The demand for more accurate and visually pleasing images is increasing with the number of digital photos taken daily. However, the images captured by modern cameras are degraded by noise, which leads to deteriorated visual image quality. The de-noising approaches existing in the literature cannot reduce noise without losing image features (edges, corners, and other sharp structures). The developed de-noising method can detect and de-noise the noisy pixels without losing image features at higher and lower noise levels. The present approach can filter the noise at or above 90% noise density and preserve image details to enhance the image processing task.

This paper explores the intersection of Artificial Intelligence (AI) and digital storytelling in marketing, focusing on how AI-driven techniques can enhance emotional attachment and influence consumer behavior. With the rapid advancement of AI, its integration into marketing strategies has become crucial, particularly for personalizing consumer experiences and enhancing brand narratives. This study investigates AI's role in creating emotionally engaging narratives, a largely unexplored area in marketing and advertising. The research is motivated by the need to understand the dynamics between AI-driven techniques and emotional attachment in digital marketing. The paper hypothesizes a significant relationship between consumers' emotional attachment to brands, influenced by AI-driven storytelling, and their purchasing behavior. A mixed-methods research approach is employed to test this hypothesis, combining a survey with detailed interviews. The study assesses how emotional attachment, influenced by AI and storytelling, impacts consumer purchasing decisions and brand loyalty in online shopping. It also evaluates the effectiveness of AI-driven storytelling techniques in digital marketing campaigns from the perspective of online consumers. Preliminary findings suggest that while emotional attachment significantly influences consumer purchasing behavior, other factors also play a crucial role. The study reveals that AI's role in marketing is valued, but the essence of storytelling should remain grounded in human experiences. The paper concludes that the future of digital marketing lies in a harmonious blend of AI and traditional storytelling, where AI's data-driven insights complement the authenticity and emotional resonance of narratives. This research contributes to the understanding of AI's potential in enhancing emotional attachment-driven branding and online selling performance, offering insights for future strategies in digital marketing.

We present studies of a novel plastic scintillator branded M600, which was developed and provided by Target Systemelektronik. This new material is, in contrast to other solid organic scintillators offered by commercial providers, based on a polyurethane matrix complemented with scintillating and wavelength-shifting additives. This paper covers measurements of absolute light output, LO, (photons per 1 MeV-ɣ, ɣ-non-proportionality (NP), light pulse shapes, and neutron-gamma (n/ɣ) discrimination. The measurements were carried out either using standard calibration gamma sources (LO, NP) or in a mixed field of neutron and ɣ radiation from an intense (~4 × 105 neutrons/s/4π) AmBe source (n/ɣ discrimination). The measured light output was 9650±1000 ph/MeV, and the PSD's figure of merit (FoM) was found as 2.2±0.1 at 1000 keVee for Ø1" × 1" M600 sample. The bigger sample (Ø2" × 2") exhibits about 25% lower LO and poorer FoM when compared to the Ø1" × 1" M600, due to self-absorption.

The IIoT network involves smart sensors, actuators, and technologies extending IoT capabilities across industrial sectors. With the rapid development in connected technology and communications in industrial applications, IIoT networks and devices are increasingly integrated into less secure physical environments. Anomaly detection in IIoT is crucial for cybersecurity. This paper proposes a novel anomaly detection model for IIoT systems, leveraging a hybrid deep learning (DL) model. The hybrid DL approach combines Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for anomaly detection in IoT edge computing. The proposed CNN+GRU model achieves a notable 94.94% accuracy, underscoring the importance of careful model selection for IIoT anomaly detection. The paper suggests exploring XGBoost with hybrid CNN+GRU architectures as a future direction for high accuracy in complex IIoT contexts. The Experimental results indicate a 96.41% accuracy, excelling in metrics like false alarm rate (FAR), recall, precision, and F1score. Based on these findings, we recommend future researchers consider advanced hybrid architectures and enhance efficiency using XGBoost with hybrid CNN+GRU. This approach holds promise for significant contributions to IIoT systems' security and Performance evolution.

