a high recognition rate and stability.
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Manwani, Amit, Peter N. Steinmetz, and Christof Koch. "The Impact of Spike Timing Variability on the Signal-Encoding Performance of Neural Spiking Models." Neural Computation 14, no. 2 (February 1, 2002): 347–67. http://dx.doi.org/10.1162/08997660252741158.
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It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.
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Cheng, Bing, and Xiaokun Zhu. "A Multiresolution Approximation Theory of Fractal Transform." Fractals 05, supp01 (April 1997): 173–86. http://dx.doi.org/10.1142/s0218348x97000747.
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In this paper, we show that the fractal transform (FT) constitutes a multiresolution approximation to the square-integrable space L2(Td) for d≥1, where T is the interval (-∞,∞). This provides a theoretical basis for the successful applications of the fractal transform algorithms in signal/image encoding. There are many similarities between fractal-based and wavelet-based approximations. However, they are undamentally different from each other in many aspects. Fractal-based multiresolution approximation to signals/images is by a way of self-increasing model complexity, and wavelet-based multiresolution approximation to signals/images is by a way of decomposing data complexity into single (time domain) components.
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Fang, Hong-Wen, and Chih-Cheng Lu. "A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors." Applied Sciences 8, no. 12 (November 24, 2018): 2379. http://dx.doi.org/10.3390/app8122379.
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Cardiovascular diseases are the leading cause of death worldwide. Due to advancements facilitating the integration of electric and adhesive technologies, long-term patch electrocardiogram (ECG) monitors (PEMs) are currently used to conduct daily continuous cardiac function assessments. This paper presents an ECG encoding scheme for joint lossless data compression and heartbeat detection to minimize the circuit footprint size and power consumption of a PEM. The proposed encoding scheme supports two operation modes: fixed-block mode and dynamic-block mode. Both modes compress ECG data losslessly, but only dynamic-block mode supports the heartbeat detection feature. The whole encoding scheme was implemented on a C-platform and tested with ECG data from MIT/BIH arrhythmia databases. A compression ratio of 2.1 could be achieved with a normal heartbeat. Dynamic-block mode provides heartbeat detection accuracy at a rate higher than 98%. Fixed-block mode was also implemented on the field-programmable gate array, and could be used as a chip for using analog-to-digital convertor-ready signals as an operation clock.
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Rajasekaran, Perigaram K., and George R. Doddington. "Method of encoding speech signals involving the extraction of speech formant candidates in real time." Journal of the Acoustical Society of America 92, no. 1 (July 1992): 628. http://dx.doi.org/10.1121/1.404102.
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Louet, Yves, Rami Othman, and Alexandre Skrzypczak. "A Soft-Output STBC Decoder for Aeronautical Telemetry." Journal of Telecommunications and Information Technology 1 (March 31, 2020): 13–20. http://dx.doi.org/10.26636/jtit.2020.138319.
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Alamouti encoding is a well-known space time block encoding technique used to improve the received signal quality in Rayleigh fading channels. In aeronautical telemetry, this encoding technique is applied to shaped offset quadrature phase shift keying tier generation (SOQPSK-TG) modulation in order to handle the two-antenna issue. It is provided for in telemetry-related IRIG standards. In this paper, we propose a unique decoding architecture for Alamouti-encoded SOQPSK-TG signals, taking advantage of pulse amplitude modulation decomposition with soft and hard outputs. We exploit this result to obtain a Viterbi algorithm (VA) for hard decoding and a soft output Viterbi algorithm (SOVA) for soft and hard decoding, with a twofold benefit: operation using one trellis structure, unlike decoders that are based on the 8-waveforms cross-correlated trellis-coded quadrature modulation (XTCQM) approximation, and very attractive bit error rate performance, as well as a complexity trade-off
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Alsaleem, Mona N., Md Saiful Islam, Saad Al-Ahmadi, and Adel Soudani. "Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation." Bioengineering 9, no. 9 (September 16, 2022): 480. http://dx.doi.org/10.3390/bioengineering9090480.
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Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance without the need for using any transformation or handcrafted feature engineering techniques. The proposed scheme uses different kernel sizes to produce the encoded signal by using multiple streams that are passed into a one-dimensional sequence of blocks of a residual convolutional neural network (ResNet) to extract representative features from the input ECG signal. This also allows networks to grow in breadth rather than in depth, thus reducing the computing time by using the parallel processing capability of deep learning networks. We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. Experiments were carried out for a performance evaluation using the publicly available PhysioNet CinC Challenge 2017 dataset. The proposed multiscale encoding scheme outperformed existing deep learning-based methods with an average F1 score of 98.54%, but with a lower network complexity.
