Signal Restoration Research Articles

ENPP1 is an innate immune checkpoint of the anticancer cGAMP-STING pathway in breast cancer.

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) expression correlates with poor prognosis in many cancers, and we previously discovered that ENPP1 is the dominant hydrolase of extracellular cGAMP: a cancer-cell-produced immunotransmitter that activates the anticancer stimulator of interferon genes (STING) pathway. However, ENPP1 has other catalytic activities and the molecular and cellular mechanisms contributing to its tumorigenic effects remain unclear. Here, using single-cell RNA-seq, we show that ENPP1 in both cancer and normal tissues drives primary breast tumor growth and metastasis by dampening extracellular 2'3'-cyclic-GMP-AMP (cGAMP)-STING-mediated antitumoral immunity. ENPP1 loss-of-function in both cancer cells and normal tissues slowed primary tumor growth and abolished metastasis. Selectively abolishing the cGAMP hydrolysis activity of ENPP1 phenocopied ENPP1 knockout in a STING-dependent manner, demonstrating that restoration of paracrine cGAMP-STING signaling is the dominant anti-cancer mechanism of ENPP1 inhibition. Finally, ENPP1 expression in breast tumors deterministically predicated whether patients would remain free of distant metastasis after pembrolizumab (anti-PD-1) treatment followed by surgery. Altogether, ENPP1 blockade represents a strategy to exploit cancer-produced extracellular cGAMP for controlled local activation of STING and is therefore a promising therapeutic approach against breast cancer.

Benzodiazepine receptor agonist carbacetam modulates the level of vascular endothelial growth factor in the retina of rats with streptozotocin-induced diabetes

One of the primary mechanisms of retinal neurodegeneration in diabetes mellitus is gamma-aminobutyric acid (GABA) deficiency that makes the use of GABA-benzodiazepine receptor modulators a promising option for the correction of this diabetic complication. The aim of this study was to determine the effect of the benzodiazepine receptor agonist carbacetam on the expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) in retina of rats with hyperglycemia. Experimental diabetes was modeled by a single administration of streptozotocin (50 mg/kg) to three-month-old male Wistar rats. Immunoblotting and immunohistochemical studies were performed using monoclonal antibodies against VEGF and HIF-1α. It was shown that the development of diabetic retinopathy (DR) at the early stages was accompanied by a progressive multifold increase in the retina content of VEGF on 7-28 days and HIF-1α on 28th day. Insulin and insulin+carbacetam treatment significantly alleviated diabetes-induced overexpression of both HIF-1α and VEGF. Carbacetam was shown to block the diabetogenic increase in VEGF content in retina. The introduction of insulin with carbacetam significantly reduced the expression of VEGF and the development of specific morphological manifestations of DR. Thus, restoration of GABA-ergic signaling can be used as a promising therapeutic option for the correction of DR disorders. Keywords: carbacetam, GABA-benzodia­zepine receptors, HIF-1α hyperglycemia, retinopathy, streptozotocin-induced diabetes, VEGF

A denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm

AbstractEstimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring.

Deriving the Terrestrial Water Storage Anomaly From GRACE Spherical Harmonic Coefficients Using a Convolutional Neural Network

AbstractTerrestrial water storage anomaly (TWSA), derived from Gravity Recovery and Climate Experiment (GRACE) satellites, has been widely used in hydrology studies. The inversion is commonly achieved by truncating and filtering spherical harmonic coefficients (SHC), whereby the result is characterized by leakage error and low resolution. It remains unclear whether machine learning methods can help resolve this challenging issue. In this study, we present a convolutional neural network (CNN) approach to correct TWSA from GRACE SHC by leveraging the knowledge of the leakage effect determined from global hydrological models (GHMs) and land surface models (LSMs). The CNN approach is implemented in three representative regions in China, that is, the human‐impacted Haihe River Basin, the nature‐impacted Yangtze River Basin and the model‐limited Tibetan Plateau. The results show the following: (a) The recovery performance of CNN at the basin scale is better than that at the grid scale, and the grid‐scale recovery is significantly influenced by the spatial heterogeneity of TWSA and the input GHM/LSMs; (b) The more accurate the GHM/LSMs used for training, the better the recovery performance of CNN; and (c) The trained model retains comparable performance in deriving the TWSA time series from GRACE SHC when compared to that derived from other methods (i.e., scaling factor and mass concentration solutions) with average r = 0.90 and RMSE = 21.50 mm. This study highlights the potential of machine learning to supplement conventional correction methods when deriving the TWSA from GRACE SHC by utilizing signal restoration knowledge learned from multiple accurate GHMs/LSMs.

