Tracking Vehicles in Real Time

This study is novel as it expands the understanding of factors affecting real time visibility compliance. The article responds to calls for an indepth identification of the reasons for the gap between expectations on real time visibility and successful implementations. Strong impact of subcontracting on compliance suggests availability of resources within freight forwarders network including systems for storing data on truck and trailer plate numbers, interfaces amongst subcontractors IT systems are of immense importance for achieving real time visibility Drivers' digital skills and privacy concerns have been found to be an obstacle to achieve real-time visibility. For the supply chain theory contribution is the identification of weakly penetrated so far factors affecting both data sharing and connectivity. The study should help supply chain managers to decide whether implement real time visibility platform or wait.

This paper presents a real-time auditory and visual tracking of multiple objects for humanoid under real-world environments. Real-time processing is crucial for sensorimotor tasks in tracking, and multiple-object tracking is crucial for real-world applications. Multiple sound source tracking needs perception of a mixture of sounds and cancellation of motor noises caused by body movements. However its real-time processing has not been reported yet. Real-time tracking is attained by fusing information obtained by sound source localization, multiple face recognition, speaker tracking, focus of attention control, and motor control. Auditory streams with sound source direction are extracted by active audition system with motor noise cancellation capability from 48 KHz sampling sounds. Visual streams with face ID and 3D-position are extracted by combining skincolor extraction, correlation-based matching, and multiple-scale image generation from a single camera. These auditory and visual streams are associated by comparing the spatial location, and associated streams are used to control focus of attention. Auditory, visual, and association processing are performed asynchronously on different PC's connected by TCP/IP network. The resulting system implemented on an upper-torso humanoid can track multiple objects with the delay of 200 msec, which is forced by visual tracking and network latency.

Automatic license plate recognition (ALPR) technology has gained
widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great
significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.

Vehicle tracking is one of computer vision's most important applications, with applications ranging from robotics and traffic monitoring to autonomous vehicle navigation and many more. Even with the significant advancements in recent research, issues like occlusion, fluctuating illumination, and fast motion still need to be addressed, calling for more investigation and creativity in this field. This study performs a thorough examination of various vehicle-tracking approaches and suggests a thorough classification scheme that divides them into four main categories: strategies that rely on features, segmentation, estimate, or learning. Two wellknown methods are highlighted specifically in the estimation-based category: particle filters and Kalman filters.

The objective of this paper is to explore the role of human tracking technology, primarily the use of global positioning systems (GPS) in locating individuals for the purposes of mutual legal assistance (MLA), and providing location intelligence for use in interstate police cooperation within the context of transnational crime. GPS allows for the 24/7 continuous real-time tracking of an individual, and is considered manifold more powerful than the traditional visual surveillance often exercised by the police. As the use of GPS for human tracking grows in the law enforcement sector, federal and state laws in many countries are to a great extent undefined or even contradictory, especially regarding the need to obtain warrants before the deployment of location surveillance equipment. This leaves courts ruling on transnational crimes in the precarious position of having to rely on age-old precedents which are completely void to the new capabilities of today's tracking technologies. On one side of the debate are civil libertarians who believe the individual's right to be let alone is being eroded to the compromise of human rights, and on the other side are law enforcement agencies who wish to provide more precise evidence to judges and juries during hearings against suspects (particularly in issues pertaining to national security). This paper argues that there is a radical middle position, the via media: that a warrant process is legislatively defined and not only for MLAs but also to formalise existing informal interstate police cooperation. Safeguards are required to overcome the potential misuse of human tracking technologies by police officials and others in positions of power. And this particularly in light of the emerging implantable high-tech identification and tracking devices now commercially available.

