arXiv subjects

Alireza Maleki

Publications and source records attributed to Alireza Maleki.

16 recordsLinked to original sources

arXiv2026

The Role of Geometric Analysis in Interferometer Design and Optimization for Gravitational Quantum Entanglement

The pursuit of a quantum theory of gravity, aiming to unify general relativity and quantum mechanics, remains one of the most enduring challenges in physics. Because of the extreme energy scales associated with the Planck regime, direct experimental evidence for quantum gravity remains elusive. However, recent proposals suggest that quantum entanglement between two massive particles may provide a pathway to probe the quantum nature of gravity. In this study, we examine the interferometer geometries proposed in these works, with particular attention to the commonly used approximation that neglects phase contributions from the vertical segments of the particle trajectories. Our analysis shows that this approximation can lead to incorrect predictions and, in certain parameter regimes, to null results where entanglement would otherwise be expected. We derive exact solutions that incorporate the full particle trajectories and demonstrate that the vertical arms can significantly affect the accumulated phase. Crucially, we identify configurations in which the induced entanglement vanishes entirely, a feature missed by simplified treatments. These findings show that accounting for the full interferometer geometry is not merely a refinement, but is essential for accurately assessing gravity-induced entanglement.

quant-ph↗

arXiv2026

Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis. However, the clinical applicability of models that use multimodal learning is hampered by their poor performance under noisy or uninformative data streams. Present fusion approaches often lack robust mechanisms for the dynamic assessment of data quality and for the provision of a trustable confidence score on the final prediction. This dissuades their deployment in safety-critical settings. To address these limitations, we introduce Adaptive Confidence-weighted Expansion (ACE), a novel framework to enhance the trustworthiness of multimodal fusion models. ACE first enhances the multimodal space by generating new, complementary modalities from intra-modality correlations. It then employs a dual-level confidence mechanism that (1) adaptively reweighs all modalities by their reliability before fusion and (2) estimates a global trust score over the fused, final decision. To evaluate ACE, we used four challenging multi-omics datasets (BRCA, KIPAN, LGG, and ROSMAP). ACE significantly outperforms existing state-of-the-art algorithms in both classification performance and confidence calibration. Our framework provides a more stable and robust data fusion method that facilitates the use of multimodal learning in addressing high-stakes problems.

cs.LG↗

arXiv2026

Spectral-Efficient LoRa with Low Complexity Detection

In this paper, we propose a spectral-efficient LoRa (SE-LoRa) modulation scheme with a low complexity successive interference cancellation (SIC)-based detector. The proposed communication scheme significantly improves the spectral efficiency of LoRa modulation, while achieving an acceptable error performance compared to conventional LoRa modulation, especially in higher spreading factor (SF) settings. We derive the joint maximum likelihood (ML) detection rule for the SE-LoRa transmission scheme that turns out to be of high computational complexity. To overcome this issue, and by exploiting the frequency-domain characteristics of the dechirped SE-LoRa signal, we propose a low complexity SIC-based detector with a computation complexity at the order of conventional LoRa detection. By computer simulations, we show that the proposed SE-LoRa with low complexity SIC-based detector can improve the spectral efficiency of LoRa modulation up to $445.45\%$, $1011.11\%$, and $1071.88\%$ for SF values of $7$, $9$, and $11$, respectively, while maintaining the error performance within less than $3$ dB of conventional LoRa at symbol error rate (SER) of $10^{-3}$ in Rician channel conditions.

eess.SP↗

arXiv2025

Resource-efficient medical image classification for edge devices

Medical image classification is a critical task in healthcare, enabling accurate and timely diagnosis. However, deploying deep learning models on resource-constrained edge devices presents significant challenges due to computational and memory limitations. This research investigates a resource-efficient approach to medical image classification by employing model quantization techniques. Quantization reduces the precision of model parameters and activations, significantly lowering computational overhead and memory requirements without sacrificing classification accuracy. The study focuses on the optimization of quantization-aware training (QAT) and post-training quantization (PTQ) methods tailored for edge devices, analyzing their impact on model performance across medical imaging datasets. Experimental results demonstrate that quantized models achieve substantial reductions in model size and inference latency, enabling real-time processing on edge hardware while maintaining clinically acceptable diagnostic accuracy. This work provides a practical pathway for deploying AI-driven medical diagnostics in remote and resource-limited settings, enhancing the accessibility and scalability of healthcare technologies.