This paper describes the process followed to implement a system to characterize the complex permittivity of materials in the 10-330 GHz frequency band. Firstly, the method used and the system's calibration process are shown, consisting of a double calibration TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line). Subsequently, a smoothing technique is used to improve the accuracy of the results. Finally, a test is performed on quartz and glass fiber samples, showing that the results are quite reliable over the entire measured bandwidth.

This work outlines the procedure to establish a system for assessing materials' complex permittivity and permeability within the 110-170 GHz frequency range. We present the employed methodology and the calibration procedure for the system, incorporating a dual approach using TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line) methods. Following that, a smoothing technique is used to enhance the accuracy of the results. Tests were conducted on a HIPS sample to validate the system's performance, demonstrating the results' reliability across the entire measured bandwidth.

Unsupervised ML-based approaches have emerged for driving critical decisions about training data samples to help solve challenges in many life critical applications. This paper proposes parallel and distributed computing unsupervised ML techniques to improve the execution time of different ML algorithms. Various unsupervised ML models are developed, implemented, and tested to demonstrate the efficiency, in terms of execution time and accuracy, of the serial methods as compared to the parallelized ones. We developed sequential, parallel, and distributed cloud computing unsupervised ML models based and determined the most efficient model through comparative analysis. As a case study, sequential, parallel, and distributed approaches of Simple K-Means, Minibatch K-means, and Fuzzy C-Means are investigated to study the developed models' efficiency using country datasets for multiple organizations to train and test the developed model. Parallel and distributed computing models are developed utilizing could computing architect, i.e., cloud Amazon SageMaker, to study their efficiency in the execution time and model accuracy. The results show that the proposed parallel and distributed Fuzzy C-Means outperforms the other two clustering methods in terms of execution time with 0.932ms and 0.623ms with a minimal impact on the accuracy of the developed models.

Context-aware computing applications depend on embedded hardware systems, utilizing sensors embedded in the hardware to gather real-time data and interact with specialized OS software (firmware) for autonomous processing and analysis of intelligent data. Securing embedded IC hardware systems and ensuring their trustworthiness requires an intelligent approach to effectively detect spontaneous hardware Trojans (HTs) insertions and modification attacks aiming to compromise the system's integrity, potentially leaking sensitive information or causing destruction. Implementing robust and advanced intrusion detection systems against supply chain hardware Trojan to countermeasure and continuous monitoring the behavior of these malicious hardware is essential to enable trust in context-aware computing applications. AI-enabled hardware side-channel analysis, involving power and timing assessments, assists in detection of anomalies that may signify potential Trojans. This paper propose intelligent AI approach utilizing unsupervised machine learning in conjunction with hardware side-channel analysis to eliminate the need for golden data samples and efficiently detect hardware Trojan detection. Employing unsupervised clustering, the methodology not only showcased a superior false positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts such as the K-Nearest Neighbors (KNN) classifier, Support Vector Machine (SVM), and Gaussian classifier-methods reliant on the availability of golden data for training. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a groundbreaking paradigm in hardware Trojan detection, accentuating its potential in bolstering the integrity of semiconductor IC supply chains.

This paper describes an application of a machine learning based method and compares it to classical methods for input modelling of software processing latencies for a discrete-event simulation model of a hardware-in-the-loop test bench. We apply and compare the following four different methods for input modelling: stochastic distribution functions, phase-type distributions with Markovian state changes, phase-type distributions modelled by an advanced Markov-chain, and a machine learning linear regression approach based on recurrent neural networks. We compare the different model results with the measured data using different metrics. We use two common metrics used in machine learning: the mean absolute error and the root mean square error. And we introduce two new metrics inspired by the service curve concept of network calculus. We compare the cumulative sum of the latencies describing a service curve visually and with two metrics. We discuss the advantages and disadvantages of the models for our use case. The resulting models can be used to evaluate the performance and improve the design of a hardware-in-the-loop streaming system. We focus on the playback buffer backlog and the end-to-end latency, under the challenging conditions that the underlying data does not fulfil the independent and identically distribution assumption.