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Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "A Signal Complexity-Based Approach for AM–FM Signal Modes Counting." Mathematics 8, no. 12 (December 4, 2020): 2170. http://dx.doi.org/10.3390/math8122170.
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Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.
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Athavale, Yashodhan, and Sridhar Krishnan. "A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health." Sensors 18, no. 9 (September 6, 2018): 2966. http://dx.doi.org/10.3390/s18092966.
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Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.
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Naya, Yuji, He Chen, Cen Yang, and Wendy A. Suzuki. "Contributions of primate prefrontal cortex and medial temporal lobe to temporal-order memory." Proceedings of the National Academy of Sciences 114, no. 51 (November 30, 2017): 13555–60. http://dx.doi.org/10.1073/pnas.1712711114.
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Neuropsychological and neurophysiological studies have emphasized the role of the prefrontal cortex (PFC) in maintaining information about the temporal order of events or items for upcoming actions. However, the medial temporal lobe (MTL) has also been considered critical to bind individual events or items to their temporal context in episodic memory. Here we characterize the contributions of these brain areas by comparing single-unit activity in the dorsal and ventral regions of macaque lateral PFC (d-PFC and v-PFC) with activity in MTL areas including the hippocampus (HPC), entorhinal cortex, and perirhinal cortex (PRC) as well as in area TE during the encoding phase of a temporal-order memory task. The v-PFC cells signaled specific items at particular time periods of the task. By contrast, MTL cortical cells signaled specific items across multiple time periods and discriminated the items between time periods by modulating their firing rates. Analysis of the temporal dynamics of these signals showed that the conjunctive signal of item and temporal-order information in PRC developed earlier than that seen in v-PFC. During the delay interval between the two cue stimuli, while v-PFC provided prominent stimulus-selective delay activity, MTL areas did not. Both regions of PFC and HPC exhibited an incremental timing signal that appeared to represent the continuous passage of time during the encoding phase. However, the incremental timing signal in HPC was more prominent than that observed in PFC. These results suggest that PFC and MTL contribute to the encoding of the integration of item and timing information in distinct ways.
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Blochberger, Matthias, and Franz Zotter. "Particle-filter tracking of sounds for frequency-independent 3D audio rendering from distributed B-format recordings." Acta Acustica 5 (2021): 20. http://dx.doi.org/10.1051/aacus/2021012.
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Six-Degree-of-Freedom (6DoF) audio rendering interactively synthesizes spatial audio signals for a variable listener perspective based on surround recordings taken at multiple perspectives distributed across the listening area in the acoustic scene. Methods that rely on recording-implicit directional information and interpolate the listener perspective without the attempt of localizing and extracting sounds often yield high audio quality, but are limited in spatial definition. Methods that perform sound localization, extraction, and rendering typically operate in the time-frequency domain and risk introducing artifacts such as musical noise. We propose to take advantage of the rich spatial information recorded in the broadband time-domain signals of the multitude of distributed first-order (B-format) recording perspectives. Broadband time-variant signal extraction retrieving direct signals and leaving residuals to approximate diffuse and spacious sounds is less of a quality risk, and likewise is the broadband re-encoding to enhance spatial definition of both signal types. To detect and track direct sound objects in this process, we combine the directional data recorded at the single perspectives into a volumetric multi-perspective activity map for particle-filter tracking. Our technical and perceptual evaluation confirms that this kind of processing enhances the otherwise limited spatial definition of direct-sound objects of other broadband but signal-independent virtual loudspeaker object (VLO) or Vector-Based Intensity Panning (VBIP) interpolation approaches.
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Sidorov, S. M. "Hidden Markov Model of Two-Component System with Group Instantly Replenished Time Reserve." INFORMACIONNYE TEHNOLOGII 27, no. 2 (February 12, 2021): 64–71. http://dx.doi.org/10.17587/it.27.64-71.
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Most systems allow the construction of a semi-Markov model. However, during the operation of the system, full information contained in the state encoding is not always available, but it is possible to obtain some signal (information). Tasks arise to assess the consistency of the model with the received data (signals), to refine the model and its parameters. Such parameters can be characteristics of random values characterizing system operation, time reserve value, etc. The theory of hidden Markov models allows solving these problems. In order to move from a semi-Markov model of the system to its hidden Markov model, it is proposed to first the semi-Markov model merge using a stationary phase merging algorithm. In this paper, on the basis of the semi-Markov model with a common phase state space of a two-component system with a group instantly replenished timereserve, we construct a hidden Markov model of a two-component system with a group instantly replenished time reserve. It is used to evaluate the characteristics and predict the states of the system in question based on the received vector of signals. The influence of the time reserve value on the probability of occurrence of the obtained vector of signals is shown.