Object identification and tracking applications of pattern recognition at video rates is a problem of wide interest, with previous attempts limited to very simple threshold or correlation (restricted window) methods. New high-speed algorithms together with fast digital hardware have produced a system for missile and aircraft identification and tracking that possesses a degree of "intelligence" not previously implemented in a real-time tracking system. Adaptive statistical clustering and projection-based classification algorithms are applied in real time to identify and track objects that change in appearance through complex and nonstationary background/foreground situations. Fast estimation and prediction algorithms combine linear and quadratic estimators to provide speed and sensitivity. Weights are determined to provide a measure of confi'dence in the data and resulting decisions. Strategies based on maximizing the probability of maintaining track are developed. This paper emphasizes the theoretical aspects of the system and discusses the techniques used to achieve realtime implementation. Index Terms-Image processing, intensity histograms, object identification, optical tracking, projections, tracking system, video data compression, video processing, video tracking. velopment of the real-time videotheodolite (RTV). U.S. Army White Sands Missile Range began the development of the RTV in 1974, and the system is being deployed at this time. Design philosophy called for a system capable of discriminatory judgment in identifying the object to be tracked with 60 independent observations/s, capable of locating the center of mass of the object projection on the image plane within about 2 per-Manuscript

1. Introduction Intelligent Space or iSpace is a new kind of computing system aiming at improving the environments of humans, creating a natural and easy to use solution. Its main feature is that the intelligence is not implemented separately in the actors, but it is distributed in the whole space. In this paper, we present a hand gesture and movement recognition system, whose purpose is to be used as an intuitive interface for the iSpace.

Augmented Reality has now progressed to the point where real-time applications are being considered and needed. At the same time it is important that synthetic elements are rendered and aligned in the scene in an accurate and visually acceptable way. In order to address these issues a real-time, robust and efficient 3D model-based tracking algorithm is proposed for a 'video see through' monocular vision system. The tracking of objects in the scene amounts to calculating the pose between the camera and the objects. Virtual objects can then be projected into the scene using the pose. Here, non-linear pose computation is formulated by means of a virtual visual servoing approach. In this context, the derivation of point-to-curves interaction matrices are given for different features including lines, circles, cylinders and spheres. A local moving edges tracker is used in order to provide real-time tracking of points normal to the object contours. A method is proposed for combining local position uncertainty and global pose uncertainty in an efficient and accurate way by propagating uncertainty. Robustness is obtained by integrating a M-estimator into the visual control law via an iteratively re-weighted least squares implementation. The method presented in this paper has been validated on several complex image sequences including outdoor environments. Results show the method to be robust to occlusion, changes in illumination and misstracking.

the rise in cyber attacks has overwhelmed the monetary resources and human ability to analyze and combat every new form of cyber threat in the cyber security industry. With the increasing digital presence, there is a large amount of personal and financial information that should be protected from cyber attacks. In fact, cyber attacks can ruin the reputation of an organization or letdown the organization completely. This research examines the use of AI in the enhancement of cyber security. Recent developments in artificial intelligence are transformational and have exceeded the level of human performance in tasks such as data analytics. The study adopted the thematic literature review method, and data were sourced from Google scholar, science direct, research gates, academia, and others. The investigation revealed that application of AI in controlling cyber attack has advantages and disadvantages; however, the advantages outweigh the disadvantages. This researcher discovers that with...

Sosyal medya platformlarından, mobil uygulamalardan, telefon operatörlerinden ve daha birçok farklı kaynaktan elde edilen pek çok kişisel veri (iletişim veya kimlik bilgileri, fotoğraf vb.) çeşitli sebeplerle kullanılmaktadır. Verinin çok hızlı bir şekilde artış göstermesi ve dijital alanlarda belirli amaçlarla işlenerek alınıp satılan bir metaya dönüştürülmesi gizlilik ihlallerine yönelik birçok tartışmayı da beraberinde
getirmektedir. Bu verilerin en fazla üretildiği alanların başında mobil uygulamalar gelmektedir. Bir mobil uygulama indirildiğinde o uygulamayı kullanabilmek için fotoğraf, e-posta, kişiler, dosyalar, konum, sesler vb. uygulamalara erişim izni istemekte ve izin verilmediği taktirde uygulamalar kullanılamamaktadır. Dolayısıyla kullanıcıların mobil uygulamaları kullanırken mahremiyetlerine yönelik aldığı kararlar, gizlilik ihlallerine karşı tutumlarını ve farkındalık düzeylerini ortaya koymaktadır. Çalışma mobil uygulamaların kullanım pratiklerine ilişkin mevcut durumu ortaya koyma, mobil uygulamalar aracılığıyla kişisel bilgilerin işlenmesi
adına gerçekleşen işlemlere karşı bireylerin bilgi düzeylerini ve bakış açılarını saptama amacı taşımaktadır. Nicel araştırma desenlerinden tarama araştırmasına dayanan çalışmada, veriler anket tekniği ile toplanmış ve SPSS 25.0 programında analiz edilmiştir. Amacına yönelik örnekleme yöntemi kullanılarak Türkiye’de mobil uygulama kullanan 18 yaş üstü 1762 kişiyle yapılan araştırma sonucunda, bireylerin
mobil uygulamaları kullanırken kişisel verilerine dair endişe duymalarına rağmen mobil uygulamaları kullanmaya devam ettikleri ve kişisel verilerinin üçüncü kişiler tarafından kullanılmasına yönelik farkındalık düzeylerinin düşük olduğu sonucuna ulaşılmıştır.

