Collision Detection and the Design of Fair and Stable MAC Scheme for Wireless Ad Hoc Networks

Fairness and stability guarantee among TCP flows is very stubborn in wireless ad hoc networks. There is not a MAC protocol that can fulfill this acquirement until now. In this paper, we firstly reveal the in-depth causes of the severe TCP unfairness and instability problems in IEEE 802.11-based multihop networks. Then we utilize the collision detection mechanism of the IEEE 802.11 protocol which is often ignored by most of the people to design a novel collision detection mechanism-based MAC (CDMB-MAC) scheme to solve the short-term and long-term fairness and stability issues while providing a good aggregate throughput in many topologies.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

A Novel Low-Complexity Low-Latency Power Efficient Collision Detection Algorithm for Wireless Sensor Networks

Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Simulating unmanned aerial vehicle flight control and collision detection

An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application and development prospects.Since the UAV’s flight orientation is easily changeable,its orientation and flight path are difficult to control,leading to its high damage rate.Therefore,UAV flight-control technology has become the focus of attention.This study focuses on simulating a UAV’s flight and orientation control,and detecting collisions between a UAV and objects in a complex virtual environment.The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment.A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper,to improve the system performance and accelerate the collision-detection process.This provides a practical method for future studies on UAV flight position and orientation control,collision detection,etc.

Real Time Collision Detection Using Depth Texturing Spheres

In this paper,we present a novel collision detection algorithm to real time detect the collisions of objects.We gen- erate sphere textures of objects,and use programmable graphics hardware to mapping texture and check the depth of different ob- jects to detect the collision.We have implemented the algorithm and compared it with CULLIDE.The result shows that our algo- rithm is more effective than CULLIDE and has fixed executive time to suit for real-time applications.

Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition;2) the local features are mutually processed to improve the reliability of vehicle detection;3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions;4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.

Using LBG quantization for particle-based collision detection algorithm

Most collision detection algorithms can be efficiently used only with solid and rigid objects, for instance, Hierarchical methods which must have their bounding representation recalculated every time deformation occurs. An alternative algorithm using particle-based method is then proposed which can detect the collision among non-rigid deformable polygonal models. However, the original particle-based collision detection algorithm might not be sufficient enough in some situations due to the improper particle dispersion. Therefore, this research presents an improved algorithm which provides a particle to detect in each separated area so that particles always covered all over the object. The surface partitioning can be efficiently performed by using LBG quantization since it can classify object vertices into several groups base on a number of factors as required. A particle is then assigned to move between vertices in a group by the attractive forces received from other particles on neighbouring objects. Collision is detected when the distance between a pair of corresponding particles becomes very small. Lastly, the proposed algo- rithm has been implemented to show that collision detection can be conducted in real-time.

A collision detection approach in virtual environment of micromanipulation robot

Operators suffer much diffieulty in manipulating miero-size objects without the assistance of friendly interfaces due to the scaling effects in micro worht. The paper presented a general framework for mieromanipulation robot hased on virtual reality technology. With the framework we brought forward a FDH （Fixed Direction Hulls） based hounding box method to handle the eollision ,teteetion of the peg-in-hole mieroassembly. The eollision response model for the collision between micro needle and hole was presented. The virtual three and corresponding displacement were calculated with the model of bending deformation and pressing ,teformation. Experiments verify the validity of collision response model.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

Efficient and Cost-Effective Vehicle Detection in Foggy Weather for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems

Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.

DDoS Attack Detection Scheme Based on Entropy and PSO-BP Neural Network in SDN

SDN (Software Defined Network) has many security problems, and DDoS attack is undoubtedly the most serious harm to SDN architecture network. How to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security research. Based on the characteristics of SDN, a DDoS attack detection method combining generalized entropy and PSOBP neural network is proposed. The traffic is pre-detected by the generalized entropy method deployed on the switch, and the detection result is divided into normal and abnormal. Locate the switch that issued the abnormal alarm. The controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal switch. Experiments show that compared with other methods, the detection accurate rate is guaranteed while the CPU load of the controller is reduced, and the detection capability is better.

Prevalence of metabolic syndrome in type 2 diabetic patients: A comparative study using WHO, NCEP ATP III, IDF and Harmonized definitions

To determine the prevalence of metabolic syndrome (MetS) in Malaysian type 2 diabetic patients using WHO, NCEP ATP III, IDF and the new Harmonized definitions, and the concordance between these definitions. This study involved 313 patients diagnosed with type 2 diabetes mellitus (T2DM) at two Malaysian tertiary hospitals. Socio-demographic data were assessed using a pre-tested interviewer-administered structured questionnaire. Anthropometric measurements were carried out according to standard protocols. Clinical and laboratory characteristics were examined. Kappa (k) statistics were used for the agreement between the four MetS definitions. The overall prevalence rates of MetS (95% CI) were 95.8% (93.6-98.1), 96.1% (94.0-98.3), 84.8% (80.8-88.9) and 97.7% (96.1-99.4) according to the WHO, NCEP ATP III, IDF and the Harmonized definitions, respectively. The Kappa statistics demonstrated a slight to substantial agreement between the definitions (k = 0.179-0.875, p k = 0.875, p hest specificity (100%) in identifying MetS. In conclusion, the new Harmonized criteria established the highest prevalence of MetS among the four definitions applied. There was a very good concordance between the WHO and NCEP ATP III criteria. The extremely high prevalence of MetS observed in type 2 diabetic patients indicates an impending pandemic of CVD risk in Malaysia. Aggressive treatment of MetS components is required to reduce cardiovascular risk in T2DM.

Use of blood-based biomarkers for early diagnosis and surveillance of colorectal cancer

Early screening for colorectal cancer(CRC) holds the key to combat and control the increasing global burden of CRC morbidity and mortality. However, the current available screening modalities are severely inadequate because of their high cost and cumbersome preparatory procedures that ultimately lead to a low participation rate. People simply do not like to have colonoscopies. It would be ideal, therefore, to develop an alternative modality based on blood biomarkers as the first line screening test. This will allow for the differentiation of the general population from high risk individuals. Colonoscopy would then become the secondary test, to further screen the high risk segment of the population. This will encourage participation and therefore help to reach the goal of early detection and thereby reduce the anticipated increasing global CRC incidence rate. A blood-based screening test is anappealing alternative as it is non-invasive and poses minimal risk to patients. It is easy to perform, can be repeated at shorter intervals, and therefore would likely lead to a much higher participation rate. This review surveys various blood-based test strategies currently under investigation, discusses the potency of what is available, and assesses how new technology may contribute to future test design.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

Cyberspace Security Using Adversarial Learning and Conformal Prediction

This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted?self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.