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Paluch, Katarzyna, Katarzyna Jurewicz, and Andrzej Wróbel. "Beyond Difference in Reaction Time: Understanding Neuronal Activity during the Preparatory Period of the Decision Process." Journal of Cognitive Neuroscience 33, no. 2 (February 2021): 263–78. http://dx.doi.org/10.1162/jocn_a_01648.
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Even the simplest perceptual tasks are executed with significant interindividual differences in accuracy and RT. In this work, we used the diffusion decision model and multi-electrode EEG signals to study the impact of neuronal activity during the preparatory period on the following decision process in an attention task. Two groups were defined by fast and slow responses during the performance of control trials. A third, control group performed the same experiment but with instructions defining signal for response execution. We observed that the fast-responding group had a shorter duration of nondecision processes (describing both stimulus encoding and response preparation) preceded by lower power of the frontal upper alpha (10–15 Hz) and central beta (21–26 Hz) activities during the preparatory period. To determine whether these differences were followed by a shortening of the early perceptual or late motor process, we analyzed lateralized readiness potential (LRP). The time from LRP onset until response execution (LRP-RT interval) was similar in all three groups, enabling us to interpret shortening of nondecision time as reflecting faster stimulus encoding.
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Śmigiel, Sandra, Krzysztof Pałczyński, and Damian Ledziński. "Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset." Sensors 21, no. 24 (December 7, 2021): 8174. http://dx.doi.org/10.3390/s21248174.
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Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.
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Williams, Leanne M., Andrew H. Kemp, Kim Felmingham, Belinda J. Liddell, Donna M. Palmer, and Richard A. Bryant. "Neural Biases to Covert and Overt Signals of Fear: Dissociation by Trait Anxiety and Depression." Journal of Cognitive Neuroscience 19, no. 10 (October 2007): 1595–608. http://dx.doi.org/10.1162/jocn.2007.19.10.1595.
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Although biases toward signals of fear may be an evolutionary adaptation necessary for survival, heightened biases may be maladaptive and associated with anxiety or depression. In this study, event-related potentials (ERPs) were used to examine the time course of neural responses to facial fear stimuli (versus neutral) presented overtly (for 500 msec with conscious attention) and covertly (for 10 msec with immediate masking to preclude conscious awareness) in 257 nonclinical subjects. We also examined the impact of trait anxiety and depression, assessed using psychometric ratings, on the time course of ERPs. In the total subject group, controlled biases to overtly processed fear were reflected in an enhancement of ERPs associated with structural encoding (120–220 msec) and sustained evaluation persisting from 250 msec and beyond, following a temporo-occipital to frontal topography. By contrast, covert fear processing elicited automatic biases, reflected in an enhancement of ERPs prior to structural encoding (80–180 msec) and again in the period associated with automatic orienting and emotion encoding (230–330 msec), which followed the reverse frontal to temporo-occipital topography. Higher levels of trait anxiety (in the clinical range) were distinguished by a heightened bias to covert fear (speeding of early ERPs), compared to higher depression which was associated with an opposing bias to overt fear (slowing of later ERPs). Anxiety also heightened early responses to covert fear, and depression to overt fear, with subsequent deficits in emotion encoding in each case. These findings are consistent with neural biases to signals of fear which operate automatically and during controlled processing, feasibly supported by parallel networks. Heightened automatic biases in anxiety may contribute to a cycle of hypervigilance and anxious thoughts, whereas depression may represent a “burnt out” emotional state in which evaluation of fear stimuli is prolonged only when conscious attention is allocated.
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Tian, Zuo Xi, Feng Yu, and Zeng Wu Liu. "Designs for Synchronous Data Acquisition of a Distributed System." Applied Mechanics and Materials 239-240 (December 2012): 869–72. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.869.
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To resolve the problem of acquiring multi sensors signals synchronously in a distributed system, a project of distributed synchronous data acquisition based network time server was designed. The system comprised multi distributed sensor units, a centralized control unit and D&C (display and control) center. Each sensor unit was equipped with a data acquisition module. All data from sensor units were concentrated and sent to the D&C center, and the D&C center implemented power supply and management of sensor units via the centralized control unit. To synchronize the data acquisition modules, a network time server was employed in the D&C center. It received standard time information from GPS and outputted the time signal with DCF77-encoding. Each data acquisition module received and decoded DCF77 time signal, obtaining absolute time and synchronizing its time base. Above project was applied successfully in a system comprising 20 distributed sensor units. The results prove the designs feasible.
