Recently Network Fault Prediction (NFP) has arisen new scientific interest. The ability to predict network equipment failure is increasingly identified as an effective tool to improve network reliability. This predictive capability can then be used, to mitigate or to enact preventive maintenance on incoming network failures. This could enable the emergence of zero-failure networks and allow safety-critical applications to run over larger and higher complexity heterogeneous networks. In this paper, we present a comprehensive survey on Network Fault Prediction using Machine Learning (ML) methods entirely dedicated to telecommunication networks. Specifically, we first introduce the key concepts of NFP. Second, we present the specific constraints of a Network and System Integrator (NSI), such as heterogeneity, Quality of Service (QoS), maintenance costs, and guaranteed Time To Restore (TTR) according to equipment importance, of the networks. As a consequence, we propose a new metric of ML performance, measuring the expected reduction in maintenance cost based on a ML predictive model. Third, we provide a survey of the ML methods used in NFP studies, and procure recommendations regarding their use in telecommunication networks. Fourth, we propose a summary of the different types of NFP predictions regarding different NFP problems, such as Equipment Health, Network Health, Link Failure, or Alarm Prediction. Finally, we identify the need for zero-loss and zero-delay handover, and the necessity of additional prediction elements as future research directions. In conclusion, we summarize the state of the art, and the new findings of this survey.

Partial discharges (PDs) on liquid impregnated pressboard insulation have been investigated for sinusoidal and switched voltages for two different liquids-one mineral oil and one synthetic ester. The insulation models used were a rounded electrode resting on a pressboard plate on a plane electrode representing semi-uniform field, and a sharp-edged electrode representing divergent field. The main result shows that the PD inception voltage (PDIV) is considerably lower at switched than at sinusoidal voltage. Similarly, the mineral oil showed a lower inception voltage than the synthetic ester. Finally, a lower PDIV for the sharp conical electrode compared to the spherical electrode was seen. The differences can be explained by formation and transport of space charges. The space charges will shield the high-field regions when having the same polarity as the voltage and then enhance the electric field right after a voltage polarity reversal. There is minimal time available for charge drift at polarity reversal of switched voltage. Contrary, for sinusoidal slowly varying voltage there is plenty of time for the charges to drift before the opposite voltage peak occurs. Thus, less field enhancement from space charges under sinusoidal voltage. The field enhancement due to space charges of opposite polarity is enhanced for liquids with higher injection current, such as mineral oil.

The fast and accurate interfacing of digital control platforms with power hardware constitutes a bottleneck in power electronic systems. The guidelines for the effective implementation of this interface are proprietary and not readily available to the designer. This paper presents a systematic approach for developing a mixed-domain control platform for high-frequency power converter applications. The proposed design methodology comprises three crucial steps. In the first step, the platform requirements are identified considering a wide range of switching frequency variations (up to tens of megahertz) found in power electronic applications. In the second step, a system architecture is derived, and its key building blocks and associated features are established based on the application requirements. These building blocks include serial ADCs and DACs, parallel ADCs and DACs for high-speed operation, protection circuits, PWM channels for converter gating, and digital communication features. In the third step, hardware components are selected for realizing different blocks based on a detailed comparison of the salient features of commercially available parts. A prototype of the proposed control platform is fabricated, and the experimental results are presented for validation. The results reinforce the criteria laid down in the design methodology. Further, the proposed control platform is compared with commercially available rapid control prototyping products and is shown to be economically effective for the desired features in typical power converter applications.

In this paper, we present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance and power consumption of our system with measurements. We show that our system achieves acceptable data rates, is very space and energy efficient with an average power consumption of 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work.

Marine radar is widely employed in ocean monitoring systems. However, the presence of sea clutter and noise significantly hampers the detection performance of marine radar. In this paper, we propose an unsupervised-to-supervised sea clutter suppression and denoise framework. The unsupervised stage provides sufficiently high-quality pseudo-labels to the supervised stage, effectively addressing the challenge of obtaining clean labels in current supervised deep learning-based (DL-based) sea clutter suppression methods. The experiments demonstrate that our proposed method exhibits excellent adaptability in both single-target and multi-target scenarios, as well as under various pulse quantities, effectively suppressing sea clutter and noise in low signal-to-clutter ratio (SCR) and signal-to-noise ratio (SNR). In addition, we also verified that the proposed method does not lose clean signals at high SCR and SNR. To validate the widespread applicability of our unsupervised stage, we integrate it into other supervised DL-based clutter suppression methods, and the experiments show a noticeable improvement in the performance of these methods. To further verify the effectiveness of the proposed framework, we conduct validation on downstream applications of radar target detection. Our code will be released after possible acceptance.

