With the rapid advancement of technology, an increasing number of devices are being connected to the Internet and getting smart. Such advancement brings new challenges in the field of intelligent ...transportation system (ITS), including transferring of high data rates, providing rapid response system to users, addition of new devices, and their remote configuration. Thus, mobile information systems, along with intelligent multimodal mobility services, cope with such constraints and take significant benefit of the associated technology from emerging information and communication frameworks. Therefore, recent advancement in the field of telecommunication has witnessed increased interest in ITS, especially vehicular ad-hoc networks (VANETS). Software-defined networks (SDNs) can also bring advantages to ITS due their flexibility and programmability to the network via their logical and centralized control entity. However, the bandwidth and continuous connection between ITS and SDN is still a challenge owing to the highly mobile nature of VANETS. Therefore, to address this issue, this paper presents a novel concept for enhancing the capabilities of ITS via the newly proposed 5G-based SDN architecture for ITS. The proposed system architecture is based on the following three function layers: sensing layer, relay layer, and core network layer. Continuous accessibility, via flexible and programmable features, is achieved through SDN features. In addition, high data rates and bandwidth are provided by the proposed 5G architecture. The simulation results show that the proposed system architecture achieves better results than the ad-hoc on-demand distance vector routing protocol.
The Internet of Things (IoT) has particular applications in public safety as well as other domains such as smart cities, health monitoring, smart homes and environments, smart industry, and various ...types of pervasive systems. The attacker can simply attack the IoT device in such applications, because it is randomly distributed, dynamic topology and not reliable due to energy and communication limitation. Moreover, the threat to confidentiality and security is increasing as the number of devices connected in IoT is increasing. As the numbers of devices connected to the Internet is expanding, the threat to confidentiality and security is increasing. The aim of this paper is design a typical network security model for cooperative virtual networks in the IoT era. This paper presents and discusses network security vulnerabilities, threats, attacks and risks in switches, firewalls and routers, in addition to a policy to mitigate those risks. The paper provides the fundamentals of secure networking system including firewall, router, AAA server and VLAN technology. It presents a novel security model to defense the network from internal and external attacks and threats in the IoT Era. A testbed is built to investigate the proposed model, and the performed assessment show an effective security performance with a good network performance.
Monogenic forms of obesity have been identified in ≤10% of severely obese European patients. However, the overall spectrum of deleterious variants (point mutations and structural variants) ...responsible for childhood severe obesity remains elusive. In this study, we genetically screened 225 severely obese children from consanguineous Pakistani families through a combination of techniques, including an in-house-developed augmented whole-exome sequencing method (CoDE-seq) that enables simultaneous detection of whole-exome copy number variations (CNVs) and point mutations in coding regions. We identified 110 (49%) probands carrying 55 different pathogenic point mutations and CNVs in 13 genes/loci responsible for nonsyndromic and syndromic monofactorial obesity. CoDE-seq also identified 28 rare or novel CNVs associated with intellectual disability in 22 additional obese subjects (10%). Additionally, we highlight variants in candidate genes for obesity warranting further investigation. Altogether, 59% of cases in the studied cohort are likely to have a discrete genetic cause, with 13% of these as a result of CNVs, demonstrating a remarkably higher prevalence of monofactorial obesity than hitherto reported and a plausible overlapping of obesity and intellectual disabilities in several cases. Finally, inbred populations with a high prevalence of obesity provide unique, genetically enriched material in the quest of new genes/variants influencing energy balance.
Smartphone Sensors for Indoor Positioning Ashraf, Imran; Park, Yongwan; Zikria, Yousaf Bin ...
Sensors (Basel, Switzerland),
04/2023, Letnik:
23, Številka:
8
Journal Article
Recenzirano
Odprti dostop
The explosive growth and wide proliferation of mobile devices, the majority of which are smartphones, led to the inception of several novel and intuitive services, including on-the-go services, ...online customer services, and location-based services (LBS) ....
Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing ...various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.
Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent ...surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.
Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed ...internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure. Magnetic field positioning is an attractive solution for indoors; yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. The concept of DUST (device, user, space, time) and DOWTS (dynamicity, orientation, walk, trajectory, and sensor fusion) is introduced which divides the characteristics of the magnetic field dataset into basic and advanced groups and discusses the publicly available datasets accordingly.
With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and ...interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate.
Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help ...predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.
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