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Wang, Xinmei, Weifeng Mou, and Huatao Zhu. "Effect of Laser Parameters on Optical Stealth Transmission System Performance." Sensors 21, no. 16 (August 9, 2021): 5358. http://dx.doi.org/10.3390/s21165358.
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The performance of an optical stealth transmission system based on gain-switched laser depends largely on the laser parameters. Modulation frequency, bias current, and modulation current are considered to study the covertness and bit error rate performance of the optical stealth transmission system. According to optical stealth carrier generation with time spreading and all-optical encoding, the stealth signals are derived. A complementary encoding scheme is adopted in the system simulation. The simulation results show that the temporal and spectral characteristics of the generated stealth signal can be changed by adjusting the bias current, modulation current, and modulation frequency. However, there is a trade-off between bit error rate performance and covertness of the stealth channel. Under the premise of error-free transmission, the bias current and modulation frequency should be reduced and the modulation current should be improved to optimize the covertness of the stealth channel.
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Juusola, Mikko, and Gonzalo G. de Polavieja. "The Rate of Information Transfer of Naturalistic Stimulation by Graded Potentials." Journal of General Physiology 122, no. 2 (July 14, 2003): 191–206. http://dx.doi.org/10.1085/jgp.200308824.
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We present a method to measure the rate of information transfer for any continuous signals of finite duration without assumptions. After testing the method with simulated responses, we measure the encoding performance of Calliphora photoreceptors. We find that especially for naturalistic stimulation the responses are nonlinear and noise is nonadditive, and show that adaptation mechanisms affect signal and noise differentially depending on the time scale, structure, and speed of the stimulus. Different signaling strategies for short- and long-term and dim and bright light are found for this graded system when stimulated with naturalistic light changes.
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Macnae, James. "Stripping very low frequency communication signals with minimum shift keying encoding from streamed time-domain electromagnetic data." GEOPHYSICS 80, no. 6 (November 2015): E343—E353. http://dx.doi.org/10.1190/geo2015-0304.1.
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Kalafatovich, Jenifer, Minji Lee, and Seong-Whan Lee. "Decoding declarative memory process for predicting memory retrieval based on source localization." PLOS ONE 17, no. 9 (September 8, 2022): e0274101. http://dx.doi.org/10.1371/journal.pone.0274101.
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Many studies have focused on understanding memory processes due to their importance in daily life. Differences in timing and power spectra of brain signals during encoding task have been linked to later remembered items and were recently used to predict memory retrieval performance. However, accuracies remain low when using non-invasive methods for acquiring brain signals, mainly due to the low spatial resolution. This study investigates the prediction of successful retrieval using estimated source activity corresponding either to cortical or subcortical structures through source localization. Electroencephalogram (EEG) signals were recorded while participants performed a declarative memory task. Frequency-time analysis was performed using signals from encoding and retrieval tasks to confirm the importance of neural oscillations and their relationship with later remembered and forgotten items. Significant differences in the power spectra between later remembered and forgotten items were found before and during the presentation of the stimulus in the encoding task. Source activity estimation revealed differences in the beta band power over the medial parietal and medial prefrontal areas prior to the presentation of the stimulus, and over the cuneus and lingual areas during the presentation of the stimulus. Additionally, there were significant differences during the stimuli presentation during the retrieval task. Prediction of later remembered items was performed using surface potentials and estimated source activity. The results showed that source localization increases classification performance compared to the one using surface potentials. These findings support the importance of incorporating spatial features of neural activity to improve the prediction of memory retrieval.
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Yamanaka, Shuto, Tatsuho Nagatomo, Takefumi Hiraki, Hiroki Ishizuka, and Norihisa Miki. "Machine-Learning-Based Fine Tuning of Input Signals for Mechano-Tactile Display." Sensors 22, no. 14 (July 15, 2022): 5299. http://dx.doi.org/10.3390/s22145299.