The controlled waterway in the upper reaches of the Yangtze River has become a bottleneck for shipping due to its curved, narrow and turbulent characteristics. The vessels passing through it must obey the signal revealed by the Intelligent Vessel Traffic Signaling System (IVTSS) to pass in one direction. The accuracy of signals is directly related to traffic safety and efficiency. However, the unreliability of vessel sensing sensors in these areas and the latency of transmission and computation of large amounts of sensing data may negatively impact IVTSS. Hence, more information from the physical world is needed to ensure the stable operation of the IVTSS, and we proposed an edge computing-centric sensing and execution system based on IoT architecture to enhance the reliability of IVTSS. We conducted experiments using plug-and-play methods, reducing command and recording error rates by 89.47% and 86.27%, respectively, achieving the goal of real-time perception control.

In a world where traditional authentication systems are constrained, the introduction of voice-based authentication provides a viable option. This cutting-edge biometric security method uses unique vocal traits including pitch, tone, and speech patterns to create a unique voiceprint. Its sophisticated and dependable verification procedure, when combined with voice-activated services and secure access systems, not only solves au-thentication issues but also improves user experience overall. Our suggested method provides a thorough blueprint for developing a lightweight voice authentication system that is based on text. This system leverages an effective encoder model to convert voice spectrograms, making deployment easy even on edge devices with limited resources. Various dimensionality reduction techniques are explored to obtain optimal voice embeddings that capture speaker uniqueness while minimizing model complexity. A key novelty is the application of model compression techniques including lightweight architectures and Siamese Networks to obtain highly condensed voice embeddings for each user, reducing storage and infrastructure costs compared to traditional voice authentication methods. The proposed lightweight spectrogram embeddings leverage language-agnostic acoustic features, enabling language-independent speaker verification. Additionally, dimensionality reduction applied during voice registration allows the capturing of discriminative voice characteristics in a low-dimensional compact feature space. This significantly cuts down the storage requirements and infrastructure costs per user compared to standard voice biometric approaches, while retaining competitive verification performance. The architecture is optimized for responsiveness by leveraging lightweight frameworks. The proposed system delivers competitive voice authentication capabilities while minimizing memory, computational, and energy footprints. This makes the system useful for integration into smart devices and paves the way for ubiquitous voice biometrics.

This paper explores the problems caused by spam messages on the internet. It talks about different types of spam, like email spam and fake advertisements on social media. The paper also looks at how spam can make things difficult for people, like draining resources and tricking them into clicking on harmful links. The paper suggests fighting against spam by enabling filters in emails, using third-party spam filtering, avoiding clicking suspicious links, reporting, and blocking and avoiding using personal or professional emails. Lastly, the paper discusses what might happen with spam in the future and the challenges we may face. Overall, the paper aims to help and understand then find ways to deal with it for a safer and smoother experience.

Nearly ideal vertical AlxGa1-xN (\(0.7\ \le x<1.0\)) p-n diodes are fabricated on an AlN substrate. Distributed polarization doping (DPD) was employed for both p-type and n-type layers of the p-n junction, instead of conventional impurity doping, to overcome the major bottleneck of AlN-based material: the control of conductivity. Capacitance-voltage measurements revealed that the net charge concentration agreed well with the DPD charge concentration expected from the device layer structure. The fabricated devices exhibited a low turn-on voltage of 6.5 V, a low differential specific ON-resistance of 3 mΩ cm2, electroluminescence (maximum at 5.1 eV), and an ideality factor of 2 for a wide range of temperatures (room temperature-573 K). Moreover, the breakdown electric field was 7.3 MV cm-1, which was almost twice as high as the reported critical electric field of GaN at the same doping concentration. These results clearly demonstrate the usefulness of DPD in the fabrication of high-performance AlN-based power devices.