Smart cities have emerged to tackle life critical challenges that can thwart the overwhelming urbanization process, such as expensive health care, increasing energy demand, traffic jams, and environmental pollution. This paper proposes efficient and high-quality cloud-based machine-learning solutions for safe urban smart city environment. For that, supervised MLbased models, i.e., regression and classification, are developed utilizing cloud-based solutions to forecast high performance in execution time and enhanced quality of the solution in terms of the accuracy of the implemented cloud-based ML solution. To predict AQI, i.e. air quality index, ML models utilize pollutants in the air data sets. The mean absolute error, mean squared error, root means the squared error, R2 score are used to validate and test the designed models. As classification models, we perform the support vector machine and random forest algorithms, which are measured using the accuracy score and confusion matrix. Execution times and accuracy of the developed models are computed and contrasted with the times for the cloud-based versions of these models. The results show that among the regression algorithms, lasso regression has an r2 score of 80 percent, while linear regression has an r2 score of 75 percent. Furthermore, among the classification models, the random forest algorithm performs better with an accuracy of 99 percent than the support vector machine approach with 95 percent accuracy. In conclusion, our findings demonstrate that run-time is minimized when models are executed on a cloud platform compared to a desktop machine. Moreover, the accuracy of our models is maintained with reduced execution time.

Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real world variations, e.g. vehicles of different shapes, positions, colours, etc. Supervised learning needs annotated datasets, and several datasets are available for developing automotive functions. However, these datasets are tremendous in volume, and labelling accuracy and quality can vary across different datasets and within dataset frames. Accurate and appropriate ground truth is especially important for automotive, as "incomplete" or "incorrect" learning can impact negatively vehicle safety when these neural networks are deployed. This work investigates ground truth quality of widely adopted automotive datasets, including a detailed analysis of KITTI MoSeg. According to the identified and classified errors in the annotations of different automotive datasets, this paper provides three different criteria collections for producing improved annotations. These criteria are enforceable and applicable to a wide variety of datasets. The three annotations sets are created to: (i) remove dubious cases; (ii) annotate to the best of human visual system; (iii) remove clear erroneous bounding boxes. KITTI MoSeg has been reannotated three times according to the specified criteria, and three state-of-the-art deep neural network object detectors are used to evaluate them. The results clearly show that network performance is affected by ground truth variations, and removing clear errors is beneficial for predicting real world objects only for some networks. The relabelled datasets still present some cases with "arbitrary"/"controversial" annotations, and therefore this work concludes with some guidelines related to dataset annotation, metadata/sub-labels, and specific automotive use-cases.

In this paper, an analytical tool of uniform 3D gainspace approach is proposed to analyze the impact of the lossy, linear and reciprocal embedding networks, as well as the lossy matching networks for near-fmax embedded amplifiers. Based on the uniform gain space approach, a super-gain-boosting technique, which involves the employment of the cross conductance to the differential pair as well as Y/Z-embedding networks is thoroughly proposed to significantly boost the power gain. Compared to the conventional gain boosting techniques, the proposed can significantly boost the Mason's U of a transistor, but also further obtain gain improvement benefiting from the intuitive uniform 3D gain-space approach. Finally, to validate the proposed analytical approach, a three-stage amplifier is implemented in a 65nm CMOS process based on the proposed super gain-boosting technique and over-push gain-boosting technique. The three-stage amplifier demonstrates a measured Psat of-1.95dBm and a maximum PAE of 2.87% at 189GHz, along with a maximum power gain of 32.1dB.

In this summer project, we perform an analysis of the intermediate layer neurons primarily through the use of CLIP-dissect, a powerful model to describe neurons, and its Soft-WPMI similarity function. Specifically, we first validate the relative accuracy of CLIP-dissect when analyzing intermediate layer neurons using ground-truth labeling, before using the large pretrained model and the Soft-WPMI similarities to perform analysis of convolutional neural networks (CNNs) such as ResNet-50. We then create an interactive GUI with the visualizations of model, layer, and neuron-level analysis to allow anyone to access the intermediate neurons of these CNNs using output from CLIP-dissect and images from Broden. We allow users to directly search for specific concepts within any network so that they can better understand the inner workings of the models that increasingly define our daily life. Our website with an interactive version of this project can be found at https://dr4nx.github.io/clip-search/index.html.