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Deducing the input signal for a tactile display to present the target surface (i.e., solving the inverse problem for tactile displays) is challenging. We proposed the encoding and presentation (EP) method in our prior work, where we encoded the target surface by scanning it using an array of piezoelectric devices (encoding) and then drove the piezoelectric devices using the obtained signals to display the surface (presentation). The EP method reproduced the target texture with an accuracy of over 80% for the five samples tested, which we refer to as replicability. Machine learning is a promising method for solving inverse problems. In this study, we designed a neural network to connect the subjective evaluation of tactile sensation and the input signals to a display; these signals are described as time-domain waveforms. First, participants were asked to touch the surface presented by the mechano-tactile display based on the encoded data from the EP method. Then, the participants recorded the similarity of the surface compared to five material samples, which were used as the input. The encoded data for the material samples were used as the output to create a dataset of 500 vectors. By training a multilayer perceptron with the dataset, we deduced new inputs for the display. The results indicate that using machine learning for fine tuning leads to significantly better accuracy in deducing the input compared to that achieved using the EP method alone. The proposed method is therefore considered a good solution for the inverse problem for tactile displays.
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Rozenberg, Liat, Sagi Lotan, and Dan Feldman. "Finding Patterns in Signals Using Lossy Text Compression." Algorithms 12, no. 12 (December 11, 2019): 267. http://dx.doi.org/10.3390/a12120267.
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Whether the source is autonomous car, robotic vacuum cleaner, or a quadcopter, signals from sensors tend to have some hidden patterns that repeat themselves. For example, typical GPS traces from a smartphone contain periodic trajectories such as “home, work, home, work, ⋯”. Our goal in this study was to automatically reverse engineer such signals, identify their periodicity, and then use it to compress and de-noise these signals. To do so, we present a novel method of using algorithms from the field of pattern matching and text compression to represent the “language” in such signals. Common text compression algorithms are less tailored to handle such strings. Moreover, they are lossless, and cannot be used to recover noisy signals. To this end, we define the recursive run-length encoding (RRLE) method, which is a generalization of the well known run-length encoding (RLE) method. Then, we suggest lossy and lossless algorithms to compress and de-noise such signals. Unlike previous results, running time and optimality guarantees are proved for each algorithm. Experimental results on synthetic and real data sets are provided. We demonstrate our system by showing how it can be used to turn commercial micro air-vehicles into autonomous robots. This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them. Our open source code may be useful for both the community of millions of toy robots users, as well as for researchers that may extend it for further protocols.
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Xiao, Lei, Yubing Han, and Zuxin Weng. "Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels." Remote Sensing 14, no. 20 (October 12, 2022): 5086. http://dx.doi.org/10.3390/rs14205086.
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A novel framework for a low-cost coding digital receiving array based on machine learning (ML-CDRA) is proposed in this paper. The received full-array signals are encoded into a few radio frequency (RF) channels, and decoded by an artificial neural network in real-time. The encoding and decoding networks are studied in detail, including the implementation of the encoding network, the loss function and the complexity of the decoding network. A generalized form of loss function is presented by constraint with maximum likelihood, signal sparsity, and noise. Moreover, a feasible loss function is given as an example and the derivations for back propagation are successively derived. In addition, a real-time processing implementation architecture for ML-CDRA is presented based on the commercial chips. It is possible to implement by adding an additional FPGA on the hardware basis of full-channel DRA. ML-CDRA requires fewer RF channels than the traditional full-channel array, while maintaining a similar digital beamforming (DBF) performance. This provides a practical solution to the typical problems in the existing low-cost DBF systems, such as synchronization, moving target compensation, and being disabled at a low signal-to-noise ratio. The performance of ML-CDRA is evaluated in simulations.
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Hu, Jun, Huajin Tang, K. C. Tan, Haizhou Li, and Luping Shi. "A Spike-Timing-Based Integrated Model for Pattern Recognition." Neural Computation 25, no. 2 (February 2013): 450–72. http://dx.doi.org/10.1162/neco_a_00395.
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During the past few decades, remarkable progress has been made in solving pattern recognition problems using networks of spiking neurons. However, the issue of pattern recognition involving computational process from sensory encoding to synaptic learning remains underexplored, as most existing models or algorithms target only part of the computational process. Furthermore, many learning algorithms proposed in the literature neglect or pay little attention to sensory information encoding, which makes them incompatible with neural-realistic sensory signals encoded from real-world stimuli. By treating sensory coding and learning as a systematic process, we attempt to build an integrated model based on spiking neural networks (SNNs), which performs sensory neural encoding and supervised learning with precisely timed sequences of spikes. With emerging evidence of precise spike-timing neural activities, the view that information is represented by explicit firing times of action potentials rather than mean firing rates has been receiving increasing attention. The external sensory stimulation is first converted into spatiotemporal patterns using a latency-phase encoding method and subsequently transmitted to the consecutive network for learning. Spiking neurons are trained to reproduce target signals encoded with precisely timed spikes. We show that when a supervised spike-timing-based learning is used, different spatiotemporal patterns are recognized by different spike patterns with a high time precision in milliseconds.
