Deep brain stimulation is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fibre activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signalling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.

A multiple scattering model for passive radiative transfer (RT) in vegetation that accounts for the vertical profile of the plant structure is developed, offering advancements over the commonly-used single-layer uniform scattering models prevalent in the vegetated land surface microwave remote sensing. The proposed model takes into account the complexities of the canopy morphology with vertical heterogeneity, enabling the representation of overlapping vegetation species applicable to diverse plant types and growth stages. Additionally, it serves as a valuable tool for understanding the influence of the vegetation vertical structure on the microwave brightness temperatures. The model is constructed based on high-order solutions to the RT equations, obtained through a numerical iterative approach with an efficient interpolation scheme for algorithm acceleration. This methodology facilitates the accurate distinction of the contributions to the brightness temperature from each scattering order and scattering mechanism, ensuring a comprehensive consideration of multiple scattering effects within various vegetated scenarios. The model is validated using the SMAPVEX12 L-band forest data set, encompassing a wide range of soil moisture variations. Comparisons are made between the brightness temperatures simulated by the newly developed multiple-scattering model with a continuous profile or layered profile and those obtained from a uniform single-layer model. Results demonstrate significant improvements in the multi-layered or the continuously profiled model, showing improved agreement with the measured brightness temperatures. Furthermore, the proposed model is parameterized by matching the high-order solutions to the RT equation to the widely adopted reduced order albedo-tau formalism. The resulting equivalent parameters are linked to the geometries and the electromagnetic properties of the vegetation layer, while also incorporating the effects of multiple scattering. Comparative analysis of the equivalent parameters derived from the layered model and those derived from the single-layer model reveals that the vertical heterogeneity of the vegetation structure has a notable influence on the effective scattering albedo and it yields a value more consistent with the albedo as chosen in the SMAP/SMOS inversion algorithms. Meanwhile, the impact of the vegetation vertical profile on the effective optical thickness and the effective transmissivity of the vegetation layer is weak.These insights are essential for the retrieval of soil moisture and vegetation characteristics including the plant vertical structures in microwave remote sensing.

We propose an integrated behavior and motion planning framework for the automated lane-merging problem. The behavior planner combines search-based planning with game theory to model the interaction between vehicles and select multivehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process. To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions. In practice, however, the surrounding vehicles might deviate from the computed equilibrium trajectories. Thus, we introduce a branch model predictive control (BMPC) framework to account for the uncertain behavior modes of the surrounding vehicles. A tailored numerical solver is developed to enhance computational efficiency by leveraging the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios.

This letter, based on measuring the admittance of a black-boxed grid-following inverter (GFLI) at seven distinct operating points to synthesize an operating-point-parameterized state-space model (OPP SSM), presents an efficient method for analytical prediction of the maximum transferable active power (MTAP) injected by the black-boxed GFLI into the grid under any arbitrary grid condition and reactive power injection level. The proposed method obviates the need for repetitive electromagnetic transient time-domain simulations or frequency-domain impedance criteria by gradually increasing active power output until the MTAP is reached. The feasibility of the synthesized OPP SSM for MTAP prediction has been experimentally validated on a power-controlled GFLI, demonstrating its potential for aiding the integration of inverter-based resources in modern power systems.

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.

A document by Christoph Funda . Click on the document to view its contents.

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.