One of the main challenges of planetary rover missions is the traversability of rough terrain areas, steep slopes and very soft soil fields. In the past, rovers repeatedly struggled while driving especially in these soft soils, with the Spirit rover even having to be abandoned. With ESA's upcoming ExoMars mission, a rover with an articulated suspension will land on Mars in 2021 that offers additional degrees of freedom through its actuated deployment joints. A full-body kinematic model allows to take full advantage of such a locomotion system. In this thesis, such a model was derived for EXM-BB2, a mobility subsystem prototype for the ExoMars rover. The model allows to specify desired body velocities and additional constraints on joint rates to achieve desired motions and includes a slip formulation on wheel level. Additionally, kinematic redundancies can be exploited by the introduction of secondary sub-tasks through null-space projection. Based on this model, two locomotion mo...

The relationship between electromyography (EMG) and age group is to identify and analysis the electrical activity/signal on upper-limb muscle. Nine participants/subjects with their arm fixed at 90o angle and perform isometric contraction using the dynamometer. The EMG is one kind of biological signal that can be recorded by using signal sensors. A raw EMG signal is obtained from the experiment and then will be filtered to get accurate results. The Root-Mean-Square (RMS) and Mean-Absolute-Value (MAV) is then obtained by using the calculations from the raw data. The results shows the EMG activity for younger age group is more significant (higher activity) compare to the adult and older age group.

We present results from a tracker which is part of the integrated surveillance system ADVISOR which is designed to operate in real-time in a distributed local network of offthe-shelf computers. For PETS2001, our indoor people tracker has been modified to include the tracking of vehicles in outdoor scenes. An effort has been made only to use simple techniques in these modifications. Solutions include splitting and merging of regions which have been processed by the motion detector, as well as the temporal incorporation of static objects into the background image.

Sometimes the futile process at the parking gates exasperates the customers waiting in long queues to enter. What if parking spaces were fully automated and human interaction were tapered off making the process brisk? The exposition aims at solving the snags stated above and assist the organization with the fully automated Parking Bill System. The implementation of Artificial Intelligence in the parking system could assuage the tortuous process and provide a hassle-free parking experience. With this project, we give incentives to people who finish their work faster and exit the parking lot sooner. The project will help the parking space to be automated and provide a new user experience.

In this era the Artificial Intelligence (AI) combined with computer vision techniques are seamlessly applied across various domains. The medical image analysis domain is also gaining the advantage of the AI solutions. The medical domain requires real-time analysis of the images and videos being generated for providing automatic assistance to the experts. Artefacts Artefact are image features that do not represent any original scene but occur due to a quirk of the modality itself. The presence of artefacts in Images or video frames poses a challenge for efficient analysis to extract the relevant information. The actual information in the image for understanding the given scene usually lies behind the presence of these artefacts. Examples of artefacts include distortion, blurring, occlusion by other objects and so on. The presence of these in an image must be identified and in the case of video analysis, the frames without the presence of with minimal presence of artefacts must be considered for analysis. Endoscopy is a procedure that involves both diagnosis and therapeutic solutions in various inner regions of human body. Analyzing the image data generated by this procedure using AI based solution can provide an assistance to the medical experts. The work focuses on providing deep-learning based result based on the standard YOLO V3 model for artefact detection and localization on the endoscopy frames has been proposed. The proposed model has achieved a mean average precision (mAP) of 0.76 and an Intersection of Union (IoU) of 0.63 by training the model on the images from the widely available Endoscopy Artefact Detection (EAD) dataset.