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

Restoration of LAT Signaling in CD33-Directed CAR T Cells Improves Potency and Persistence Against Acute Myeloid Leukemia

Despite the development of novel therapies, the majority of patients with acute myeloid leukemia (AML) will not achieve long-term remissions. Patients with relapsed/refractory (r/r) AML have particularly poor outcomes, as conventional chemotherapy regimens are often unable to elicit long-term remissions. Allogeneic hematopoietic stem cell transplant (HSCT) is potentially curative in eligible patients, however, bridging therapies which can induce deep remissions prior to HSCT are needed. Second generation chimeric antigen receptor (CAR) T cells targeting lineage-restricted antigens, such as CD19, have induced remissions in up to 80 - 90% of pediatric patients with r/r B-lineage acute lymphoblastic leukemia (ALL), however, post-CAR relapses are common and only ~50% of treated patients achieve durable remissions. Similar strategies targeting myeloid-restricted antigens with 2 nd generation CAR T cells in AML have not replicated the success seen in ALL. As clinical experience has highlighted the impact of CAR T cell potency, persistence, and antigen-sensitivity to long-term efficacy in ALL, strategies to boost these properties in myeloid-directed CAR T cells are necessary to extend the clinical benefit of this therapy to patients with AML. Utilizing a global phosphoproteomics screen, we recently identified that inefficient phosphorylation of the Linker for activation of T cells (LAT) and the subsequent decrease in the downstream signaling pathways results in suboptimal CAR T cell activation. To overcome inefficient LAT activation, we developed a novel bicistronic CAR platform, consisting of a 2 nd generation CAR along with an Adjunctive LAT-Activating CAR (ALA-CART) which improved sensitivity to low-antigen leukemia cells, increased in vivo potency, and enhanced persistence in xenograft models of ALL. We hypothesized that the restoration of LAT signaling via the ALA-CART platform would also improve potency and persistence in pre-clinical models of AML. We found that while CD33 ALA-CART cells responded to CD33+ AML cells in vitro, they were less cytotoxic and produced lower levels of cytokines than CD33-28z or CD33-BBz 2 ndgeneration CAR T cells. However, CD33 ALA-CART cells demonstrated increased potency in xenograft models, eradicating AML cells at much lower doses than CD33-28z and CD33-BBz CAR T cells (Fig. 1A, p<0.0001). This in vivo potency correlated with an enrichment of T stem cell memory cells in the ALA-CART product, resulting in increased persistence of CD33 ALA-CART cells after leukemia clearance (Fig. 1B, p=0.0139). The ALA-CART platform was associated with increased mitochondrial mass and function, suggesting that the increased signaling through LAT results in an enhanced capacity for oxidative phosphorylation, potentially contributing to the increased persistence of CD33 ALA-CART cells in vivo. Thus, the ALA-CART platform, designed to restore LAT signaling in CAR T cells, demonstrates that CAR molecules can be rationally designed to enhance both in vivo potency and persistence, and represents a promising strategy for the development for more effective AML-directed CAR T cells.

Restoration of motion-corrupted EEG signals using attention-guided operational CycleGAN

Electroencephalogram (EEG) signals suffer substantially from motion artifacts even in ambulatory settings. Signal processing techniques for removing motion artifacts from EEG signals have limitations, and the potential of classical or deep machine-learning algorithms for this task remains largely unexplored. We propose Attention-Guided Operational CycleGAN (AGO-CycleGAN), a novel CycleGAN-based framework to remove motion artifacts and enhance the quality of corrupted EEG signals. It incorporates self-generative operational neurons and an attention-guided Feature Pyramid Network with modified bottlenecks as generators and PatchGAN-based discriminators. AGO-CycleGAN was trained and tested on a single-channel EEG dataset from 23 subjects, using a subject-independent Jackknife cross-validation approach. It outperformed other methods and was evaluated through qualitative and quantitative analysis, employing robust metrics in both temporal and frequency domains. The results indicate its effectiveness in restoring EEG signals affected by severe motion artifacts. AGO-CycleGAN achieves state-of-the-art EEG restoration performance in the temporal domain, gaining improvements in signal-to-noise ratio (ΔSNR) and temporal correlation (ηtemp) by 26.497 dB and 87.2%, respectively. It also showed excellent performance in preserving the spectral EEG components (delta, theta, alpha, beta, and gamma), evaluated through band power ratio before and after restoration. Spectral correlation (ηspec) improved by 93.5% after cleaning the motion artifacts. Qualitative evaluations showed excellently reconstructed clean EEG waveforms upon restoration. Spectral restoration visualized through Power Spectral Density (PSD) plots and per-band topographic maps showed a uniform removal of high-power motion artifact components throughout the spectrum. AGO-CycleGAN significantly outperformed existing techniques in EEG artifact removal and can be extended to multi-channel EEG systems.

