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As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications-focusing on challenges like pedestrian safety and adaptive traffic management-depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration. We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence.

Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic utility of interconnected communities. The implementation of city-wide automatic pedestrian counting systems provides researchers with invaluable data, enabling the development and training of deep learning applications that offer better insights into traffic and crowd flows. Benefiting from real-world data provided by the City of Melbourne pedestrian counting system, this study presents a pedestrian flow prediction model, as an extension of Diffusion Convolutional Grated Recurrent Unit (DCGRU) with dynamic time warping, named DCGRU-DTW. This model captures the spatial dependencies of pedestrian flow through the diffusion process and the temporal dependency captured by Gated Recurrent Unit (GRU). Through extensive numerical experiments, we demonstrate that the proposed model outperforms the classic vector autoregressive model and the original DCGRU across multiple model accuracy metrics.

Digital twinning is becoming increasingly vital in the design and real-time control of future wireless networks by providing precise cost-effective simulations, predictive insights, and real-time data integration. This paper explores the application of digital twinning in optimizing wireless communication systems within urban environments, where building arrangements can critically impact network performances. We develop a digital twin platform to simulate and analyze how factors such as building positioning, base station placement, and antenna design influence wireless propagation. The ray-tracing software package of Matlab is compared with Remcom Wireless InSite. Using a realistic radiation pattern of a base transceiver station (BTS) antenna, ray tracing simulations for signal propagation and interactions in urban landscapes are then extensively examined. By analyzing radio heat maps alongside antenna patterns, we gain valuable insights into optimizing wireless deployment strategies. This study highlights the potential of digital twinning as a critical tool for urban planners and network engineers.

Multi-Layer Perceptrons (MLP) are powerful tools for representing complex, non-linear relationships, making them essential for diverse machine learning and AI applications. Efficient hardware implementation of MLPs can be achieved through many hardware and architectural design techniques. These networks excel at predictive modeling and classification tasks like image classification, making them a popular choice. Approximate computing techniques are increasingly used to optimize critical path delay, area, power, and overall hardware efficiency in high-performance computing systems through controlled error and related trade-offs. This study proposes a hardware MLP neural network implemented in 45nm CMOS technology, in which MAC units of the neurons incorporate error and power controllable approximate multipliers for classification of the MNIST dataset. The optimized network consists of 10 neurons within the hidden layers, occupying 0.026mm2 of area, with 5.55mW at 100MHz frequency in accurate mode and 4.81mW in lowest accuracy mode. The experiments indicate that the proposed design achieves a maximum rate of 13.33% decrease overall and 24.78% in each neuron's power consumption with only a 0.92% decrease in accuracy in comparison with accurate circuit., Comment: 6 pages, 7 figures, accepted at The 6th Iranian International Conference on Microelectronics (IICM2024)

We propose an approach to model spatial heterogeneity in SIR-type models for the spread of epidemics via \emph{nonlocal aggregation terms}. More precisely, we first consider an SIR model with spatial movements driven by nonlocal aggregation terms, in which the inter-compartment and intra-compartment interaction terms are distinct, and modelled through smooth interaction kernels. For the Cauchy problem of said model we provide a full well-posedness theory on $\R^2$ for $L^1\cap L^\infty \cap H^1$ initial conditions. The existence part is achieved by considering an approximated model with artificial linear diffusion, for which existence and uniqueness is proven via Duhamel's principle and Banach fixed point, and by providing suitable uniform estimates on the approximated solution in order to pass to the limit via classical compactness techniques. To prove uniqueness, we use classical $L^2$-stability which relies on the $H^1$-regularity of the solution. In the second part of the paper we provide a brief, general discussion on the steady states for these type of models, and display a specific example of non-trivial steady states for an SIS model with aggregations (driven by a single repulsive-attractive potential), the existence of which is determined by a threshold condition for a suitable "space-dependent" basic reproduction rate.