eess.IV↗

arXiv2025

Horizon Entropy Refined: Quantum Contributions and Cosmological Insights

We study the effects of quantum fluctuations on the event horizon area and their implications for corrections to the Bekenstein-Hawking entropy. These quantum corrections are incorporated into the framework of large-scale gravitational systems, utilizing the holographic principle to derive modified Friedmann equations. By redefining the Bekenstein-Hawking entropy, our model predicts significant alterations to the Friedmann equations within specific parameter ranges, offering novel perspectives on cosmological scales. Using distance modulus data from the Pantheon supernova sample, we demonstrate the model's potential to constrain the parameters governing quantum corrections and address unresolved cosmological issues. Crucially, our analysis reveals that quantum fluctuations can increase the area of the event horizon by up to 47\%. Beyond this threshold, theoretical predictions encounter substantial challenges when compared with observational data. This approach bridges quantum gravity and observational cosmology, opening new avenues for testing and refining theoretical models.

hep-th↗

arXiv2024

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task

Deep learning techniques have proven highly effective in image classification, but their deployment in resourceconstrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which demands even more available resources. In this work, we introduce an approach that combines saliency-guided training with quantization techniques to create an interpretable and resource-efficient model without compromising accuracy. We utilize Parameterized Clipping Activation (PACT) to perform quantization-aware training, specifically targeting activations and weights to optimize precision while minimizing resource usage. Concurrently, saliency-guided training is employed to enhance interpretability by iteratively masking features with low gradient values, leading to more focused and meaningful saliency maps. This training procedure helps in mitigating noisy gradients and yields models that provide clearer, more interpretable insights into their decision-making processes. To evaluate the impact of our approach, we conduct experiments using famous Convolutional Neural Networks (CNN) architecture on the MNIST and CIFAR-10 benchmark datasets as two popular datasets. We compare the saliency maps generated by standard and quantized models to assess the influence of quantization on both interpretability and classification accuracy. Our results demonstrate that the combined use of saliency-guided training and PACT-based quantization not only maintains classification performance but also produces models that are significantly more efficient and interpretable, making them suitable for deployment in resource-limited settings.

cs.LG↗

arXiv2024

Edge AI as a Service with Coordinated Deep Neural Networks

As artificial intelligence (AI) applications continue to expand in next-generation networks, there is a growing need for deep neural network (DNN) models. Although DNN models deployed at the edge are promising for providing AI as a service with low latency, their cooperation is yet to be explored. In this paper, we consider that DNN service providers share their computing resources as well as their models' parameters and allow other DNNs to offload their computations without mirroring. We propose a novel algorithm called coordinated DNNs on edge (\textbf{CoDE}) that facilitates coordination among DNN services by establishing new inference paths. CoDE aims to find the optimal path, which is the path with the highest possible reward, by creating multi-task DNNs from individual models. The reward reflects the inference throughput and model accuracy. With CoDE, DNN models can make new paths for inference by using their own or other models' parameters. We then evaluate the performance of CoDE through numerical experiments. The results demonstrate a $40\%$ increase in the inference throughput while degrading the average accuracy by only $2.3\%$. Experiments show that CoDE enhances the inference throughput and, achieves higher precision compared to a state-of-the-art existing method.

cs.NI↗

arXiv2023

A Tutorial on Chirp Spread Spectrum for LoRaWAN: Basics and Key Advances

Chirps spread spectrum (CSS) modulation is the heart of long-range (LoRa) modulation used in the context of long-range wide area network (LoRaWAN) in internet of things (IoT) scenarios. Despite being a proprietary technology owned by Semtech Corp., LoRa modulation has drawn much attention from the research and industry communities in recent years. However, to the best of our knowledge, a comprehensive tutorial, investigating the CSS modulation in the LoRaWAN application, is missing in the literature. Therefore, in the first part of this paper, we provide a thorough analysis and tutorial of CSS modulation modified by LoRa specifications, discussing various aspects such as signal generation, detection, error performance, and spectral characteristics. Moreover, a summary of key recent advances in the context of CSS modulation applications in IoT networks is presented in the second part of this paper under four main categories of transceiver configuration and design, data rate improvement, interference modeling, and synchronization algorithms.

