In analyzing aggregated data, a Pearson correlation coefficient of 0.88 was obtained. For 1000-meter road sections, the coefficients were 0.32 on highways and 0.39 on urban roads. Incrementing IRI by 1 meter per kilometer precipitated a 34% expansion in normalized energy consumption. The normalized energy data provides insight into the characteristics of the road's surface texture, as the results indicate. In light of the growing use of connected vehicle technologies, this method demonstrates promising potential for large-scale road energy efficiency monitoring in future applications.

The domain name system (DNS) protocol underpins the internet's operation, yet recent years have seen the advancement of various techniques for organizations to be subjected to DNS-based attacks. During the last few years, the increased use of cloud solutions by companies has created more security difficulties, as cyber criminals employ various strategies to take advantage of cloud services, their configurations, and the DNS protocol. Two DNS tunneling methods, Iodine and DNScat, were used to conduct experiments in cloud environments (Google and AWS), leading to positive exfiltration results under varied firewall configurations as detailed in this paper. Identifying malicious DNS protocol activity poses a significant hurdle for organizations lacking robust cybersecurity resources and expertise. This study leverages diverse DNS tunneling detection methods within a cloud framework to construct a monitoring system boasting high reliability, minimal implementation costs, and user-friendliness, particularly for organizations with restricted detection capabilities. To configure a DNS monitoring system and analyze the collected DNS logs, the open-source framework, Elastic stack, was employed. Moreover, a variety of traffic and payload analysis techniques were employed to find different kinds of tunneling methods. The cloud-based monitoring system's array of detection techniques can monitor the DNS activities of any network, making it especially suitable for small organizations. Additionally, unrestricted data uploads are permitted daily by the open-source Elastic stack.

The research presented in this paper leverages deep learning techniques to perform early sensor fusion of mmWave radar and RGB camera data for object detection, tracking, and embedded system deployment in ADAS. Not only can the proposed system be utilized within ADAS systems, but it also holds potential for implementation within smart Road Side Units (RSUs) of transportation networks to monitor real-time traffic conditions and proactively warn road users of imminent dangers. CK1-IN-2 MmWave radar signals exhibit impressive resilience to unfavorable weather conditions like cloudy, sunny, snowy, night-light, and rainy days, maintaining effective operation in both normal and harsh conditions. Employing an RGB camera for object detection and tracking presents limitations; these are overcome by the early combination of mmWave radar and RGB camera data, which effectively compensates for poor performance in unfavorable weather or lighting. The proposed methodology leverages radar and RGB camera data, and outputs the results directly via an end-to-end trained deep neural network. Furthermore, the overall system's intricacy is diminished, enabling the proposed methodology to be implemented on both personal computers and embedded systems such as NVIDIA Jetson Xavier, achieving a frame rate of 1739 frames per second.

Given the considerable increase in life expectancy witnessed over the last hundred years, society is confronted with the challenge of inventing inventive approaches for supporting active aging and elder care. Leveraging cutting-edge virtual coaching methods, the e-VITA project is supported financially by both the European Union and Japan, focusing on the key aspects of active and healthy aging. In a process of participatory design, comprising workshops, focus groups, and living laboratories spanning Germany, France, Italy, and Japan, the requirements for the virtual coach were meticulously established. Several use cases were then selected, and development was executed using the open-source Rasa framework. By utilizing Knowledge Graphs and Knowledge Bases as common representations, the system facilitates the integration of context, subject matter expertise, and multimodal data. The system is available in English, German, French, Italian, and Japanese.

The configuration of a first-order universal filter, electronically tunable in mixed-mode, is explored in this article. This design utilizes just one voltage differencing gain amplifier (VDGA), one capacitor, and one grounded resistor. Correct input selection within the proposed circuit allows for the accomplishment of all three fundamental first-order filter functions, low-pass (LP), high-pass (HP), and all-pass (AP) across the four operational modes, encompassing voltage mode (VM), trans-admittance mode (TAM), current mode (CM), and trans-impedance mode (TIM), all through a singular circuit configuration. Electronic tuning of the pole frequency and passband gain is accomplished through variable transconductance values. The proposed circuit's non-ideal and parasitic effects were also examined in detail. Experimental findings, in conjunction with PSPICE simulations, have corroborated the design's performance. The proposed configuration's success in practical situations is supported by considerable simulation and experimental evidence.

