This paper demonstrates a method for capturing, analyzing, and replicating FM radio station signals. Using RTL-SDR and CUBIC-SDR software, signals are transmitted to a PC, converted to .wav, and processed in MATLAB via Fast Fourier Transform. Simulated transmission through an FM transmitter replicates original conditions, providing comparison of Fourier analysis graphs for signal equivalence. This systematic approach ensures accurate signal replication, contributing to advancements in signal processing and communication technology.

Machine learning can be used as a systematic method to non-algorithmically "program" quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate "building blocks", eliminating that difficult step and potentially reducing unnecessary complexity. In addition, the machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. Here we apply our prior work in quantum machine learning technique, to create a QGAN, a quantum analog to the classical Stylenet GANs developed by Kerras for image generation and classification. A quantum system is used as a generator and a separate quantum system is used as a discriminator. The generator Hamiltonian quantum parameters are augmented by quantum "style" parameters which play the role of the style parameters used by Kerras. Both generator parameters are trained in a GAN MinMax problem along with quantum parameters of the discriminator Hamiltonian. We choose a problem to demonstrate the QGAN that has purely quantum information. The task is to generate and discriminate quantum product states. "Real" product states are generated to be detected and separated from "fake" quantum product states generated by the quantum generator. The problem of detecting quantum product states is chosen to demonstrate the QGAN because is well known as purely quantum mechanical, has no classical analog and is an open problem for quantum product states of more than 2 qubits. With proper encoding of image pixels into quantum states as density matrices, the method demonstrated here for this quantum problem is applicable to GAN image generation and detection, the problem so successfully demonstrated by Kerras's classical GAN, but can be hosted on and taking advantage of the nature of quantum computers.

As a generalization of the continuous-time pipeline (CTP), we present a theoretical model that can be used to analyze any conventional continuous-time (CT) analog-to-digital converter (ADC). In addition to providing a unified approach for deriving reconstruction filter coefficients, the general model enables new ADC architectures that do not fit in the framework of neither the cascaded continuous-time sigma-delta modulators (CT-Σ∆Ms), nor the CTP ADCs. As an example exploiting this design flexibility, we present a 7th order Leapfrog ADC. The aggressive 7th order filter enables a low oversampling ratio (OSR), while stability is ensured by local digital feedback with single-bit quantizers. The modular structure makes it well-suited for a programmatic design methodology, and the layout of the ADC is generated using a custom-made Python tool for analog layout compilation. Implemented in a 130nm CMOS technology, the prototype achieves a dynamic range of 82 dB with an OSR of 11, while consuming 2.5 mW from a 1.5 V supply. The resulting Schreier figure-of-merit (FOM) is 168 dB.

Electromyogram (EMG)-informed neuromusculoskeletal (NMS) models can predict physiologically plausible muscle forces and joint moments. However, calibrating model parameters (e.g., optimal fiber length, tendon slack length) to the individual is time-consuming, with the optimization often requiring hours to converge and typically not accounting for unrecorded muscle excitations. This study addresses these limitations by incorporating differentiable physics and muscle synergies into the calibration of NMS models. Methods: We implemented an NMS model with auto-differentiable Hill-type muscles, enabling the use of adaptive gradient descent optimizers. Two types of calibration were evaluated: a standard EMG-driven approach and a synergy-hybrid approach that also synthesized unrecorded excitations. These methods were evaluated using upper and lower limb data, each from a single participant. Results: The calibration time was reduced by up to 26 times while maintaining comparable accuracy in moment predictions. Compared to the EMG-driven calibration, the synergy-hybrid calibration improved the estimates of model parameters for reduced number of EMG channels. Conclusion: Autodifferentiable Hill-type muscle models greatly reduce NMS model calibration time and enables the synthesis of unrecorded muscle excitations through muscle synergies, facilitating the calibration of all muscle parameters. Significance: This new rapid calibration could support deployment of NMS models in time-sensitive applications, including real-time biomechanical analyses and personalized neurorehabilitation.

Training models to excel across a wide range of linguistic tasks often encounters the challenge of balancing task complexity with learning efficiency, particularly when dealing with the inherent variability in real-world data. Introducing a dynamic difficulty adjustment mechanism driven through reinforcement learning offers a novel and significant approach, as it allows models to adapt to varying levels of task difficulty in real-time, promoting robust learning without the pitfalls of overfitting or underfitting. The research details the development and implementation of a reinforcement learning framework that dynamically modulates task complexity, leveraging carefully defined difficulty metrics and a sophisticated reward function to guide the model's learning process. Results demonstrate that this adaptive calibration significantly enhances model performance, generalization, and computational efficiency, confirming the critical role of dynamic adjustment in advancing model robustness and versatility. The implications of this approach extend to various specialized domains, suggesting that the strategic use of dynamic difficulty adjustment could lead to the development of more resilient and adaptable models capable of handling complex linguistic challenges with greater accuracy and efficiency.

CHARGE (Charged Hyperspherical Autoencoder with Repulsive Gradient Encoding) is a novel autoencoder designed with a unique latent space that is hyperspherical and of n dimensions. The model leverages a specialized loss function based on Coulomb's Law or the Inverse Square Law, promoting a more effective and efficient latent space. This approach enables CHARGE to perform better on generative AI tasks compared to traditional Variational Autoencoders (VAEs), as it is deterministic rather than probabilistic. The results show that CHARGE provides superior performance in generating realistic data and encoding meaningful latent representations, making it a promising candidate for various generative tasks in AI.

The mechanical motion of permanent magnets is crucial in applications such as motors, wireless power transfer, communication systems, and biomedical devices. These systems often rely on either resonant oscillatory motion or continuous rotational motion of permanent magnets, with performance and stability influenced by factors like temperature variations, material properties, and nonlinear effects. In this paper, we introduce an adaptive driving circuit using a negative impedance converter (NIC) that autonomously sustains resonance or continuous rotation in magneto-mechanical systems without the need for sensors or active tuning. The NIC-based driving circuit also incorporates a saturable gain to regulate and maintain the desired oscillation amplitude and rotational speed using simple DC voltage control. Experimental results validate the system's ability to maintain resonance or continuous rotation under varying conditions, demonstrating the robustness and practicality of this driving circuit.

Artificial General Intelligence (AGI) is different from specialized Artificial Intelligence (AI) in that it can understand and work with meanings, mimicking human mental processes. Existing AI platforms are not aware of the significance of the things they interact with unlike deep chess or facial recognition systems. They rely on statistical patterns instead of semantic understanding and hence perform narrow tasks well rather than grasping higher level concepts.A very essential task today is defining meaning. Thus, this paper outlines a roadmap aimed at formalizing the notions of knowledge and meaning in such a manner that may facilitate their incorporation into AGI. Today's definitions are not good enough because they are not strict or machine-friendly. Consequently, we suggest a systematic an(AP) approach which should be used to describe what we mean with mondo e sense making (or rather enc "oding"), c term s that will be introduced below.. The proposed approach aims to bridge the gap between human cognitive capabilities and artificial systems, advancing the field towards the realization of true AGI.

In today's dynamic and competitive digital era, companies are leveraging cloud technology, machine learning, and data visualization techniques to reinvent their business processes. The combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise network to hold massive volumes of data and provide employees and customers easy access to these cloud data. These massive collections of complex data sets are facing security challenges. While cloud platforms are more vulnerable to security threats and traditional security technologies are unable to cope with the rapid data explosion in cloud platforms, machine learning powered security solutions and data visualization techniques are playing instrumental roles in detecting security threat, data breaches, and automatic finding software vulnerabilities. The purpose of this paper is to present some of the widely used cloud services, machine learning techniques and data visualization approach and demonstrate how to integrate cloud service, data analytic and machine learning techniques that can be used to detect and reduce cyber risks associated with the modern cloud based infrastructure. In this paper I applied the machine learning supervised classifier to design a model based on wellknown UNSW-NB15 dataset to predict the network behavior metrics and demonstrated how data analytics techniques can be integrated to visualize network traffics.

Who dominates the destiny of the world, humans or artificial intelligence (AI)? This question strikes at the very heart of contemporary humanity's existential anxieties about its future. If we want to seriously consider whether or not unfriendly AI 'neurons' pose any threat to human civilisation and humanity's continual existence and evolution in the Universe, we need to know as much as possible about the Universe in which we find ourselves, our place in it, and what cognition, consciousness and mentality really are. How might we combine philosophical, cognitive science and technological perspectives, to explore the evolving relationship between humans and AI, in order to engage and address the questions at the core of this human-AI complex, namely the future of civilisation-what will it look like, who can claim to be our successors, towards what goals and ends? The evolution and development of human cognition as well as the emergence of AI can help us define these potential paths of future development. Where do we stand today, in relation to our own history and development and to the possibilities that artificial intelligence can offer us? The essay explores the ethical, social and existential questions that arise from the increasing automation of artificial intelligence and how it relates to the story of humanity, from its origins to its contemporary cultural expression. It also underscores the significance of holistic approaches to apprehending and addressing the risks that come with AI development. Such approaches should combine findings from various fields namely philosophy, morality, psyche and technology so as to manage a complicated set of problems. To sum up, this summary highlights the critical necessity for sophisticated viewpoints that go beyond simple man versus machine divisions. It is rather proposing a situation where humans use AI as an instrument for improving collective happiness and ensuring responsible management over technological advances and the larger life system.

We analytically demonstrate how the properties of local cascaded metasurface pairs can be mapped to those of single non-local metasurfaces such that their external field scattering remains identical regardless of the particular incident field. This mapping is obtained for two-dimensional problems by exploiting Love's equivalence principle and fundamental linear operations. We verify the mapping with simulated data and show that the mapping preserves the losslessness, passivity, and reciprocity characteristics of the original local cascaded metasurface pair.

The sensitivity of distribution network losses to the change in net power at the network nodes (and phases of each node for unbalanced distribution systems) conveys key messages about the need to increase or decrease the net power to reduce the network losses. Similar information is provided by the total network losses allocated to nodes and phases, determined by a simple calculation based on the power flow solution. However, the allocated losses are not formally equivalent to the sensitivities of network losses to net load variation. This paper provides original insights into the interpretation and application of the allocated losses in radial distribution systems. New analytical formulations of the sensitivity of the total losses with respect to the node current magnitude are provided, starting from the power flow solution for balanced and unbalanced radial distribution networks. Two fundamental results are then presented for the first time: (i) the sign of the allocated losses is identical to the sign of total loss sensitivities with respect to the node current magnitudes, and (ii) the increase/reduction of the total losses for small net power changes in a node are determined by calculating the product of the sign of the allocated losses and the sign of the net node power, without the need of carrying out further power flow calculations. The results are discussed showing solutions obtained on balanced and unbalanced radial distribution systems with time-varying loads and local generations, even in reverse power flow conditions.

This study deals with flexibility quantification from space and water heating in about a million houses equipped with heat pumps, for providing emergency power support in a renewable dominated power-system. As electricity consumption determines the flexibility potential, this is estimated using integrated physics-based models involving a heat pump, space and water heating. The flexibility estimations are between 2.1 GW and 0.5 GW, at outdoor ambient temperatures ranging between -10◦C and 10◦C respectively, for a power-system with a dimensioning fault of 1.45 GW. The reduction in electric energy is between 5.4 GWh and 3.2 GWh, when the indoor and water temperatures are reduced from 20◦C and 55◦C, to 18◦C and 50◦C respectively over 17.25 hours, considering 24 hours recovery period. Additionally, electricity consumption as a function of time serves as valuable information for power balancing. Furthermore, a modified Nordic-32 bus system with a high share of renewable power installations is proposed. In this system, the loss of a major generation corresponding to 12% of the load, causes an instantaneous frequency deviation below 48 Hz. This can be limited by providing emergency power support using house heating flexibility corresponding to at least 5.9% of the load.

The exponential growth in urban populations and the consequent surge in vehicular traffic have rendered traditional traffic management systems inadequate. These systems, reliant on static timing mechanisms, are unable to adapt to real-time traffic conditions, resulting in inefficiencies, congestion, and delayed emergency responses. This paper presents an innovative Traffic Management System (TMS) that leverages artificial intelligence (AI) and image recognition technologies to dynamically manage traffic flow. The system aims to optimize signal timings, prioritize emergency vehicles, and promptly respond to traffic incidents, thereby enhancing overall transportation efficiency and urban mobility.

