Signal Processing (United States)

Spiking Neural Networks (SNNs) process information through discrete, time-dependent spikes, closely 2 mimicking the dynamics of biological neurons. This temporal coding enables SNNs to capture rich spatio-3 temporal patterns and exploit event-driven sensing and computation, offering sophisticated information 4 processing capabilities and practical efficiency at the same time.The key strengths of SNNs reside in their biological plausibility and energy efficiency. SNNs can operate 6 with significantly reduced power consumption, compared to conventional neural networks and deep learning 7 models. Advances in learning algorithms, and theoretical and software frameworks have accelerated 8 the development of SNNs, bringing neuroscience-inspired models to practical machine intelligence in 9 engineering systems, while also reducing the performance gap from deep neural networks (DNNs).

In the electromagnetic field formed by coaxial cables, electromagnetic waves of different frequencies are shown to propagate at the same speed (the speed of light c). Therefore, the variational method, which is based on the fact that electromagnetic waves of different frequencies have different propagation speeds, cannot be applied. In this session, the characteristics of light propagating through an electromagnetic field of glass, suitable for applying the variational method and for constructing lenses, are determined. Although the electromagnetic field is derived from the properties of dielectrics, it has been demonstrated to be equivalent to a circuit that represents the Schrödinger equation used in quantum mechanics. In other words, it may be possible to apply electromagnetic fields from classical physics (circuit theory) to quantum mechanics. Quaternions and wave functions that are considered to be related to complex functions will be the topic of future studies because the Schrödinger equation is a complex function.

ABSTRACT In the companion “Theory” article, we presented a new framing of the seismic location problem in terms of differential geometry (Harris et al., 2025). From that viewpoint, we developed a “project and correct” approach for estimating the relative locations of earthquakes. Here, we use project and correct to estimate high-precision relative locations of events from an earthquake sequence beneath the town of Pahala, Hawaii, using high-precision correlation-derived picks. The sequence was active from 2020 through 2022 and produced many highly correlated signals at Hawaii Volcano Observatory (HVO) stations on the island of Hawaii. The data we inverted consisted of 2882 events with observations at 5 HVO stations. For comparison with the travel-time image, we also produced conventional hypocenter solutions using both the Bayesloc program (Myers et al., 2007, 2009) and a purpose-built double-difference code. There were obvious structural elements in the resulting image, the resolution of which we used to test the performance of the project and the correct algorithm. For the projection step, we first produced a 3D local basis using an singular value decomposition (SVD) of the 2882 groups of times. Projection of the travel-time vectors into this basis resulted in an image with structures similar to those produced by our conventional locators, but with distortion as predicted by theory. Removing the distortion requires an inverse operator generated from the metric tensor at the geometric centroid of the events. We compared two approaches to obtaining such an inverse operator. The first uses an estimate of the geographic centroid of the event cloud from the centroid of the travel-time data. The second approach uses the centroid of the conventionally produced locations. The first approach produces a corrected image very similar to the conventional results, but with a rotation. The corrected image produced using the conventionally derived centroid is a near-exact match to the conventional locations.

Nearly 432 million adults across the world currently have disabling hearing loss, and 2.5 billion people are projected to have some degree of hearing loss in the next 25 years, per the World Health Organization. People with hearing impairments can benefit from hearing aid devices to be more active in daily life where they will come across multitudes of sounds of varying frequencies and noise levels. One crucial reason noted for the low usage of hearing aid devices is the accessibility and affordability of these devices. Smartphones and tablets have recently become valuable devices due to their availability, powerful built-in features, and user friendliness. In this paper, we present and analyze an open-source multichannel dynamic range compression algorithm, an indispensable part of the signal processing pipeline for the hearing aid application. It provides optimal fitting of received signals by adjusting the frequency-dependent gain function according to the individual's hearing sensitivity levels. This work examines how the algorithm automatically adjusts the gains at sixoctave frequency bands for optimal speech intelligibility and comfort for the user with the improvements in hearing being assessed by comparing the audiograms of a hearing impaired patient with and without the use of a hearing aid.

The voiced parts of the speech signal are shaped by glottal pulse excitation, the vocal tract, and the speaker’s lips. Semantic information contained in speech is shaped mainly by the vocal tract. Unfortunately, the quasiperiodicity of the glottal excitation, in the case of the HFCC parameterization, is one of the factors affecting the significant scatter of the feature vector values by introducing ripples into the amplitude spectrum. This paper proposes a method to reduce the effect of quasiperiodicity of the excitation on the feature vector. For this purpose, blind deconvolution was used to determine the vocal tract transfer function estimator and the corrective function of the amplitude spectrum. Subsequently, on the basis of the obtained HFCC parameters, statistical models of individual Polish speech phonemes were developed in the form of mixtures of Gaussian distributions, and the influence of the correction on the quality of classification of speech frames containing Polish vowels was considered in details. The aim of the introduced solution was to narrow the GMM distributions, which clearly, according to the detection theory, reduces classification errors. The results obtained confirm the effectiveness of the proposed method.

