USAGE OF IOT EDGE APPROACH FOR ROAD QUALITY ANALYSIS

This paper applies a modified cascaded fuzzy reasoning Petri net (CFRPN) model to analyze dangerous driving events on a freeway. The dangerous driving events can be divided into two groups: (1) the interaction between a driver's vehicle and the road environment, and (2) the interaction between a driver's vehicle and nearby vehicles. These two classes of driving events may occur simultaneously and lead to certain serious traffic situations. The proposed system analyzes these two kinds of events and determines dangerous situations from data collected by various sensors. Since collecting real driving event data on freeway is dangerous and time consuming, a data generation system is developed to generate the experimental data. Such data can help evaluate the performance of the proposed analysis system. Finally, experimental results show that the proposed system is accurate and robust

This article deals with the problem of room categorization, i.e. the classification of a room as being a bathroom, kitchen, living-room, bedroom, etc., by an autonomous robot operating in home environments. For that, we propose a room categorization system based on a Bayesian probabilistic framework that combines object detections and its semantics. For detecting objects we resort to a state-of-the-art CNN, Mask R-CNN, while the meaning or semantics of those detections is provided by an ontology. Such an ontology encodes the relations between object and room categories, that is, in which room types the different object categories are typically found (toilets in bathrooms, microwaves in kitchens, etc.). The Bayesian framework is in charge of fusing both sources of information and providing a probability distribution over the set of categories the room can belong to. The proposed system has been evaluated in houses from the Robot@Home dataset, validating its effectiveness under real-world conditions.

In the field of sports, including kayaking, developing technical skills and optimizing performance are essential. Given the constraints on equipment design (paddles, footrests, seats, boats), the use of portable on-water measurement systems is crucial for elite kayakers and coaches to study equipment usage and athletes movements in real-world conditions. Among the technologies employed, force sensors and inertial measurement units (IMUs) are commonly used for on the water dynamic and kinematic analysis respectively. However, finding a portable system for kayaking that allows comprehensive evaluation while being suitable for on-water analysis has proven extremely challenging. Existing systems primarily focus on paddle data collection and analysis, with limited attention to data transmission and the energy efficiency of wireless sensor nodes. Moreover, the body of literature in this area remains relatively fragmented and limited, with contributions dispersed across disciplines including biomechanics, embedded systems, and sports science. This study reviews essential systems for real-time, comprehensive analysis of professional kayakers performance. It examines both commercial systems and academic research, focusing on dynamic and kinematic measurements, with an emphasis on inertial and force sensors, particularly strain gauges, which play a central role in this emerging trend in kayaking. These technologies enable detailed analysis, paving the way for future developments to enhance kayakers training programs significantly.

The increasing complexity of cyber threats requires the development of effective methods for detecting and classifying attacks in network traffic. This study analyzes the effectiveness of three popular machine learning algorithms: Random Forest, which is used for anomaly detection, Support Vector Machines (SVM), which performs cyber threat classification, and autoencoders, which are used for data preprocessing and deep traffic analysis. Considering each method's advantages, a combined model is proposed that combines the capabilities of these algorithms, increasing the efficiency of threat detection and optimizing the attack classification process. The experiments demonstrate that the proposed approach increases attack detection accuracy by 3–7% and reduces response time compared to using individual machine learning algorithms. In addition, the combined model contributes to resource optimization, which is a critical factor for deploying scalable solutions in real-world conditions. Special attention is paid to the hybrid model that combines autoencoders and the Random Forest algorithm. Its effectiveness has been confirmed in test environments, where better results in detecting network anomalies were demonstrated compared to the use of only one algorithm. The proposed approach allows not only to increase the level of protection of information systems, but also to provide flexibility in configuration, which makes the model suitable for use in a wide range of cybersecurity tasks. The results obtained can be used to improve existing cyber protection methods, in particular in the field of critical infrastructure protection, where timely detection of threats is a key security factor. The high efficiency of the combined approach confirms its feasibility for implementation in network traffic monitoring systems and cybersecurity in general.