Restoration of Rod-Derived Metabolic and Redox Signaling to Prevent Blindness.

Vision is initiated by capturing photons in highly specialized sensory cilia known as the photoreceptor outer segment. Because of its lipid and protein composition, the outer segments are prone to photo-oxidation, requiring photoreceptors to have robust antioxidant defenses and high metabolic synthesis rates to regenerate the outer segments every 10 days. Both processes required high levels of glucose uptake and utilization. Retinitis pigmentosa is a prevalent form of inherited retinal degeneration characterized by initial loss of low-light vision caused by the death of rod photoreceptors. In this disease, rods die as a direct effect of an inherited mutation. Following the loss of rods, cones eventually degenerate, resulting in complete blindness. The progression of vision loss in retinitis pigmentosa suggested that rod photoreceptors were necessary to maintain healthy cones. We identified a protein secreted by rods that functions to promote cone survival, and we named it rod-derived cone viability factor (RdCVF). RdCVF is encoded by an alternative splice product of the nucleoredoxin-like 1 (NXNL1) gene, and RdCVF was found to accelerate the uptake of glucose by cones. Without RdCVF, cones eventually die because of compromised glucose uptake and utilization. The NXNL1 gene also encodes for the thioredoxin RdCVFL, which reduces cysteines in photoreceptor proteins that are oxidized, providing a defense against radical oxygen species. We will review here the main steps of discovering this novel intercellular signaling currently under translation as a broad-spectrum treatment for retinitis pigmentosa.

A projection-based hybrid PRP-DY type conjugate gradient algorithm for constrained nonlinear equations with applications

Based on the convex combination technique, we propose a projection-based hybrid conjugate gradient algorithm for solving nonlinear equations with convex constraints in this paper. The conjugate parameter of the proposed algorithm is a convex combination of the modified Polak-Ribière-Polyak and Dai-Yuan type conjugate parameters, and the search direction has the sufficient descent property without the use of a line search strategy. The proposed hybrid algorithm's global convergence is established under appropriate assumptions. The numerical experiments demonstrate that the proposed algorithm is more efficient and competitive than existing methods under some benchmark test problems. Furthermore, it is also extended to solve the sparse signal and impulse noise image restoration problem that arises in compressive sensing.

MATHEMATICAL FRAMEWORK FORMULATION AND IMPLEMENTATION FOR HYPERSPECTRAL AEROSPACE IMAGES PROCESSING

This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth's surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate.

An efficient pretreatment method based on AgNPs-doped SnO2 photocatalyst for the accurate detection of heavy metals in organic-rich water samples

Humic acid (HA), the primary composition of natural organic matter (NOM) widely distributed in water and soil, can complex with heavy metal ions (HMIs), i.e., Cd(II) and Pb(II) in this study, which deters the accurate detection of HMIs using square wave anodic stripping voltammetry (SWASV). Hence, in this study, an efficient pretreatment method was proposed to restore the electrochemical signal of Cd(II) and Pb(II) by breaking the complexation based on AgNPs-doped SnO2 photocatalyst combined with LP/UV irradiation. Optimization of the key parameters for electrochemical signal restoration including pH for photolysis, AgNPs doping rate, photocatalyst dosage and photolysis time were performed to further elevating the accuracy in the proposed pretreatment method over 96.9% for Cd(II) and Pb(II) in 15 min. The effect of different HA concentrations on SWASV signal of Cd(II) and Pb(II) was also investigated adopting the optimal parameters. Then, the UV–vis absorption spectra, crystal structure, and the morphology of AgNPs-doped SnO2 photocatalyst were investigated to excavate the reasons behind the most excellent AgNPs doping rate to SnO2 in signal restoration. Moreover, the behavior of HA degradation and transformation under LP/UV irradiation was studied to investigate the mechanism of electrochemical signal restoration. Finally, the feasibility of the proposed method was testified by comparing detection results with ICP-MS results using real water samples extracted from aquaculture water.

