Secure Location-Aided Routing Protocols With Wi-Fi Direct For Vehicular Ad Hoc Networks

Secure routing protocols are proposed for the vehicular ad hoc networks. The protocolsintegrate the security authentication process with the Location-Aided Routing (LAR) protocol to supportWi-Fi Direct communications between the vehicles. The methods are robust against various security threats.The security authentication process adopts a modified Diffie-Hellman key agreement protocol. The Diffie-Hellman protocol is used with a short authentication string (SAS)-based key agreement over Wi-Fi Directout-of-band communication channels. It protects the communication from any man-in-the-middle securitythreats. In particular, the security process is integrated into two LAR routing schemes, i.e., the request-zoneLAR scheme and the distance-based LAR scheme.We conduct extensive simulations with different networkparameters such as the vehicular node density, the number of the malicious nodes, and the speed of thenodes. Simulation results show that the proposed routing protocols provide superior performance in securedata delivery and average total packet delay. Also, the secure distance-based LAR protocol outperforms thesecure request-zone LAR protocol

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PD

Fuzzy Inference System for Speed Bumps Detection Using Smart Phone Accelerometer Sensor

Recently, a significant amount of research attention has been given to monitoring the road surface anomalies such as potholes and speed bumps. In this paper, speed bump detection method based on a fuzzy inference system (FIS) is proposed. The fuzzy inference system detects and recognizes the speed bumps from the variance of the vertical acceleration and the speed of the vehicle. The proposed method utilizes the embedded sensor (accelerometer) in the Smartphone. The proposed method is tested and evaluated under different speed levels. The results show that the proposed method is promising for bumps detection

A Real-Time Olive Fruit Detection for Harvesting Robot Based on Yolo Algorithms

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Evaluation of Using NIR Simplified Spectroscopy in Yogurt Fermentation Automation

In the fermentation process sugars are transformed into lactic acid. pH meters have traditionally been used for fermentation process monitoring based on acidity. More recently, near infrared (NIR) spectroscopy has proven to provide an accurate and non-invasive method to detect when the transformation of sugars into lactic acid is finished. The fermentation process when sugars are transformed into lactic acid. This research proposes the use of simplified NIR spectroscopy using multispectral optical sensors as a simpler and less expensive measure to end the fermentation process. The NIR spectrum of milk and yogurt is compared to find and extract features that can be used to design a simple sensor to monitor the yogurt fermentation process. Multispectral images in four selected wavebands within the NIR spectrum are captured and show different spectral remission characteristics for milk, yogurt and water, which support the selection of these wavebands for milk and yogurt classification

Cooperative Detection of Moving Targets in Wireless Sensor Network Based on Fuzzy Dynamic Weighted Majority Voting Decision Fusion

Abstract-Multisensor data fusion has many military and civilian applications due to its statistical advantages. In this work, we propose a heuristic to enhance cooperative detection of moving targets within a region that is monitored by a wireless sensor network. This heuristic is based on fuzzy dynamic weighted majority voting for decision fusion. It fuses all the local decisions of the neighboring sensor nodes and determines the number and types of moving targets. A fuzzy logic weights each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized. In addition, a finite state machine is proposed to reduce the detection false alarm and to estimate the best time at which the cluster decisions should be reported to the sink or gateway. Simulation results show that there is an optimal sensor number for distributed detection of a random process. This work is compared with the normal majority voting algorithm for hard decision fusion. It shows that the fuzzy weighted majority voting for decision fusion has less detection error than the normal majority voting

Cyclic Behaviour of Heat-Damaged Beam−Column Joints Modified with Nano-Silica, Nano-Titanium, and Nano-Alumina

This research is designed to check the potential of modifying concrete with nanomaterials to enhance the cyclic behavior of beam−column joints. It also studies the effect of heat on the cyclic behavior of beam−column joints modified with nanomaterials. Experimental and numerical programs are carried out to explore the cyclic behavior of the heat-damaged and unheated RC joints modified with nanomaterials. Six half-scale exterior RC beam-to-column joints were prepared; two control specimens, two specimens were modified with nano-silica and nano-alumina, and two specimens were modified with nano-silica and nano-titanium. The cement was replaced by 1.33% nano-alumina and 0.67% nano-silica (by cement weight), and the other concrete mix was modified with 1.33% nano-silica and 0.67% nano-titanium, where the cement was replaced by a total of 2% nano-alumina and nano-silica in two specimens, and a total of 2% nano-silica and nano-titanium in the other two specimens. One specimen from each concrete mix was subjected to a temperature of 720 °C for 2 h. The joint specimens were subjected to lateral cyclic loading on the beam and axial loading on the column. Test results showed that the replacement of cement with 2% nano-alumina and nano-silica or 2% nano-silica and nano-titanium is recommended to enhance RC joints’ behavior. The nanomaterials changed the mode of failure of the joint specimens from brittle joint shear failure to a combined type of failure involving the ductile beam hinge and joint shear