Traffic sampling has become an indispensable tool in network management. While there exists a plethora of sampling systems, they generally assume flow rates are stable and predictable over a sampling period. Consequently, when deployed in networks with dynamic flow rates, some flows may be missed or under-sampled, while others are over-sampled. This paper presents the design and evaluation of dSamp, a network-wide sampling system capable of handling dynamic flow rates in Software-Defined Networks (SDNs). The key idea in dSamp is to consider flow rate fluctuations when deciding on which network switches and at what rate to sample each flow. To this end, we develop a general model for sampling allocation with dynamic flow rates, and then design an efficient approximate integer linear program called APX that can be used to compute sampling allocations even in large-scale networks. To show the efficacy of dSamp for network monitoring, we have implemented APX and several existing solutions in ns-3 and conducted extensive experiments using model-driven as well as trace-driven simulations. Our results indicate that, by considering dynamic flow rates, APX outperforms the existing solutions by up to 10% in sampling more flows at a given sampling rate., Comment: 9 pages, 8 figures

We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual ground-truth object annotation (labeling) with an automated and configurable process. Boundless is based on the Unreal Engine 5 (UE5) City Sample project with improvements enabling accurate collection of 3D bounding boxes across different lighting and scene variability conditions. We evaluate the performance of object detection models trained on the dataset generated by Boundless when used for inference on a real-world dataset acquired from medium-altitude cameras. We compare the performance of the Boundless-trained model against the CARLA-trained model and observe an improvement of 7.8 mAP. The results we achieved support the premise that synthetic data generation is a credible methodology for training/fine-tuning scalable object detection models for urban scenes.

In the present analysis, we explore a new version of dark energy called Barrow holographic dark energy within the framework modified gravity called $f(Q,T)$ gravity by adopting the simple homogeneous, isotropic, and spatially flat Friedmann-Robertson-Walker (FRW) model of the universe. Our goal is to understand how the universe evolved over time. To do this, we use parameterizetion of Hubble's parameter method. We then use a powerful tool called Monte Carlo Markov Chain to find the best values for the constants in our formula. We do this by comparing our formula to actual data from observations of the universe. Once we have the best values for the constants, we calculate other important parameters that describe the universe's evolution. These include: Deceleration parameter which measures how quickly the expansion is slowing down. We found $q_0 = -0.601^{+0.0131}_{-0.0131}$. Equation of state parameter to measures the properties of dark energy. We find $\omega_0 = -0.7018^{+0.0101}_{-0.0101}$. We also study the stability and energy conditions along with the state-finder and $O_m(z)$-parameter of our model to ensure it's consistent with our understanding of the universe., Comment: 11 pages, 7 figures. arXiv admin note: text overlap with arXiv:2408.00022

We present a novel data-driven simulation environment for modeling traffic in metropolitan street intersections. Using real-world tracking data collected over an extended period of time, we train trajectory forecasting models to learn agent interactions and environmental constraints that are difficult to capture conventionally. Trajectories of new agents are first coarsely generated by sampling from the spatial and temporal generative distributions, then refined using state-of-the-art trajectory forecasting models. The simulation can run either autonomously, or under explicit human control conditioned on the generative distributions. We present the experiments for a variety of model configurations. Under an iterative prediction scheme, the way-point-supervised TrajNet++ model obtained 0.36 Final Displacement Error (FDE) in 20 FPS on an NVIDIA A100 GPU., Comment: CVPR 2024 Workshop POETS Oral

In this study the effects of Joule heating parameter on entropy generation and heat transfer in MHD flow inside a channel is investigated by means of Physics-Informed Neural Networks (PINNs) in form of a parametric analysis in addition to exploring the solution to the ill-posed problem. All of the governing equations are reformulated in terms of first order derivatives and the dimensionless form of the governing equations has been employed to further lessen the number of parameters and achieve better compatibility with loss function terms. Dimensionless groups such as Reynolds number, Prandtl number, Hartmann number and Joule heating parameter have been designated as the input for the neural network in order to perform the parametric study. Besides achieving high accuracy for case of parameters confined in the predefined ranges, the generalization ability of the method is depicted by solving the problem for the cases where the dimensionless parameters were outside of the assumed ranges. Moreover, the ability of handling Neumann boundary conditions is also investigated in the present study despite being neglected prevalently in the literature concerning PINNs. The effects of Joule heating parameter on entropy generation are researched using a parametric approach utilizing PINNs which is another novel aspect of the article at hand. As a concluding remark, an ill-posed problem is also defined such that the Joule heating parameter is included in a learning process and the method has been able to determine Joule heating parameter as a parameter of interest alongside other learnable parameters of the neural network such as weights and biases.