This article reports the outcomes of the Solar Pilar Project, whose main objective was to provide low-cost, high-quality electricity to residents of a community in the metropolitan region of Rio de Janeiro. The technological solutions, tested under real-world conditions in an “open-air laboratory,” proved highly effective in delivering electricity to low-income communities by combining quality, affordability, and the potential to reduce non-technical losses (NTL). The implementation of a hybrid generation system (solar photovoltaic with batteries) in units of Study Group A yielded average reductions of 70% in electricity bills, whereas credit compensation through a photovoltaic power plant for Study Group B resulted in reductions of approximately 50%. These benefits exceeded the discounts offered under the Social Electricity Tariff. Importantly, despite the direct revenue losses initially faced by the utility company, the project demonstrated that substantial financial recovery is possible by lowering NTL to regulatory levels. Moreover, simulations indicated improvements in voltage levels across low- and medium-voltage networks, thereby contributing to enhanced supply quality. Finally, the introduction of a symbolic fee charged to beneficiaries was identified as a potential mechanism to ensure the economic viability and scalability of the model.

BackgroundLiver cirrhosis is a leading cause of morbidity, premature mortality and acute care utilization in patients with digestive disease. In the province of Alberta, hospital readmission rates for patients with cirrhosis are estimated at 44% at 90 days. For hospitalized patients, multiple care gaps exist, the most notable stemming from i) the lack of a structured approach to best practice care for cirrhosis complications, ii) the lack of a structured approach to broader health needs and iii) suboptimal preparation for transition of care into the community. Cirrhosis Care Alberta (CCAB) is a 4-year multi-component pragmatic trial which aims to address these gaps. The proposed intervention is initiated at the time of hospitalization through implementation of a clinical information system embedded electronic order set for delivering evidence-based best practices under real-world conditions. The overarching objective of the CCAB trial is to demonstrate effectiveness and implementation feasibility for use of the order set in routine patient care within eight hospital sites in Alberta.MethodsA mixed methods hybrid type I effectiveness-implementation design will be used to evaluate the effectiveness of the order set intervention. The primary outcome is a reduction in 90-day cumulative length of stay. Implementation outcomes such as reach, adoption, fidelity and maintenance will also be evaluated alongside other patient and service outcomes such as readmission rates, quality of care and cost-effectiveness. This theory-based trial will be guided by Normalization Process Theory, Consolidated Framework on Implementation Research (CFIR) and the Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM) Framework.DiscussionThe CCAB project is unique in its breadth, both in the comprehensiveness of the multi-component order set and also for the breadth of its roll-out. Lessons learned will ultimately inform the feasibility and effectiveness of this approach in “real-world” conditions as well as adoption and adaptation of these best practices within the rest of Alberta, other provinces in Canada, and beyond.Trial registrationClinicalTrials.gov: NCT04149223, November 4, 2019.

Recently, during natural disasters like earthquakes, typhoons, flood, and heavy snowfall, messages and photos describing the situations faces by people are actively posted on social media sites. Therefore, the development of an analysis system using the data on social media sites to enhance situation awareness in the real world is an important research topic. In our previous work, we developed a density-based spatiotemporal analysis system to enhance situation awareness during emergency situations. The system could identify areas related to an observed emergency topic using tweet classifier, spatiotemporal clustering, and photo image classifier using the Bag-of-Features (BoF) model. In this paper, we propose a novel density-based spatiotemporal analysis system with a photo image classifier that used a pretrained deep network. The pre-trained deep network is integrated into the conventional photo image classifier instead of the BoF model. The proposed system can enhance situation awareness compared to our previous system by accurately classifying the photo images related to an observed emergency topic. To evaluate the proposed system, we used actual photo images attached to tweets related to “heavy rain” in Japan. The experimental results showed that the proposed system can classify photo images related to “heavy rain” more sensitively than our previous system.

We introduce a novel platform for medical device training: hybrid physical-virtual simulators of medical devices, combining touchscreen-enabled virtual emulations of real devices with sensorized physical peripherals to enable tactile, hands-on interaction between the trainee, simulated device and standardized patients or mannequins. The system enables objective measurement and recording of trainee performance, including interactions with both the virtual device elements and the physical components, and can include metrics and feedback not available in the real device. The system also includes an integrated wireless signaling device for use with standardized patients. We present the implementation of an example system, a virtual defibrillator with sensorized paddles and wireless signaling of successful defibrillator operation.