The immense appeal of technology-driven approaches and advancements in addressing routine processes has greatly fostered the rise of smart cities. Millions of interconnected devices and sensors work together to generate and disseminate substantial volumes of data. The abundance of easily accessible personal and public data within these digitized, automated urban environments leaves smart cities susceptible to internal and external security threats. With the rapid evolution of technology, the conventional method of using usernames and passwords is no longer a reliable safeguard against the ever-increasing sophistication of cyberattacks targeting valuable data and information. Single-factor authentication systems, both online and offline, present security challenges that multi-factor authentication (MFA) can successfully resolve. The smart city's security architecture requires multi-factor authentication (MFA), and this paper explores its implementation and importance. Regarding smart cities, the paper's introduction explores the associated security threats and the privacy issues they raise. A detailed explanation of MFA's role in securing smart city entities and services is presented in the paper. CK1-IN-2 Within the paper, a novel multi-factor authentication system, BAuth-ZKP, built upon blockchain technology, is proposed to secure smart city transactions. Zero-knowledge proofs underpin the secure and private transactions between smart city entities facilitated by smart contracts. To conclude, the prospective advancements, progressions, and reach of using MFA within the intelligent urban environment are evaluated.

The capability of inertial measurement units (IMUs) in remote patient monitoring enables an accurate determination of the presence and severity of knee osteoarthritis (OA). The Fourier representation of IMU signals served as the tool employed in this study to differentiate between individuals with and without knee osteoarthritis. A study population of 27 patients with unilateral knee osteoarthritis (15 female) was joined by 18 healthy controls (11 female). Overground walking gait acceleration signals were captured during the activity. Employing the Fourier transform, we extracted the frequency characteristics from the signals. In order to discern acceleration data from those with and without knee osteoarthritis, a logistic LASSO regression analysis was conducted on frequency domain features, along with participant age, sex, and BMI. CK1-IN-2 The model's accuracy was quantitatively estimated by implementing a 10-fold cross-validation approach. Between the two groups, the signals presented different frequency components. In terms of average accuracy, the classification model, utilizing frequency features, performed at 0.91001. Analysis of the final model revealed a contrast in the distribution of the selected features across patient groups with different levels of knee osteoarthritis (OA) severity. In our analysis of acceleration signals, Fourier transformed and subject to logistic LASSO regression, we found an accurate method to determine knee osteoarthritis.

Human action recognition (HAR) is a very active research area and a significant part of the computer vision field. Even with the substantial body of work on this topic, HAR (Human Activity Recognition) algorithms like 3D convolutional neural networks (CNNs), two-stream networks, and CNN-LSTM architectures tend to have complex configurations. A significant number of weight adjustments are inherent in the training of these algorithms, ultimately requiring powerful hardware configurations for real-time HAR implementations. Consequently, this paper introduces a novel frame-scraping technique, leveraging 2D skeleton features and a Fine-KNN classifier, to address dimensionality issues in human activity recognition systems. Using OpenPose, we attained the 2D positional information. Empirical evidence confirms the potential applicability of our technique. By incorporating an extraneous frame scraping technique, the OpenPose-FineKNN method obtained accuracies of 89.75% on the MCAD dataset and 90.97% on the IXMAS dataset, surpassing the performance of existing techniques.

Sensor-based technologies, such as cameras, LiDAR, and radar, are integral components in the implementation of autonomous driving, encompassing recognition, judgment, and control. Although recognition sensors are exposed to the external environment, their operational efficiency can be hampered by interfering substances, such as dust, bird droppings, and insects, affecting their visual performance during their operation. The existing research addressing performance deterioration through sensor cleaning procedures is narrow in its focus.