Emotions are considered to convey much meaning in communication. Hence, artificial methods for emotion categorization are being developed to meet the increasing demand to introduce intelligent systems, such as robots, into shared workspaces. Deep learning algorithms have demonstrated limited competency in categorizing images from posed datasets with the main features of the face being visible. However, the use of sunglasses and face masks are common in our daily lives, especially with the outbreak of communicable diseases such as the recent coronavirus. Anecdotally, partial coverings of the face reduce the effectiveness of human communication, so would this have hampering effects on computer vision, and if so, would different emotion categories be affected equally? Here, we analyze the performance of emotion classification systems when faces are partially covered with simulated sunglasses and face masks. Deep neural networks consider all pixels in an image as equally important unlike the neuroscientific findings on how humans recognize emotions. Hence, we propose a method that considers different constituent parts (e.g. mouth, eyes, and jaw) separately, giving more attention to relevant (uncovered) regions of the face. The method is compared with three standard, partial coverings-based and attention-based methods. We found that face coverings worsen emotion categorization by up to 74% for the state-of-the-art methods, whereby emotion categories are affected differently by different coverings, e.g. clear mouth coverings have little effect on categorizing happiness, but sadness is affected badly. The proposed method (on average 60.43%) has significantly improved the performance over the standard deep learning (< 46% on average), partial coverings-based (< 47% on average), as well as the attention-based (< 51% on average) methods for both the CK+, KDEF, and RAF-DB datasets when faces were partially covered with sunglasses or different face masks.

Rationale: Whether working together to move a table or supporting a child learning to ride a bike, physically connected individuals use the exchange of haptic information to improve motor performance. However, this improvement occurs at the cost of additional effort for the more skilled partner. Objective: Here, we aim to assess whether an asym-metric connection, consisting of a stiffer link to the less skilled partner, could increase performance without additional effort in collaborative tasks. Methods: Through computational modelling, we first evaluated such a hypothesis on simulated human dyads tracking a common target. The approach was then experimentally validated on a three degree-of-freedom tracking task using two commercial robots as individual interfaces. Results: The simulation and experimental results confirm that using an asymmetric connection stiffness can improve joint performance without requiring additional effort from either partner compared to solo performance. Conclusion: This suggests that the training of motor skills with a proficient partner-like a physical therapist assisting a patient or a violin teacher demonstrating bowing techniques-may be enhanced through the use of robot-mediated asymmetric haptic communication. Index Terms-Human-robot interaction, joint action, human motor control Impact Statement-Using an asymmetric connection in a dyad enables the partners to improve their common performance without additional effort.

We compute the weight distribution of R(4, 9) by combining the approach described in D. V. Sarwate's Ph.D. thesis from 1973 with knowledge on the affine equivalence classification of Boolean functions. To solve this problem posed, e.g., in the MacWilliams and Sloane book [12, p. 447], we apply a refined approach based on the classification of Boolean quartic forms in eight variables due to Ph. Langevin and G. Leander, and recent results on the classification of the quotient space R(4, 7)/R(2, 7) due to V. Gillot and Ph. Langevin.

In developing economies, logistics inefficiencies impede supply chain operations, leading to increased costs and hampering economic growth. This article investigates the pivotal role of mobile technology in optimizing logistics and enhancing Supply Chain Management (SCM), with a specific focus on Uganda. It delves into the landscape of mobile technology utilization, addressing the benefits and associated risks within logistics operations, encompassing storage, transportation, and management. The evolving concept of SCM is discussed, emphasizing its strategic significance in improving efficiency and customer satisfaction. The article systematically examines the advantages of integrating mobile technology into logistics operations, emphasizing improved collaboration, enhanced supply chain performance, resilience to disruptions, and advancements in service quality and global competitiveness. Practical insights from Uganda illustrate the application of mobile technology in the local context. However, integration introduces risks, including cybersecurity vulnerabilities, technological dependencies, and integration challenges. The paper recommends proactive measures, such as robust security protocols and contingency plans, to mitigate risks and ensure effective integration. This article targets policymakers, industry professionals, and researchers. It provides valuable insights into strategically employing mobile technology for enhanced logistics operations and SCM in developing economies, emphasizing its transformative potential in fostering efficient, resilient, and competitive supply chain ecosystems.