Declaration I hereby declare that this thesis entitled "Ethiopian Car License Plate Recognition Using Deep Learning" was prepared by me, with the guidance of my advisor. The work contained herein is my own except where explicitly stated otherwise in the text, and that this work has not been submitted, in whole or in part, for any other degree or professional qualification.

Mean shift tracking is a widely used tool for robust and quick tracking of the object in video, against partial occlusion and clutter. This paper proposes a Parzen window based mean shift tracker for visual object tracking. It combines three RGB color histogram and generates its Kernel Density estimation using Gaussian Kernel. Parzen window density estimation method is used to solve the basic problems of mean shift like, divide by zero problems in the weight computation and its association to tracking interruption. The Parzen window interpolates the histogram of target candidate. The experimental results show that proposed method performs better than traditional mean shift for fast moving objects and objects having background clutter at moderate increase in computational complexity. © 2014 Elixir All rights reserved. ARTICLE INF O Articl e h istory: Received: 10 December 2013; Received in revised form: 20 January 2014; Accepted: 1February 2014;

Background: Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object is larger than a flat panel detector, most CBCT images contain truncated data or incomplete projections, which degrade image quality. In this work, we propose the truncation effect reduction for fast iterative reconstruction in CBCT imaging inside the field of view (FOV).Methods: The volume matrix size of FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was also modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with the conventional filtered back projection (FBP) method. To validate the proposed method, we used a head phantom for evaluation...

İnternet teknolojisi üzerinden birbirleriyle haberleşebilen nesnelerin meydana getirdiği ağ olan Nesnelerin İnternet'i (IoT) önemli bir veri kaynağı olarak değerlendirilmektedir. Nesnelerin İnterneti (IoT) ile veri paylaşımı yapılabilmekte, aynı zamanda yaşam kalitesini iyileştirmek, yeni hizmetler ve uygulamalar oluşturmak için tüm cihazlara bağlanılabilmektedir. IoT, çeşitli cihazların ve nesnelerin adreslenebilir olmasını sağlamakla birlikte, bu cihazları tanıyabilmekte ve yerini belirleyebilmektedir. Nesnelerin birbirine bağlandığı bu ağın kullanımı ise etik sorunları da beraberinde getirmektedir. Dijitalleşme sürecinin bir parçası olan IoT'un yararlı kullanım alanları ile birlikte güvenlik açıkları, gizlilik ihlalleri ve mahremiyete saygısızlık, gözetim toplumunun inşa edilmesi gibi çeşitli güçlükleri ön plana çıkmaktadır. Bu güçlükleri öncelikli olarak ele almak ve IoT ürünleri ve hizmetleri için güvenlik ve gizliliğin sağlanması temel olmalıdır. Buradan hareketle çalışma, IoT teknolojilerinin etik yaklaşımla kullanımına dikkat çekmeyi ve gözetim, bilgi güvenliği, gizlilik sorunlarını tartışmayı amaçlamaktadır. Çalışma, IoT teknolojisinin kullanımı kapsamında, tehditlerin ve çözüm önerilerinin incelenmesi açısından önemlidir. Sonuç olarak mahremiyet ve şeffaflık ile gizlilik ve gözetimin aynı anda bir arada olamayacağı; bu nedenle duruma göre esnek bir biçimde karşılanması gerektiği anlaşılmıştır. Güvenlik sorunu konusunda da sorumluluğun, IoT cihazı sağlayıcıları ve kullanıcıları tarafından paylaşılmasının gerektiği görülmüştür. Diğer yandan kullanıcının gizlilik ve güvenlik konusunda bilinçli olmasını sağlamak elzemdir.