For overhead power grids, unexpected serious line sagging of extra-high voltage transmission lines would easily lead to major blackouts. Different direct and indirect sag measuring methods have been proposed, but they all have their own limitations. In this study, an Internet of Things (IoT)-based sag-monitoring system is proposed, which is able to perform real-time detection of line sags. The whole system has been deployed on two 161-kV lines for long-term field testing. The effectiveness of the proposed sag-monitoring system is verified through both theoretical calculation and field measurements. In this sag-monitoring system, a sag-sensing module is equipped with an embedded triaxial accelerometer to detect line sags at different spans of a single circuit. A catenary equation that takes temperature dependency into consideration is derived, so the measured accelerometer parameters can be converted to accurate line sag values. The long-term testing results show that the proposed sag-monitoring system yields an average error of 2.09%. The average differences between the sag values coming from the proposed system and a commercial sag measuring device are relatively small (between 0.57% and 4.14%), which proves that the sag values provided by the proposed system are reliable and accurate in the long-term testing. In addition, compared to the other existing sag measuring methods, the advantages of employing the proposed system are high measurement accuracy, and enabling wide field implementation, online monitoring, long-term field operation, and real-time transmission.

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.

<p>Attempting to reduce the existing electricity consumption bill, as well as the stability and non-outage grid recharge with the lowest possible cost and suitable quality, is one of the most important goals for those interested in energy around the world. This paper study the circumstances surrounding the Egyptian society to find the best solutions to a achieved this goals, and it was found that solar energy is one of the best alternatives available for energy. Firstly will be study the electricity consumption bill, slice prices and a program was made to calculate the consumption invoice moreover another program for quick estimation to the proposed solar system.The proposed system provides a smart integration between the solar system and grid, where the supply sustainability and the optimal cost are considered. This configuration allows the two sources separately or simultaneously supply the loads depending on photovoltaic extracted energy. Operational analysis of the proposed system will discussed in this paper. The proposed system consists of solar cells, charge controller, batteries and inverter plugged to automatic transfer switches (ATS) using Programmable Logic Control (PLC). The system grantee a safe and reliable load feeding independently on the grid status. The system durability is the most depicted feature through the modelling and experimentally results. A typical case studies for about four years of non-outages photovoltaic-grid hybrid supply (the implemented system) will be presented and discussed.</p>

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometrie attendance systems are unimodal. Unimodal biometrie systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students' attendance. The students' faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%.

This study presents a new framework for human identity recognition using heart sound signals. The proposed framework is based on extracting cepstral features from heart sound signals, which are known as phono-cardio-gram (PCG). Two well-known cepstral features have been adopted in most of the previously implemented PCG biometric authentication systems; namely, mel-frequency and linear frequency cepstral features. In this study, two more cepstral features are proposed based on modifying the mel-frequency cepstral features. The first one is based on modifying the mel-frequency equation to increase the non-linearity of the triangular filters in the frequency range of the PCG signal. The other is based on replacing mel-scaled triangular filters with wavelet packet filters where a non-linear filter bank structure is designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. The proposed system uses wavelet de-noising for pre-processing and linear discriminant analysis for classification. The proposed system is evaluated on two databases; one consists of 21 users (BioSec. database) and the other consists of 206 users (HSCT-11 database). Moreover, the proposed system is compared with previous systems that used the same databases. On the basis of the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.

The aim of the work is to create an algorithm for automatic graphological analysis of employees’ handwriting. The basis of the algorithm is parallel processing of the employees’ handwritten text. Implementing the presented algorithm will allow creating a modular automated system of graphological analysis. The article presents the architecture of an automated system of graphological analysis. The novelty of the work is the proposed creative concept of using automatic graphological analysis for timely detection of attempts of unfavourable external psychological influence on employees. The result of the study is recommendations for using the algorithm for graphological analysis of employees’ handwriting in automated information security systems.

A document analysis system which is capable of extracting the semantics of specific text portions of structured documents is presented. The architecture of the analysis system is based on a knowledge representation scheme, a semantic network, called Resco-Frame Representation of Structured Documents. It allows the definition of knowledge about document components as well as knowledge about analysis algorithms in a uniform, simple, but powerful representation formalism. Hence, this architecture enables the analysis system to exploit the specific power of both the algorithmic knowledge describing the properties of algorithms and the declarative knowledge about properties of text objects in documents. The inference engine and the control algorithm show how these two knowledge sources are combined and utilized. The flexibility of the representation formalism Fresco, the recognition results and the computational complexity of the inference algorithm are presented in two different applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>