Disaggregated networks allow operators to select components from different vendors, promoting vendor neutrality. This flexibility enables the selection of best-of-breed solutions for specific network elements. By decoupling hardware and software, disaggregated networks can potentially reduce costs. Operators can choose cost-effective devices and upgrade or replace them independently. Disaggregation also facilitates the adoption of new technologies and innovations and often adheres to open standards promoting interoperability between different equipment vendors. The Yet Another Next Generation (YANG) data modeling has been identified as the preferred language to interface the management and control system. The Network Configuration Protocol (NETCONF) is gaining prominence as a Software-Defined Networking (SDN) protocol standardized by the Internet Task Force (IETF). This paper provides an overview based on a survey of the best practices employed in designing, planning, and operating a disaggregated optical network. It presents the general system architecture including the open software tools SDN controller (based on the Open Operating Network System (ONOS)), optical line system controller (OLC), the QoT estimator based on the Gaussian Noise Simulation in Python (GNPy), and the orchestrator module.

This paper presents a comprehensive framework and actionable recommendations for the ethical integration of ChatGPT and other Large Language Models (LLMs) into the academic environment from a Diffusion of Innovation Theory perspective. It provides a critical analysis of the technology's potential benefits and risks in education and proposes a conceptual adoption model using critical DOI factors including the comparative benefits it offers, its compatibility with existing systems, its level of complexity, the ability to trial it, and its observability. Thorough guidelines on different assessment methods like surveys, interviews, and case studies are recommended to evaluate these factors regarding ChatGPT uptake. Additionally, strategies are discussed to promote responsible use, such as developing guidelines, assessing biases, and aligning with education policies. We call for interdisciplinary research between stakeholders, including educators, policymakers, and AI experts, to address LLM's multifaceted impacts on education.

NisI is a Python code developed for system identification of non-ideal systems using a single observed state. Non-Ideal System (NIS) exhibits significantly complex dynamic behavior due to the presence of nonlinear and discontinuous equations, resulting in chaotic behavior. Identifying parameters for NIS is more challenging when only certain states of the system are observable. The proposed method in this study uses a Particle Swarm Optimization (PSO) meta-heuristic to minimize the difference between experimental data and a proposed nonlinear model. This code has successfully identified all unknown parameters of the non-ideal system in experimental systems.

Unmanned aerial vehicles (UAVs), or drones, are transforming industries due to their affordability, ease of use, and adaptability. This emphasizes the need for reliable communication links, especially in beyond-line-of-sight scenarios. This paper investigates the feasibility of predicting future quality of service (QoS) in UAV payload communication links, with a special focus on 5G cellular technology. Through field tests conducted in a suburban environment, we explore challenges and trade-offs that cellular-connected UAVs face, particularly in the context of frequency band selection. We employed machine learning models to forecast uplink (UL) throughput for UAV payload communication, highlighting the significance of diverse training data for accurate predictions. The results reveal the effect of frequency band selection on UAV UL throughput rates at varying altitudes and the influence of integrating diverse feature sets, including radio, network, and spatial features, on ML model performance. These insights provide a foundation for addressing the complexities in UAV communications and enhancing UAV operations in modern networks.

In this letter, we introduce a 194 GHz threestage differential driver amplifier operating at near-fmax frequencies. To address practical considerations associated with implementing on-chip embedding elements within the sub-THz frequency range, we employ a three-element embedded core consisting of three types of embedding networks. This design approach offers greater flexibility in selecting the required embedding elements. Based on the proposed embedded core, a three-stage drive amplifier is implemented in 65 nm CMOS process. The measurement results indicate that the proposed amplifier exhibits a smallsignal gain of 20.4 dB, a Psat of-2.1 dBm, and a peak PAE of 2.2% at 194 GHz.