This paper presents the implementation (real time and simulation) of an integral sliding mode controller (ISMC) for the four degrees of freedom (DOF) of the humanoid BERT II 1 robot arm, in order to deal with the inaccuracies and unmodelled nonlinearities in the dynamic model of the robot arm. This is a task space controller, tracking Cartesian coordinates x and y. The controller has been implemented using shoulder flexion, shoulder abduction, humeral rotation and elbow flexion joints of the BERT II right arm. The main controller is the combination of a feedback linearization (FL) scheme and an ISMC. The redundant DOF are controlled by a bio-mechanically inspired posture controller, to generate human like motion pattern based on recent work. Good realtime tracking results demonstrates effectiveness of the scheme.

This paper presents the implementation (real time and simulation) of an integral sliding mode controller (ISMC) for the four degrees of freedom (DOF) of the humanoid BERT II 1 robot arm, in order to deal with the inaccuracies and unmodelled nonlinearities in the dynamic model of the robot arm. This is a task space controller, tracking Cartesian coordinates x and y. The controller has been implemented using shoulder flexion, shoulder abduction, humeral rotation and elbow flexion joints of the BERT II right arm. The main controller is the combination of a feedback linearization (FL) scheme and an ISMC. The redundant DOF are controlled by a bio-mechanically inspired posture controller, to generate human like motion pattern based on recent work. Good realtime tracking results demonstrates effectiveness of the scheme.

We present a simulation and optimisation based decision-support system to improve last-mile distribution of goods during disasters. In particular, the impact of road closures on supply and optimal transfer points is analysed by considering road, off-road and air transportation. To deal with panic buying, stockpiling and various consequences of a disaster over a rolling time-horizon, a limited number of transfer points have to be selected. At these points, relief organisations and retailers can transfer shipments to prevent stock-outs at regional stores. Furthermore, shipment requests are scheduled to vehicles, routed and optimised during each simulation run. Results show a significant reduction in average lead times when coordinating last-mile distribution during disasters.

In this article, we examined the widely used Digital Tracking and Monitoring (DTM) applications in the world and in Turkey through the scale consisting of 16 questions put forward by Floridi, Cowls, Taddeo, and Morley in their article "Ethical Principles of SARS-CoV-2 Digital Tracking and Monitoring (DTM) Systems". With the COVID-19 epidemic, new ethical problems have arised with the DTM applications developed to reduce the spread of the disease. In order to address these ethical issues, we have tried to show the scope of the questions Floridi, Cowls, Taddeo, and Morley pose to these practices and their relationship with digital ethics. We tried to increase the effectiveness of the scale put forward by Floridi, Cowls, Taddeo and Morley through different DTM applications and to improve the depth of interpretation in terms of comparison. We aimed to examine the extent to which DTM applications provide ethical validity in terms of content and function, based on the qualities of the practices. The purpose of this review; is to make an inference about the validity of these practices in extraordinary situations such as pandemics. We think that this ethical validity will shed light on how DTM applications which function on personal data should be developed by governments or private institutions. We will try to present data on how one of today's important ethical issues, such as the privacy of personal data, can be handled in the event of an epidemic that swept the world.

Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab's Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

Object detection in streaming images is a major step in different detection-based applications, such as object tracking, action recognition, robot navigation, and visual surveillance applications. In most cases, image quality is noisy and biased, and as a result, the data distributions are disturbed and imbalanced. Most object detection approaches, such as the faster region-based convolutional neural network (RCNN), single shot multibox detector with 300OE300 inputs (SSD300), and you only look once version 2 (YOLOv2), rely on simple sampling without considering distortions and noise under real-world changing environments, despite poor object labeling. In this paper, we propose an incremental active semi-supervised learning (IASSL) technology for unseen object detection. It combines batch-based active learning (AL) and bin-based semi-supervised learning (SSL) to leverage the strong points of AL's exploration and SSL's exploitation capabilities. A collaborative sampling method is also adopted to measure the uncertainty and diversity of AL and the confidence in SSL. Batch-based AL allows us to select more informative, confident, and representative samples with low cost. Bin-based SSL divides streaming image samples into several bins, and each bin repeatedly transfers the discriminative knowledge of convolutional neural network deep learning to the next bin until the performance criterion is reached. The IASSL can overcome noisy and biased labels in unknown, cluttered data distributions. We obtain superior performance, compared with the state-of-the-art technologies, such as Faster RCNN, SSD300, and YOLOv2. INDEX TERMS Object detection, convolutional neural network, incremental deep learning, active learning, semi-supervised learning.