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Parathai, Phetcharat, Naruephorn Tengtrairat, Wai Lok Woo, Mohammed A. M. Abdullah, Gholamreza Rafiee, and Ossama Alshabrawy. "Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM." Sensors 20, no. 16 (August 5, 2020): 4368. http://dx.doi.org/10.3390/s20164368.
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This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.
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Abood, Enas Wahab, Zaid Ameen Abduljabbar, Mustafa A. Al Sibahee, Mohammed Abdulridha Hussain, and Zaid Alaa Hussien. "Securing audio transmission based on encoding and steganography." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (June 1, 2021): 1777. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1777-1786.
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One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.
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Kovalchuk, A. M. "BINARY LINEAR TRANSFORMATIONS IN MODIFICATIONS OF RSA ALGORITHM OF IMAGES." Ukrainian Journal of Information Technology 2, no. 1 (2020): 37–42. http://dx.doi.org/10.23939/ujit2020.02.037.
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The images are one of the most used kinds of the information in modern information company. Therefore actual problems is the organization of protection from unauthorized access and usage. An important characteristic of the image is the presence of contours in the image. The task of contour selection requires the use of operations on adjacent elements that are sensitive to change and suppress areas of constant levels of brightness, that is, contours are those areas where changes occur, becoming light, while other parts of the image remain dark. Mathematically, the ideal outline is to break the spatial function of the brightness levels in the image plane. Therefore, contour selection means finding the most dramatic changes, that is, the maxima of the gradient vector module. This is one of the reasons that the contours remain in the image when encrypted in the RSA system, since the encryption here is based on a modular elevation of some natural number. At the same time, on the contour and on the neighboring contours of the peak villages, the elevation of the brightness value gives an even bigger gap. Problem protect from unauthorized access is by more composite in matching with a problem protect from usage. Basis for organization of protection is the interpretation of the image as stochastic signal. It stipulates carry of methods of encoding of signals on a case of the images. But the images are a specific signal, which one in possesses, is padding to representative selfless creativeness, also by visual selfless creativeness. Therefore to methods of encoding, in case of their usage concerning the images, one more requirement – full noise of the coded image is put forward. It is necessary to make to impossible usage of methods of visual image processing. The algorithm RSA is one of the most used production specifications of encoding of signals. In attitude of the images there are some problems of its encoding, the contours on the coded image are in particular saved. Therefore actual problem is the mining of modification to a method RSA such, that: to supply stability to decoding; to supply full noise of the images. One of pathes of the solution of this problem is usage of affine transformations.
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Abdallah, Hanaa A., and Souham Meshoul. "A Multilayered Audio Signal Encryption Approach for Secure Voice Communication." Electronics 12, no. 1 (December 20, 2022): 2. http://dx.doi.org/10.3390/electronics12010002.
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In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the cryptosystems rely on the values of audio samples, which allows for saving time while increasing their resistance to hackers and environments with a noisy background. The main feature of the proposed approach is to consider three levels of encryption namely fusion, substitution, and permutation where various combinations are considered. The resulting cryptosystems are compared to the one-dimensional logistic map-based encryption techniques and other state-of-the-art methods. The performance of the suggested cryptosystems is evaluated by the use of the histogram, structural similarity index, signal-to-noise ratio (SNR), log-likelihood ratio, spectrum distortion, and correlation coefficient in simulated testing. A comparative analysis in relation to the encryption of logistic maps is given. This research demonstrates that increasing the level of encryption results in increased security. It is obvious that the proposed salting-based encryption method and the multilayer DCT/DST cryptosystem offer better levels of security as they attain the lowest SNR values, −25 dB and −2.5 dB, respectively. In terms of the used evaluation metrics, the proposed multilayer cryptosystem achieved the best results in discrete cosine transform and discrete sine transform, demonstrating a very promising performance.
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McDonough, Letang, Erwin, and Kana. "Evidence for Maintained Post-Encoding Memory Consolidation Across the Adult Lifespan Revealed by Network Complexity." Entropy 21, no. 11 (November 1, 2019): 1072. http://dx.doi.org/10.3390/e21111072.