Molecular communication is a communication paradigm inspired by biological systems, where chemical signals are used to encode and transmit information. MoSK (Molecule Shift Keying) is proposed as a modulation technique that utilizes different types of signaling molecules to encode digital information. MoSK mimics natural molecular communication by leveraging the diversity of signaling molecules and their specific roles. Experimental testbeds have been developed using mi-crofluidic systems, droplet-based platforms, and biological cells to validate MoSK. A prototype platform for MoSK implementation is presented, including a transmitter with infusion and selection valves, and a fluorescence-based receiver. The receiver detects and decodes fluorescence signals emitted by Graphene Quantum Dots (GQDs), which are water-soluble and fluorescent molecular messengers. The fluorescence signals of blue-GQDs and cyan-GQDs are acquired by the receiver, and the performance of the system is evaluated in terms of synchronization, detection threshold, and symbol recognition using Principal Component Analysis (PCA). The results demonstrate the successful detection and recognition of different symbols, even at lower concentrations. PCA proves to be an efficient method for qualitative recognition of molecular messengers in MoSK-based molecular communication systems.

One important approach to improve the performance of the convolutional accelerator is to reduce its memory access latency. When the DDR memory connected to the accelerator is fixed, the memory access latency of the convolutional accelerator can be decreased by reducing the number of memory accesses or memory row conflicts, and using DMA to optimize memory access time intervals and so on. The convolutional process involves accessing a large amount of data with different sizes and types, which are usually stored in different memory rows. Accessing such data always causes a significant number of memory row conflicts, resulting in high memory access latency of the convolutional accelerator. This paper firstly analyzes the composition of DDR access latency for the convolutional accelerator and finds that the number of memory row conflicts is mainly decided by the number of loop tiling columns and padding size. Then, an OptTc method is proposed to calculate the optimal number of loop tiling columns. FPGA experiments of proposed convolution accelerator show that using the optimal number of loop tiling columns can reduce the memory access latency by 24% with negligible increase in FPGA resources in the case of VGG-16. The proposed OptTc method can effectively assist in the design of convolutional accelerators.

Unmanned Aerial Vehicle (UAV) detection in real-time is an emerging field of study that focuses on computer vision and deep learning algorithms. However, the increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. The purpose of this research is to detect UAVs, under adverse weather conditions (such as rain) and image distortions (such as motion blur and noise). The goal is to examine how these adverse conditions affect UAV detection performance and to provide techniques to increase model robustness. To achieve this, a custom training dataset was constructed by combining multiple existing datasets, supplementing them with complex backgrounds. In addition, a custom testing dataset was generated containing UAV images affected by adverse conditions.  On the proposed dataset, the performance of well-known object detection algorithms including YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS was investigated. In comparison to clean images, the results demonstrated a considerable performance decrease under adverse conditions. However, training the models on the augmented dataset containing samples of distorted and weather-affected images significantly enhanced the models' performance under challenging settings. These findings highlight the importance of taking adverse weather conditions into account during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure reliable UAV detection under extreme weather conditions and image distortions. (Note: This is a pre-print of a paper submitted to IEEE for potential journal publication and final version may vary upon acceptance and publication)

A document by Stan Zurek. Click on the document to view its contents.

The performance of machine learning models is closely linked to the quality of training data, underpinning the 'garbage in, garbage out' principle. Label noise in datasets is a key challenge in training and evaluation. This study introduces two innovative ChatGPT-based methods, ChatGPT-Predict and ChatGPT-Detect, for effective noise detection in labeled datasets. We assess the efficacy of these methods against conventional vote-based techniques, focusing on factors like noise characteristics , dataset complexity, and the impact of prompt-engineering. Comprehensive evaluations using both artificial and real-world datasets demonstrate the adaptability of our methods to different noise types and levels. Key findings emphasize the critical role of prompt design in language model performance and the distinct contrasts in handling artificial versus real-world noise. The research acknowledges potential limitations due to prompt design variability and suggests possible enhancements with more advanced models like GPT-4. Future research avenues include applying these methods with GPT-4, exploring diverse prompt templates, and extending the methodology to real-world datasets with high noise levels. This study contributes to the field by refining noise detection methodologies, thereby enhancing the robustness and reliability of machine learning models.