eess.SP↗

arXiv2022

D2D-aided LoRaWAN LR-FHSS in Direct-to-Satellite IoT Networks

In this paper, we present a device-to-device (D2D) transmission scheme for aiding long-range frequency hopping spread spectrum (LR-FHSS) LoRaWAN protocol with application in direct-to-satellite IoT networks. We consider a practical ground-to-satellite fading model, i.e. shadowed-Rice channel, and derive the outage performance of the LR-FHSS network. With the help of network coding, D2D-aided LR-FHSS transmission scheme is proposed to improve the network capacity for which a closed-form outage probability expression is also derived. The obtained analytical expressions for both LR-FHSS and D2D-aided LR-FHSS outage probabilities are validated by computer simulations for different parts of the analysis capturing the effects of noise, fading, unslotted ALOHA-based time scheduling, the receiver's capture effect, IoT device distributions, and distance from node to satellite. The total outage probability for the D2D-aided LR-FHSS shows a considerable increase of 249.9% and 150.1% in network capacity at a typical outage of 10^-2 for DR6 and DR5, respectively, when compared to LR-FHSS. This is obtained at the cost of minimum of one and maximum of two additional transmissions per each IoT end device imposed by the D2D scheme in each time-slot.

eess.SP↗

arXiv2022

Complementarity-Entanglement Tradeoff in Quantum Gravity

Quantization of the gravity remains one of the most important, yet extremely illusive, challenges at the heart of modern physics. Any attempt to resolve this long-standing problem seems to be doomed, as the route to any direct empirical evidence (i.e., detecting gravitons) for shedding light on the quantum aspect of the gravity is far beyond the current capabilities. Recently, it has been discovered that gravitationally-induced entanglement, tailored in the interferometric frameworks, can be used to witness the quantum nature of the gravity. Even though these schemes offer promising tools for investigating quantum gravity, many fundamental and empirical aspects of the schemes are yet to be discovered. Considering the fact that, beside quantum entanglement, quantum uncertainty and complementarity principles are the two other foundational aspects of quantum physics, the quantum nature of the gravity needs to manifest all of these features. Here, we lay out an interferometric platform for testing these three nonclassical aspects of quantum mechanics in quantum gravity setting, which connects gravity and quantum physics in a broader and deeper context. As we show in this work, all of these three fundamental features of quantum gravity can be framed and fully analyzed in an interferometric scheme.

gr-qc↗

arXiv2021

Quantum Steering Ellipsoid and Unruh Effect

Quantum steering is a perplexing feature at the heart of quantum mechanics that provides profound implications in understanding the nature of physical reality. On the other hand, the effect of relativistic features on quantum systems is vital in understanding the underlying foundations of physics. In this work, we study the effects of Unruh acceleration on the quantum steering of a two-qubit system. In particular, we consider the so-called quantum steering ellipsoid and the maximally-steered coherence in a non-inertial frame and find closed-form analytic expressions for the role of the Unruh acceleration in these quantities. Analyzing the conditions for the steerability of the system, we develop a geometric description for the effect of Unruh acceleration on the quantum steering of a two-qubit system.

quant-ph↗

arXiv2020

Constraint on the mass of fuzzy dark matter from the rotation curve of the Milky Way

Fuzzy Dark Matter (FDM) is one of the recent models for dark matter. According to this model, dark matter is made of very light scalar particles with considerable quantum mechanical effects on the galactic scale, which solves many problems of the cold dark matter (CDM). Here we use the observed data from the rotation curve of the Milky Way (MW) Galaxy to compare the results from FDM and CDM models. We show FDM adds a local peak on the rotation curve close to the center of the bulge, where its position and amplitude depend on the mass of FDM particles. By fitting the observed rotation curve with our expectation from FDM, we find that the mass of FDM is $m = 2.5^{+3.6}_{-2.0} \times10^{-21}$eV. We note that the local peak of the rotation curve in MW can also be explained in the CDM model with an extra inner bulge model for the MW Galaxy. We conclude that the FDM model explains this peak without a need for extra structure for the bulge.