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Memory consolidation is well known to occur during sleep, but might start immediately after encoding new information while awake. While consolidation processes are important across the lifespan, they may be even more important to maintain memory functioning in old age. We tested whether a novel measure of information processing known as network complexity might be sensitive to post-encoding consolidation mechanisms in a sample of young, middle-aged, and older adults. Network complexity was calculated by assessing the irregularity of brain signals within a network over time using multiscale entropy. To capture post-encoding mechanisms, network complexity was estimated using functional magnetic resonance imaging (fMRI) during rest before and after encoding of picture pairs, and subtracted between the two rest periods. Participants received a five-alternative-choice memory test to assess associative memory performance. Results indicated that aging was associated with an increase in network complexity from pre- to post-encoding in the default mode network (DMN). Increases in network complexity in the DMN also were associated with better subsequent memory across all age groups. These findings suggest that network complexity is sensitive to post-encoding consolidation mechanisms that enhance memory performance. These post-encoding mechanisms may represent a pathway to support memory performance in the face of overall memory declines.
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Chiasson, David, Junkai Xu, and Peter Shull. "Lossless Compression of Human Movement IMU Signals." Sensors 20, no. 20 (October 20, 2020): 5926. http://dx.doi.org/10.3390/s20205926.
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Real-time human movement inertial measurement unit (IMU) signals are central to many emerging medical and technological applications, yet few techniques have been proposed to process and represent this information modality in an efficient manner. In this paper, we explore methods for the lossless compression of human movement IMU data and compute compression ratios as compared with traditional representation formats on a public corpus of human movement IMU signals for walking, running, sitting, standing, and biking human movement activities. Delta coding was the highest performing compression method which compressed walking, running, and biking data by a factor of 10 and compressed sitting and standing data by a factor of 18 relative to the original CSV formats. Furthermore, delta encoding was shown to approach the a posteriori optimal linear compression level. All methods were implemented and released as open source C code using fixed point computation which can be integrated into a variety of computational platforms. These results could serve to inform and enable human movement data compression in a variety of emerging medical and technological applications.
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Turovsky, Egor A., Maria V. Turovskaya, Evgeniya I. Fedotova, Alexey A. Babaev, Victor S. Tarabykin, and Elena G. Varlamova. "Role of Satb1 and Satb2 Transcription Factors in the Glutamate Receptors Expression and Ca2+ Signaling in the Cortical Neurons In Vitro." International Journal of Molecular Sciences 22, no. 11 (May 31, 2021): 5968. http://dx.doi.org/10.3390/ijms22115968.
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Transcription factors Satb1 and Satb2 are involved in the processes of cortex development and maturation of neurons. Alterations in the expression of their target genes can lead to neurodegenerative processes. Molecular and cellular mechanisms of regulation of neurotransmission by these transcription factors remain poorly understood. In this study, we have shown that transcription factors Satb1 and Satb2 participate in the regulation of genes encoding the NMDA-, AMPA-, and KA- receptor subunits and the inhibitory GABA(A) receptor. Deletion of gene for either Satb1 or Satb2 homologous factors induces the expression of genes encoding the NMDA receptor subunits, thereby leading to higher amplitudes of Ca2+-signals in neurons derived from the Satb1-deficient (Satb1fl/+ * NexCre/+) and Satb1-null mice (Satb1fl/fl * NexCre/+) in response to the selective agonist reducing the EC50 for the NMDA receptor. Simultaneously, there is an increase in the expression of the Gria2 gene, encoding the AMPA receptor subunit, thus decreasing the Ca2+-signals of neurons in response to the treatment with a selective agonist (5-Fluorowillardiine (FW)). The Satb1 deletion increases the sensitivity of the KA receptor to the agonist (domoic acid), in the cortical neurons of the Satb1-deficient mice but decreases it in the Satb1-null mice. At the same time, the Satb2 deletion decreases Ca2+-signals and the sensitivity of the KA receptor to the agonist in neurons from the Satb1-null and the Satb1-deficient mice. The Satb1 deletion affects the development of the inhibitory system of neurotransmission resulting in the suppression of the neuron maturation process and switching the GABAergic responses from excitatory to inhibitory, while the Satb2 deletion has a similar effect only in the Satb1-null mice. We show that the Satb1 and Satb2 transcription factors are involved in the regulation of the transmission of excitatory signals and inhibition of the neuronal network in the cortical cell culture.
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Wårvik, Brita. "Continuity and quantity." Journal of Historical Pragmatics 15, no. 1 (February 28, 2014): 93–122. http://dx.doi.org/10.1075/jhp.15.1.05war.