A family of inertial‐based derivative‐free projection methods with a correction step for constrained nonlinear equations and their applications

AbstractNumerous attempts have been made to develop efficient methods for solving the system of constrained nonlinear equations due to its widespread use in diverse engineering applications. In this article, we present a family of inertial‐based derivative‐free projection methods with a correction step for solving such system, in which the selection of the derivative‐free search direction is flexible. This family does not require the computation of corresponding Jacobian matrix or approximate matrix at every iteration and possess the following theoretical properties: (i) the inertial‐based corrected direction framework always automatically satisfies the sufficient descent and trust region properties without specific search directions, and is independent of any line search; (ii) the global convergence of the proposed family is proven under a weaker monotonicity condition on the mapping , without the typical monotonicity or pseudo‐monotonicity assumption; (iii) the results about convergence rate of the proposed family are established under slightly stronger assumptions. Furthermore, we propose two effective inertial‐based derivative‐free projection methods, each embedding a specific search direction into the proposed family. We present preliminary numerical experiments on certain test problems to demonstrate the effectiveness and superiority of the proposed methods in comparison with existing ones. Additionally, we utilize these methods for solving sparse signal restorations and image restorations in compressive sensing applications.

Abstract PO-011: Analysis of nuclear receptor expression in head and neck cancer

Abstract Background: The nuclear receptor (NR) superfamily contains a wide array of transcription factors responsible for cellular metabolism, inflammation, differentiation, and hormonal functions. For 45 years these receptors have been of interest in head and neck squamous cell carcinoma (HNSCC) treatment and prevention, especially retinoic acid receptor (RAR), peroxisome proliferator-activated receptor (PPAR), and vitamin D receptor (VDR) families. In the current study we examined expression patterns of these receptors in The Cancer Genome Atlas (TCGA). Methods: TCGA RNA-Seq data within the cBioportal platform comparing HNSCC samples (n=515 patients/samples) to normal tissue (n=82 patients/samples) was interrogated for significant differences in nuclear receptor expression. Genes with a z-score relative to normal samples (log RNA-Seq V2 RSEM normalized) greater than +2 or less than -2 in ≥50% of cases were considered significant. Unpaired, two-tailed t-tests were used to analyze comparisons in nuclear receptor genes’ z-scores across multiple clinical and pathologic parameters, p<0.05 was deemed significant. Statistical stringency for differential gene expression conformed to cBioportal and the University of Alabama at Birmingham’s Cancer Data Analysis Portal (UALCAN). Results: Of the 46 NR genes and 19 NR cofactors examined, ninety nine percent of tumor samples in the TCGA had some form of NR gene ‘alteration’ compared to normal tissue. These alterations predominantly encompass expression up- or down-regulation. NR genes Peroxisome Proliferator Activated Receptor Gamma (PPARγ) and Retinoic Acid Receptor Related Orphan Receptor C (RORC) (both in the thyroid hormone receptor-like family), and the NR cofactor, Nuclear Receptor Coactivator 1 (NCOA1), were differentially expressed and downregulated in tumors compared to normal tissue. Differences in specific NR expression varied significantly with HPV status (30 NRs), organ site (oral cavity vs hypopharynx & larynx; 20 NRs), tumor size (T1/2 vs. T3/4; 21 NRs), gender (male vs. female; 7 NRs); tumor staging (stage I/II vs. stage III/IV; 6 NRs); lymph node status (N0 vs. N+; 5 NRs), smoking history (smokers vs. non-smokers; 3 NRs), and patient race (White vs. Black/African American; 2 NRs). Conclusions: In prior studies of nuclear receptors in aerodigestive cancer, the retinoid receptor axis was downregulated, and a subsequent goal was stimulation and restoration of activity with natural or synthetic retinoids as a rationale for treatment. Presently, we have discovered significant decreases in PPARγ expression and are targeting this receptor with thiazolidinediones in a series of preclinical and human trials in a similar rationale to promote restoration of normal signaling and events like maturation. Here, we observed significant differences in a variety of parameters of several receptors associated with risk factors, stage, race, and gender that are observed and may become the focus of interest in future targeting efforts. Citation Format: Lindsey Mortensen, Beverly R. Wuertz, Frank G. Ondrey. Analysis of nuclear receptor expression in head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-011.