astro-ph.GA↗

arXiv2019

Investigation of two colliding solitonic cores in Fuzzy Dark Matter models

One of the challenging questions in cosmology is the nature of dark matter particles. Fuzzy Dark Matter (FDM) is one of the candidates which is made of very light ($m_{FDM}\simeq 10^{-22}-10^{-21}$ eV) bosonic particles with no self-interaction. It is introduced by the motivation to solve the core-cusp problem in the galactic halos. In this work, we investigate the observational features from FDM halo collisions. Taking into account the quantum wave-length of the condensed bosonic structure, we determine the interference of the wave function of cores after collision. The fringe formation in the wave function is associated to the density contrast of the dark matter inside the colliding galaxies. The observational signatures of the fringes of the distribution of the dark matter are (i) on the lensing of the background sources, (ii) accumulation of the baryonic plasma tracking the interference of the FDM potential and (iii) excess in the X-ray emission from dense regions. Finally, we provide prospects for the observations of quantum wave features of FDM in the colliding galaxies. The NGC6240 colliding galaxy at the redshift of $z=0.024$ is a suitable candidate for this study. No signal is detected from the fringes in the Chandra data and taking into account the angular resolution of the telescope, we put constrain of $m> 7 \times10^{-23}$ eV on the mass of FDM particles.

astro-ph.CO↗

arXiv2019

Speed limit of quantum dynamics near the event horizon of black holes

Quantum mechanics imposes a fundamental bound on the minimum time required for the quantum systems to evolve between two states of interest. This bound introduces a limit on the speed of the dynamical evolution of the systems, known as the quantum speed limit. We show that black holes can drastically affect the speed limit of a two-level fermionic quantum system subjected to an open quantum dynamics. As we demonstrate, the quantum speed limit can enhance at the vicinity of a black hole's event horizon in the Schwarzschild spacetime.

hep-th↗

arXiv2016

Photocurrent Enhancement of Graphene Photodetectors by Photon Tunneling of Light into Surface Plasmons

We demonstrate that surface plasmon resonances excited by photon tunneling through an adjacent dielectric medium enhance photocurrent detected by a graphene photodetector. The device is created by overlaying a graphene sheet over an etched gap in a gold film deposited on glass. The detected photocurrents are compared for five different excitation wavelengths, ranging from nm to nm. The photocurrent excited with incident p-polarized light (the case for resonant surface plasmon excitation) is significantly amplified in comparison with that for s-polarized light (without surface plasmon resonances). We observe that the photocurrent is greater for shorter wavelengths (for both s and p-polarizations) due to the increased photothermal current resulting from higher damping of surface plasmons at shorter wavelength excitation. Position-dependent Raman spectroscopic analysis of the optically-excited graphene photodetector indicates the presence of charge carriers near the metallic edge. In addition, we show that the polarity of photocurrent switches across the gap as the incident light spot moves across the gap. Graphene-based photodetectors offer a simple architecture which can be fabricated on dielectric waveguides to exploit the plasmonic photocurrent enhancement of the evanescent field for detection. Applications for these devices include photo-detection, optical sensing and direct plasmonic detection.

physics.optics↗

arXiv2015

Curved Gratings as Plasmonic Lenses for Linearly Polarised Light

The ability of curved gratings as sectors of concentric circular gratings to couple linearly polarized light into focused surface plasmons is investigated by theory, simulation and experiment. Curved gratings, as sectors of concentric circular gratings with four different sector angles, are etched into a 30-nm thick gold layer on a glass coverslip and used to couple linearly-polarised free space light at nm into surface plasmons. The experimental and simulation results show that increasing the sector angle of the curved gratings decreases the lateral spotsize of the excited surface plasmons, resulting in focussing of surface plasmons which is analogous to the behaviour of classical optical lenses. We also show that two faced curved gratings, with their groove radius mismatched by half of the plasmon wavelength (asymmetric configuration), can couple linearly-polarised light into a single focal spot of concentrated surface plasmons with smaller depth of focus and higher intensity in comparison to single-sided curved gratings. The major advantage of these structures is the coupling of linearly-polarised light into focused surface plasmons with access to and control of the plasmon focal spot, which facilitates potential applications in sensing, detection and nonlinear plasmonics.

physics.optics↗