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Givón’s (1995) quantity principle about the diagrammatic iconicity of coding forms has mostly been investigated for topic continuity and nominal elements. The present paper considers its applicability both to participant continuity and the continuity of time, focussing on their interaction in the organisation of narrative discourse. As an additional test of the hypothesis, the paper studies historical data, examining the structuring roles of signals of participant and temporal continuities in Old English narrative prose. The findings indicate that the choice of signals of the continuities of time and participants follows the iconic quantity principle of longer and informationally-heavier forms encoding greater degrees of discontinuity. The paper also underlines the importance of text type and genre-specific factors in investigations of discourse-structural signals. Specifically for the Old English narrative data, the study provides further support for the discourse marker role of þa ‘then’ as distinct from other temporal expressions.
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Fu, Qiang, and Hongbin Dong. "Breast Cancer Recognition Using Saliency-Based Spiking Neural Network." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–17. http://dx.doi.org/10.1155/2022/8369368.
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The spiking neural networks (SNNs) use event-driven signals to encode physical information for neural computation. SNN takes the spiking neuron as the basic unit. It modulates the process of nerve cells from receiving stimuli to firing spikes. Therefore, SNN is more biologically plausible. Although the SNN has more characteristics of biological neurons, SNN is rarely used for medical image recognition due to its poor performance. In this paper, a reservoir spiking neural network is used for breast cancer image recognition. Due to the difficulties of extracting the lesion features in medical images, a salient feature extraction method is used in image recognition. The salient feature extraction network is composed of spiking convolution layers, which can effectively extract the features of lesions. Two temporal encoding manners, namely, linear time encoding and entropy-based time encoding methods, are used to encode the input patterns. Readout neurons use the ReSuMe algorithm for training, and the Fruit Fly Optimization Algorithm (FOA) is employed to optimize the network architecture to further improve the reservoir SNN performance. Three modality datasets are used to verify the effectiveness of the proposed method. The results show an accuracy of 97.44% for the BreastMNIST database. The classification accuracy is 98.27% on the mini-MIAS database. And the overall accuracy is 95.83% for the BreaKHis database by using the saliency feature extraction, entropy-based time encoding, and network optimization.
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Chen, Zhuo. "Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method." Advances in Mathematical Physics 2021 (September 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/6811192.
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The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.
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Gutierrez, Eric, Carlos Perez, Fernando Hernandez, and Luis Hernandez. "Time-Encoding-Based Ultra-Low Power Features Extraction Circuit for Speech Recognition Tasks." Electronics 9, no. 3 (February 29, 2020): 418. http://dx.doi.org/10.3390/electronics9030418.
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Current trends towards on-edge computing on smart portable devices requires ultra-low power circuits to be able to make feature extraction and classification tasks of patterns. This manuscript proposes a novel approach for feature extraction operations in speech recognition/voice activity detection tasks suitable for portable devices. Whereas conventional approaches are based on either completely analog or digital structures, we propose a “hybrid” approach by means of voltage-controlled-oscillators. Our proposal makes use of a bank a band-pass filters implemented with ring-oscillators to extract the features (energy within different frequency bands) of input audio signals and digitize them. Afterwards, these data will input a digital classification stage such as a neural network. Ring-oscillators are structures with a digital nature, which makes them highly scalable with the possibility of designing them with minimum length devices. Additionally, due to their inherent phase integration, low-frequency band-pass filters can be implemented without large capacitors. Consequently, we strongly benefit from power consumption and area savings. Finally, our proposal may incorporate the analog-to-digital converter into the structure of the own features extractor circuit to make the full conversion of the raw data when triggered. This supposes a unique advantage with respect to other approaches. The architecture is described and proposed at system-level, along with behavioral simulations made to check whether the performance is the expected one or not. Then the structure is designed with a 65-nm CMOS process to estimate the power consumption and area on a silicon implementation. The results show that our solution is very promising in terms of occupied area with a competitive power consumption in comparison to other state-of-the-art solutions.
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JUDD, KEVIN, and KAZUYUKI AIHARA. "GENERATION, RECOGNITION AND LEARNING OF RECURRENT SIGNALS BY PULSE PROPAGATION NETWORKS." International Journal of Bifurcation and Chaos 10, no. 10 (October 2000): 2415–28. http://dx.doi.org/10.1142/s0218127400001559.
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Pulse propagation networks (PPN) are neural networks in which individual action potentials encode information. The dynamics of PPN depend not only on the synaptic weights of connections but also the delay in the propagation of action potentials between neural elements. It is known that PPN can perform complex computations and information processing by encoding information as the time intervals between action potential events. In this paper we approach the practical question of constructing PPN to generate, recognize and learn arbitrary recurrent signals. We present specific examples of networks that generate and recognize signals and also describe a learning algorithm that allows PPN to learn by self-organization. Finally we discuss the possible importance of dynamical fluctuations about the mean-activity field of a neural network.