Background: The treatment of multiple sclerosis implies, beside protecting the body, the preserving of mental functions, considering how adverse cognitive decay affects quality of life. However a cognitive assessment is nowadays still realized with neuro-psychological tests without monitoring cognition on objective neurobiological grounds whereas the ongoing neural activity is in fact readily observable and readable. Objective: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms discriminate quantitatively normal from pathological patterns in the EEG signal. Methods: Baseline recordings from a prior EEG study of 93 subjects, 37 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets. Results: Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 82\% while using bands alone arrived at 64\%. A few feature combinations were identified to drive this distinction. Conclusions: Brain states analysis distinguishes successfully controls from patients with MS. Our results imply that this data-driven cross-sectional comparison of EEG data may complement customary diagnostic methods in neurology and psychiatry. However, thinking ahead in terms of quantitative monitoring of disease time course and treatment efficacy, we hope having established the analytic principles applicable to longitudinal clinical studies., Comment: 9 pages, 3 figures

Conformal phased arrays provide multiple degrees of freedom to the scan angle, which is typically limited by antenna aperture in rigid arrays. Silicon-based RF signal processing offers reliable, reconfigurable, multi-functional, and compact control for conformal phased arrays that can be used for on-the-move communication. While the lightweight, compactness, and shape-changing properties of the conformal phased arrays are attractive, these features result in dynamic deformation of the array during motion leading to significant dynamic beam pointing errors. We propose a silicon-based, compact, reconfigurable solution to self-correct these dynamic deformation-induced beam pointing errors. Furthermore, additive printing is leveraged to enhance the flexibility of the conformal phased arrays, as the printed conductive ink is more flexible than bulk copper and can be easily deposited on flexible sheets using different printing tools, providing an environmentally-friendly solution for large-scale production. The inks such as conventional silver inks are expensive and copper-based printable inks suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. This work uses a low-cost molecular copper decomposition ink with reliable RF properties at different temperature and strain to print the proposed intelligent conformal phased array operating at 2.1 GHz. Proof-of-concept prototype $2\times2$ array self-corrects the deformation induces beampointing error with an error $<1.25^\circ$. The silicon based array processing part occupying only 2.58 mm$^2$ area and 83 mW power per tile.

In this study, fluid flow inside of an obstructed channel under the influence of magnetic field has been analyzed using physics informed neural networks(PINNs). Governing equations have been utilized in low-order form and the solution has been obtained in dimensionless form. Geometric and physics-related dimensionless parameters have been used as input variables of the neural network in the learning process. The radius and longitudinal position of the obstruction have also been involved in the learning process and the problem has been solved parametrically. In the successive sections of the study, inverse problem has been a matter of interest, particularly in form of obtaining the Hartmann number using the proposed method. The results have indicated that the employed method determined the Hartmann number with great accuracy and entailed proper results. In this study a thorough exploration of effects of physical and geometric parameters on the magnetically influenced flow through a duct has been carried out. The accuracy of the results obtained from the solving method proposed have been compared to results generated by common methods used in computational fluid mechanics. The parametric solution of the problem can serve as a powerful tool in optimization problems. The method was also applicable to cases where the parameters were outside of defined range, indicating it's generalization capabilities.

We introduce Constellation, a dataset of 13K images suitable for research on detection of objects in dense urban streetscapes observed from high-elevation cameras, collected for a variety of temporal conditions. The dataset addresses the need for curated data to explore problems in small object detection exemplified by the limited pixel footprint of pedestrians observed tens of meters from above. It enables the testing of object detection models for variations in lighting, building shadows, weather, and scene dynamics. We evaluate contemporary object detection architectures on the dataset, observing that state-of-the-art methods have lower performance in detecting small pedestrians compared to vehicles, corresponding to a 10% difference in average precision (AP). Using structurally similar datasets for pretraining the models results in an increase of 1.8% mean AP (mAP). We further find that incorporating domain-specific data augmentations helps improve model performance. Using pseudo-labeled data, obtained from inference outcomes of the best-performing models, improves the performance of the models. Finally, comparing the models trained using the data collected in two different time intervals, we find a performance drift in models due to the changes in intersection conditions over time. The best-performing model achieves a pedestrian AP of 92.0% with 11.5 ms inference time on NVIDIA A100 GPUs, and an mAP of 95.4%.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes ({\alpha}, \b{eta}, and {\gamma}), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype {\alpha} represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype \b{eta} signifies severe TBI with diverse clinical manifestations, and phenotype {\gamma} represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations., Comment: 25 pages, 10 figures, 4 tables

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve $\Omega(1/\log(L))$ fraction of the optimal objective value if the minimum link capacity in the network is $C_{\min}=\Omega(\log (L))$, where $L$ is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, $(1-\epsilon)$-approximation algorithms can be designed if $C_{\min} = \Omega\big(\frac{\log (L/\epsilon) } {\epsilon^2}\big)$. To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity. We further present extended simulations using real network traces with non-stationary traffic, which demonstrate that our algorithms outperform worst-case-based algorithms in practical settings.