The Glance project provides software solutions for managing high energy physics collaborations’ management data and workflow. It was started in 2003 and operates in the ALICE, AMBER, ATLAS, CMS, and LHCb CERN experiments on top of CERN common infrastructure. The project develops Web applications using PHP and Vue.js, running on CentOS virtual machines hosted on the CERN OpenStack private cloud. These virtual machines are built via Puppet for installing and configuring core software while tailoring them to meet each experiment’s requirements in a collaborative approach under the Glance Project. This approach minimizes redundant work across experiments while allowing cooperation when responding to operations incidents. In the scenario of the CentOS 7 end-of-life, the Glance project has chosen to migrate to RHEL9 while undergoing a major upgrade of PHP (from 7.3 or 7.4 to 8.2) across the experiments. This presentation will expose the technical and organizational challenges the Glance project faces on common dependencies upgrades from the perspective of the ATLAS Glance team.

Modern technologies like computer vision (CV), Artificial Intelligence (AI), the internet of things (IoT), and (AIoT) Artificial Intelligence of Things have advanced in many fields, particularly surveillance systems, which need to process faces in real time to guarantee security. This paper proposed a comparative analysis for a real time face recognition system from video sequences with two architectures including traditional application using a personal computer and a Raspberry Pi, respectively. Our study offers an implementation for a real-time face recognition system employing Viola-Jones algorithm and Deep neural networks techniques such as Convolutional Neural Networks (CNN) for face detection and face recognition. Our face recognition system seeks to replace the conventional approach of keeping track of attendance by implementing a Raspberry Pi-based attendance monitoring system that can be utilized by all institutions and organizations. Significant progress has been made thanks to the use of CNN models which has allowed an accuracy rate of 94%.

Abstract BACKGROUND Pediatric patients undergoing cranial irradiation are at risk of developing neurocognitive impairments. Mechanistically, radiation-induced injury to the hippocampi could play an important role in this debilitating condition, by inducing loss of neural stem/progenitor cells (NSCs/NPCs) and neuroinflammation, eventually leading to hippocampal atrophy. Noradrenergic (NA) agonists can potentially mitigate this radiation-induced cognitive decline by reducing cell death and inflammation. Therefore, we hypothesize that pharmacological boosting of NA will attenuate radiation-induced impairments in neurogenesis and decrease neuroinflammation, resulting in decreased memory dysfunction. MATERIAL AND METHODS Mice were irradiated with a single fraction of 10 Gy at P22. Reboxetine and Atipamezole were administered as 7 daily intraperitoneal injections, starting immediately after radiation. Sham therapy was administered in an identical manner by injecting equivalent volumes of sterile 0.9% NaCl. At 6 hours, 1 week, and 1 month post-radiation, radiation-induced NSCs/NPCs death, deficits in neurogenesis, and inflammation were investigated with histology. Additionally, at 1 month post-irradiation, hippocampal atrophy and memory deficits were investigated by ex-vivo MRI and a behavioral test battery, respectively. RESULTS We report that Reboxetine and Atipamezole significantly mitigate radiation-induced toxicity in the hippocampi. At 6 hours post-irradiation, NA agonists reduce radiation-induced apoptosis and microglial inflammation compared to sham treatment. A substantial reduction in the number of NSCs/NPCs and immature neurons within the hippocampal dentate gyrus was observed at both 1 week and 1 month post-irradiation. This deleterious effect of irradiation on neurogenesis was significantly attenuated by Reboxetine and Atipamezole, at both timepoints. Considerable hippocampal volume loss was observed after radiotherapy, which was prevented by both drugs. Finally, behavioral testing revealed that treatment with Reboxetine after radiotherapy successfully mitigated radiation-induced deficits in working and reference memory. CONCLUSION The present findings document the marked benefits of NA treatment against radiation-induced hippocampal NSCs/NPCs death, impaired hippocampal neurogenesis, neuroinflammation, and hippocampal atrophy. Additionally, we demonstrate that Reboxetine is able to mitigate radiation-induced memory dysfunction, which highlights the potential of NA treatment for resolving long-standing complications faced by pediatric brain tumor survivors. This project is supported by BOF (Bijzonder Onderzoeksfonds) - Ghent University (I. De Bie is a PhD fellow, grant number 01D07923).

Spotting a significant number of drones flying near the entrance of a beehive during late Spring could indicate the occurrence of swarming mood, as the the surge in drone presence is related to an overcrowded hive. Swarming refers to a natural reproductive process witnessed in honey bees, wherein half of the bee colony departs from their hive alongside the aging queen. In the paper, we propose an early swarming detection mechanism that relies on the behavior of the drones. The proposed method is based on audio signals registered in a close proximity to the beehive entrance. A comparative study was performed to find the most effective preprocessing method for the audio signals for the detection problem. We have compared the results for three different power spectrum density coefficients estimation methods, which are used as an input of an autoencoder neural network to discriminate drones from worker bees. Through simulations employing real-life signals, it has been demonstrated that drone detection based solely on audio signals is indeed feasible. The attained level of detection accuracy enables the creation of an efficient alarm system for beekeepers.

Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away from goal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents.