We explore essential factors pertaining to the spatial directivity of quantum radiating source systems (QSSs), encompassing quantum antennas and quantum sensors. Our primary focus is on their capacity to control the emission of photons in specific spatial directions. We present a comprehensive definition of quantum directivity, drawing inspiration from Glauberâ\euro™s photon detection theory. This definition closely parallels the framework of analogous concepts in classical antenna theory. By conducting thorough conceptual and mathematical analysis, we address the challenge of characterizing the directive properties of a general QSS. Essentially, our approach presents a computational model that relies solely on the radiation field operatorâ\euro™s density as input.

Physically Unclonable Function (PUF) is an emerging hardware security primitive that provides a promising solution for lightweight security. PUFs can be used to generate a secret key that depends on the random manufacturing process variation of the device for lightweight authentication and device identification. This work proposes an optimized version of the Configurable Ring Oscillator (CRO) PUF that aims to reduce power consumption and area overhead. The proposed design eliminates the duplication of ROs, reduces the switching activity, and introduces the inter-stage delay as an additional source of randomness. The proposed PUF has been implemented in 22nm FDSOI technology using the Synopsys tools. A comprehensive security analysis has been acquired utilizing Challenge-Response Pairs collected from 8 chips. Results show an average of 49.42%, 38.25%, 9.95%, and 45.5% for uniformity, diffuseness, reliability, and uniqueness, respectively. Compared with the state-of-the-art, the proposed design achieves an area and power reduction of 75% and 65.1%, respectively. With the proposed PUF delivering 10 32 CRPs, it is classified as a strong PUF. Additionally, the proposed design passes NIST tests and achieves an average prediction accuracy of 67.1% of machine learning modeling.

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.

To design a reliable communication system utilizing millimeter-wave (mm-wave) technology, which is gaining popularity due to its ability to deliver multi-gigabit-per-second data rate, it's essential to consider the site-specific nature of the mmwave propagation. Conventional site-general stochastic channel models are often unsatisfactory for accurately reproducing the channel responses under specific usage scenarios or environments. For high-precision channel simulation that reflects sitespecific characteristics, this paper proposes a channel model framework leveraging a widely accepted 3GPP map-based hybrid channel modeling approach, and it provides a detailed recipe to apply it to an actual scenario using some examples. First, an extensive measurement campaign was conducted in typical urban macro and micro cellular environments using an inhouse dual-band (24/60 GHz) double-directional channel sounder. Subsequently, the mm-wave channel behavior was characterized, focusing on the difference between the two frequencies. Then, the site-specific large-scale and small-scale channel properties were parameterized. As an essential component for improving prediction accuracy, this paper proposes an exponential decay model for power delay characteristics of non-line-of-sight clusters, of which powers are significantly overestimated by deterministic prediction tools. Finally, using the in-house channel model simulator (CPSQDSIM) developed for grid-wise channel data (PathGridData) generation, a significant improvement in prediction accuracy compared with the existing 3GPP map-based channel model was demonstrated.

In today's data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.

Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on highrate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There's a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88%. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks.

In this article, we introduce the concept of "Quantum Expansion Rate", a novel measure in the field of quantum cosmology that seeks to establish a connection between the microscopic properties of light and the large-scale expansion of the universe. The quantum expansion rate, \(v_u\), is defined through a relationship between the speed of light in a vacuum, the wavelength of light, and a specific reference distance. Through mathematical analysis, we explore how different reference wavelengths can influence \(v_u\), revealing potential implications of this factor on the universe's expansion rate. The relevance of these findings in the context of the universe's early events, such as particle annihilation, and their potential impact on the cosmos's structure and evolution, is discussed. The proposal of  as a tool for understanding the interaction between quantum mechanics and cosmology raises significant questions and opens new avenues for future research.

A document by Anders Buvarp . Click on the document to view its contents.

Lidar odometry allows for precise and reliable estimation of a vehicles movement over short durations, but suffers from drift over long trajectories. In this work, we propose an approach for reducing this drift by incorporating publicly available data from OpenStreetMap (OSM). Our approach is an extension to conventional Iterative Closest Points (ICP)-based lidar odometry with an additional registration against a map derived from the building outlines of OSM. For this, we propose a complementary filter inspired fusion step, capable of reducing the drift of lidar odometry while simultaneously avoiding the integration of erroneous map data. A detailed evaluation of our method on the KITTI odometry and Boreas data sets, as well as on our own recorded data, shows our approach to be applicable in a wide variety of urban and suburban situations, capable of reducing the drifting lidar odometry and yielding state-of-the-art results when compared to other OSM map-assisted visual odometry approaches.

Typical e-bikes magnetoelastic torque sensors employ bulky fluxgate magnetometers to resolve few μT/Nm. Instead, we propose the use of a miniaturized gradiometric arrangement of GaAs Hall plates sensitive to the radial magnetic field and a design approach that reduces the impact of axial misalignment. The proposed architecture provides comparable performance to commercial solutions, with reduced sizing and simplified magnetization requirements. Experiments conducted on a prototype with a full-scale (FS) upper limit of 200 Nm show a resolution of 7 mNm/ √ Hz with a combined standard error of 1.85%FS.

A document by Ranjan Sapkota. Click on the document to view its contents.

As healthcare facilities digitize medical records of patients, particularly in the form of electronic health records (EHRs) and use revolutionary technologies like Artificial Intelligence (AI), it is critical to protect privacy, credibility, and availability of private data. Numerous benefits have been observed for employing AI in the healthcare sector, however, there also exist multiple risks. Medical experts have expressed concern about how AI can manage clinical and patient data without violating the Health Insurance Portability and Accountability Act (HIPAA). This paper investigates the complex association between AI, healthcare, and data privacy, focusing on legal frameworks, particularly HIPAA in the United States. The article first discusses HIPAA in detail along with several laws made under this act. It then delves into the gaps and inconsistencies of HIPAA and finally throws light on the potential role of AI itself in preserving data privacy in healthcare. It examines the impact of these legislations on medical professionals, technology vendors, as well as patients, emphasizing the importance of strong data regulatory structures and risk reduction efforts to prevent cybersecurity attacks and illicit data access.

Mega satellite networks recently emerged to complement the current terrestrial infrastructure to attain global coverage and faster communication links. However, with increased congestion in the radio spectrum, migrating satellite communication to high frequencies can provide higher data rates. Nevertheless, signal fading due to weather conditions and atmospheric losses becomes significant at such high frequencies. In this paper, we leverage tools from stochastic geometry to provide an analytical framework that captures the effect of high frequency fading for satellite constellations in the downlink. The analytical framework presented exploits the spatial diversity provided by the satellite network and examines the performance of the dominant satellite (satellite providing the best quality of service). Results show a close fit to the performance of practical networks and can thus provide network designers with an analytical benchmark to assist with network tuning.

Over the past few years YouTube has became a popular site for video broadcasting and earning money by publishing various different skills in the form of videos. For some people it has become a main source to earn money. Getting the videos trending among the viewers is one of the major tasks which each and every content creator wants. Popularity of any video and its reach to the audience is completely based on YouTube's Recommendation algorithm. This document is a dataset descriptor for the dataset collected over the time span of about 45 days during the Israel-Hamas War.

Explicit results are derived using simple and exact methods for the joint and marginal queue-length distributions for the M/M/c queue with two non-preemptive priority levels. Equal service rates are assumed. Two approaches are considered. One is based on numerically robust quadratic recurrence relations. The other is based on a complex contour-integral representation that yields exact closed-form analytical expressions, not hitherto available in the literature, that can also be evaluated numerically with very high accuracy.

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital-compatible bandwidths via the Vernier effect. Due to the use of dual-polarization states, the number of channelized spectral segments, and thus the RF instantaneous bandwidth (with a certain spectral resolution), can be doubled. In our experiments, we used 20 microcomb lines with ~ 49 GHz FSR to achieve 20 channels for each polarization, with high RF spectra slicing resolutions at 144 MHz (TE) and 163 MHz (TM), respectively; achieving an instantaneous RF operation bandwidth of 3.1 GHz (TE) and 2.2 GHz (TM). Our approach paves the path towards monolithically integrated photonic RF receivers (the key componentsâ\euro”active and passive MRRs are all fabricated on the same platform) with reduced complexity, size, and unprecedented performance, which is important for wide RF applications with digital-compatible signal detection.

Memristors with various switching characteristics could realise low-power reconfigurable electronics. This paper reports the coexistence of nonvolatile and volatile switching characteristics in Ag/Ti/ZnO/TiN CBRAM memristor devices and examines the feasibility of the cycle-to-cycle fluctuation of both characteristics for memory and random number generator (RNG) applications. A circuit design exploiting the nonuniformity to produce random bits is proposed. We show that random bits can be produced by the circuit regardless of the nonvolatile switching characteristics; nevertheless, volatile behavior may be preferred for low-power operation. Nonvolatile-tovolatile switching transformation can be achieved by inserting an excessively thick barrier layer or applying a low current compliance to form a metastable conducting bridge. We found a close correlation between the statistical distribution of the switching nonuniformity and the randomness parameter for making reliable random bits.

An Artificial General Intelligence (AGI) system must intelligently deal with the semantics of the situations it encounters. The flexibility required for intelligent responses is at odds with the fixed parameter spaces of statistical methods or the limitations of the rule-based syntax of the computing paradigms on which systems can now be built. Overcoming these limitations requires philosophical insights from the work of Kant, Wittgenstein, Heidegger, and Gadamer, covering ontology, epistemology, hermeneutics, axiology, and teleology. Specifically, additional teleological computations (to determine the purpose served, independently from the computations responsible for responding appropriately in the world), are necessary to overcome these shortcomings.These insights suggest using a new abstraction to satisfy the necessity for two computational processes to execute in parallel on their respective, related but different representations. The processes must consist of algorithms specifiable a priori, to apply in all possible situations. In this paper, we address some specific difficulties with concepts and show how these difficulties may be overcome using the new abstraction of higher-level perceptions proposed in the Subjective Emergence of Objective Minds (SEOM) model developed earlier.In this paper, we will focus on the representations and teleological computations necessary to give purpose to the actions of autonomous intelligent agents.