In this paper, we investigated semantic communication for multi-task processing using an information-theoretic approach. We introduced the concept of a "semantic source", allowing multiple semantic interpretations from a single observation. We formulated an end-to-end optimization problem taking into account the communication channel, maximizing mutual information (infomax) to design the semantic encoding and decoding process exploiting the statistical relations between semantic variables. To solve the problem we perform datadriven deep learning employing variational approximation techniques. Our semantic encoder is divided into a common unit and multiple specific units to facilitate cooperative multi-task processing. Simulation results demonstrate the effectiveness of our proposed semantic source and system design when statistical relationships exist, comparing cooperative task processing with independent task processing. However, our findings highlight that cooperative multi-tasking is not always beneficial, emphasizing the importance of statistical relationships between tasks and indicating the need for further investigation into the semantically processing of multiple tasks.

The release of the Citizens Broadband Radio Service (CBRS) spectrum has enabled private network deployments, but signal strength in this band is highly sensitive to weather conditions. While traditional signal propagation models are often constrained by their assumptions and lack adaptability to varying environments, Machine Learning (ML) models offer a flexible alternative for predicting signal strength under complex conditions. In this work, we develop a Neural Network (NN) based predictive model for CBRS signal strength, leveraging a comprehensive set of weather parameters, collected over a 24month period from a live CBRS network in Buffalo, NY. Our model demonstrates a Mean Absolute Error (MAE) of 1.83 dB in predicting hourly signal strength, and we further evaluate its generalization capability by applying a transfer learning approach to another CBRS network in Elmira, NY, achieving a MAE of 2.13 dB. This study highlights the potential of ML methods in modeling weather-induced signal variability and their applicability across different network environments.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that adversely affects the healthy development of children. The current clinical diagnosis of ASD relies on demographics and descriptions of clinical symptoms and assessments based on rating scales, which are both time-consuming and subjective. Previous research reported atypical visual patterns exhibited by ASD children, implying that eye-tracking could be a potential tool to assist in distinguishing ASD from others. However, most studies only examined a singular dimension of visual trajectory, neglecting the possible use of comprehensive information on eye scan paths. Given this, the present study introduced the FF-ASDNET - a framework designed for the automated screening of ASD individuals leveraging eye-tracking technologies in conjunction with deep representation learning and multi-scale feature fusion. Eye movement trajectory was first objectively quantified from multi-scale perspectives, including Gaze Movements (GM), Spatial Attention Distribution (SAD), and Temporal Visual Information (TVI). The residual network (ResNet) and the rectangular residual convolutional temporal network (RecResTCNN) were then proposed to learn the features of the three representations. Finally, the features of the three scales were fused using a convolutional neural network. The proposed design achieved an AUC of 85.96% and an F1 score of 0.85 on an independent dataset, showing that fusing multi-scale representations of eye scan trajectories could significantly improve the model’s performance, rendering the development of an automated ASD screening plausible. In summary, FF-ASDNET is a promising tool for screening ASD individuals, with potential applications in clinical diagnosis and intervention.

Detecting causality in coupled nonlinear dynamical systems is challenging for the classic Granger Causality (GC) paradigm due to mirage correlations arising from coupling effects. Convergent Cross Mapping (CCM) was introduced as a model-free alternative to complement GC in such scenarios, yet its performance can deteriorate considerably in the presence of noise. Many studies on cross-mapping-based causal discovery assess their models using only noise-free or minimally noised simulated systems, overlooking real-world data that are often susceptible to significant noise. To address this gap, we examine the noise sensitivity of CCM and demonstrate how simple preprocessing with averaging filter can enhance its robustness. Through experiments on the noisy Lorenz system and the realworld weather dataset ERA5, we provide insights into filter parameter selection and its impact on inference quality, offering practical guidance for noisy causal inference in nonlinear systems. Additionally, we hypothesize that in the context of the systems we study, causal information may reside predominantly in lower-frequency domains, explaining why averaging filters-by removing high-frequency noise-improve causal inference.