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

CORRELATION FUNCTIONS AND QUASI-DETERMINISTIC SIGNALS

When processing data on random functions, they are most often limited to constructing an empirical correlation function. In this regard, the problem arises of constructing a random function (a quasi-deterministic signal) determined by a finite set of random variables and having a given correlation function. Moreover, a random function can often be considered Gaussian, since in many cases a random signal is obtained at the output of the system, which is fairly well approximated by a Gaussian. For stationary random processes and for random fields, this problem has been considered. For random sequences and discrete random fields, as well as for non-stationary random signals, the problem remained open. The article considers the problem of restoring a random sequence from known mathematical expectation and correlation function. Such a model random sequence is constructed, in which the mathematical expectation and correlation function coincide with the given ones. The mathematical expectation and the correlation function are the simplest probabilistic numerical characteristics, but they do not uniquely determine the corresponding set of probability distribution densities that satisfy the conditions of normalization and consistency, provided that for each fixed integer value of the parameter, the random sequence is a continuous random variable. The article considers the restoration of a quasi-deterministic signal in stationary and non-stationary cases. For the stationary case, three examples are given for constructing a quasi-deterministic discrete signal EMBED Equation.DSMT4 , provided that the spectral density has three different forms. For the non-stationary case, the corresponding quasi-deterministic signal was obtained for various cases of the spectrum. The use of a random function model determined by a finite number of parameters makes it possible to significantly simplify the analysis of applied problems, the solution of which is associated with differential equations with random coefficients, which are such quasi-deterministic signals. In this case, there is no need to use a complex apparatus of stochastic differential equations, since the solution of such an equation simply depends on random variables as on parameters.

Design scheme of a high dynamic semi-airborne transient electromagnetic receiving system based on a voltage-controlled amplifier

The semi-airborne transient electromagnetic (SATEM) technique is established to examine subsurface targets. The SATEM approach is widely used in urban underground space exploration, hydrogeological search, and deep mineral exploration. However, the strength of SATEM signals declines exponentially with the depth of detection, and the SATEM system frequently operates in high electromagnetic noise environments, directly affecting the precision of deep information interpretation. This research presents a high dynamic semi-airborne transient electromagnetic (HDSATEM) receiving system to increase the late SATEM signal intensity. This system mainly comprises a voltage-controlled amplifier (VCA) and a function generator. In addition, the VCA is modulated using the gain voltage produced by the function generator. Consequently, a simulation model has been employed to analyze various functions to compare the dynamic ranges. The actual amplification performance and signal restoration capacity are confirmed using a laboratory test. A field experiment is also carried out to evaluate the actual detection performance of the proposed system compared to standard SATEM receiving and controlled source audio-frequency magneto-telluric systems. The findings indicate that the performance improvement of the HDSATEM receiving system is more than 50% in terms of depth detection compared to that of standard SATEM receiving systems.

Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems

Abstract Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorphic circuits and emerging memory technologies.

Test Methods for Signal Reconstruction of Linear Stationary Systems

The test methods designed for mathematical reconstruction of input signals of linear stationary information processing systems from distorted and noisy output signals are proposed and analyzed. It is shown that the use of test data allows one to carry out a non-blind reconstruction without determining the hardware functions of processing systems, which, in general, can belong to the class of generalized functions. The features of the use of the regularization technique in test methods are considered when solving ill-posed and ill-conditioned problems of reconstruction of real non-deterministic signals. The criteria for selecting test signals are analyzed. The results of numerical experiments on the restoration of one-dimensional signals and two-dimensional images at different noise levels are presented.

전파 거리에 따른 신호의 감쇠와 분산이 고려된 순시 주파수 추정 알고리즘 및 장거리 전파용 반사파 계측법 시뮬레이터 개발

Time-Frequency domain Reflectometry(TFDR) is used for the purpose of fault detection and evaluating the integrity in cables. TFDR can diagnose live wire in nondestructive. but, the incident signal cannot aviod attenuation and dispersion according to increasing propagation distance, propagation velocity changes with propagation distance. attenuation and dispersion in long distance lead to decresing falut detection performance of cable. therefore, Developing instantaneous frequency Estimation algorithm of signal and signal restoration algorithm in order to determine tendency of propagation velocity for propagation distance, we demonstrate the performance of algorithm by implementing TFDR simulator possible long length.

Rethinking the network determinants of motor disability in Parkinson's disease.

For roughly the last 30 years, the notion that striatal dopamine (DA) depletion was the critical determinant of network pathophysiology underlying the motor symptoms of Parkinson's disease (PD) has dominated the field. While the basal ganglia circuit model underpinning this hypothesis has been of great heuristic value, the hypothesis itself has never been directly tested. Moreover, studies in the last couple of decades have made it clear that the network model underlying this hypothesis fails to incorporate key features of the basal ganglia, including the fact that DA acts throughout the basal ganglia, not just in the striatum. Underscoring this point, recent work using a progressive mouse model of PD has shown that striatal DA depletion alone is not sufficient to induce parkinsonism and that restoration of extra-striatal DA signaling attenuates parkinsonian motor deficits once they appear. Given the broad array of discoveries in the field, it is time for a new model of the network determinants of motor disability in PD.