ARtwin project: A report specifying the pivot format of the map that will be used to develop the services provided by the ARtwin platform. This project has received funding from the European Union's Horizon 2020 Framework Programme for research and Innovation under Grant Agreement no 856994.

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-theart approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-theart methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

In the dynamically moving context of mass-customization of products, new manufacturing control architectures, based on the consideration of highly distributed, autonomous, adaptable and efficiently cooperating units integrated by a plug-and-operate approach, seem to be efficient alternatives. Amongst them, the concept of a product-driven distributed control promotes an active role of the product in its own manufacturing. This paper focuses on the possibilities to implement this concept on a case study using wireless sensor networks.

This paper is for transportation security and fleet management. An embedded device is designed and fabricated into PCB. The embedded device having SIM908, integrated GSM/GPS module, is kept inside vehicle which acquires the speed and location of the vehicle and send data to the server. The fuel level detection circuit calculates the fuel level from the fuel gauge which is present in all the vehicles. Connecting to internet requires additional money deducted by internet provider and the more use of battery. In order to be efficient, SMS function is included to start/stop internet connection. When the owner needs to track the vehicle, a SMS is sent to the device which then establishes the connection to the TCP server through GPRS. Data such as location speed and fuel level is sent to the server and stored in a database. The web page and android application designed for this purpose tracks the vehicle three dimensionally and show them in integrated map. Emergency call function is designed. The driver can call to the police, ambulance and the owner of the vehicle in emergency situations. The owner can dial a call to the vehicle at situations when the driver goes beyond the limits like high speed driving and wrong direction.

Recently, high resolution visual methods based on direct-phase measurement of periodic patterns has been proposed with successful applications to microrobotics. This paper proposes a new implementation of direct-phase measurement methods to achieve 3-DoF (degrees of freedom) visual servoing. The proposed algorithm relies on a single frequency tracking rather than a complete 2D discrete Fourier transform that was required in previous works. The method does not require any calibration step and has many advantages such as high subpixelic resolution, high robustness and short computation time. Several experimental validations (in favorable and unfavorable conditions of use) were performed using a XYθ microrobotic platform. The obtained results demonstrate the efficiency of the frequency-based controller, this in term of accuracy (micrometric error), convergence rate (30 iterations in nominal conditions) and robustness.

Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference using graphs that correspond to factorised representations of high-dimensional probability distributions. The problem of tracking objects from noisy measurements is inherently a probabilistic one and the use of PGMs to solve this problem is therefore appropriate. iv Stellenbosch University https://scholar.sun.ac.za

Purpose: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient-and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans. Methods: In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts. Results: For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection of uncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction. Conclusions: Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

This paper presents a patient monitoring system that can support multiple IoT (Internet of Things) rehabilitation devices. The physicians can plan the treatment for each patient via the Internet connection system with our software which has three sub processes: device configuration, monitoring, as well as feedback. The proposed system has been designed to support multiple devices and various types of rehabilitation devices by using the common HTTP protocol. This allows various IoT platforms to be programed to control various rehabilitation devices and connect to the Internet via WLAN, LAN, or cellular networks. The network connection test reported that the system can support many users’ devices sending the measurement data concurrently as well as correctly. Moreover, we propose a network selection algorithm for mobile devices that considers both Wi-Fi and cellular using energy efficiency, packet delay, and UDP success rate as key parameters to enhance network connection reliability....

Complex hardware systems become more and more ubiquitous in mission critical applications such as military, satellite, and medical to name but a few. In such applications, reliability remains a primary concern because a failure that occurs during their normal operations might produce important catastrophes like loss of life or loss of money. All these failures are often caused by minuscule bug that exists inside the software which controls the systems, or within the hardware itself. In addition, most of these systems cannot be interrupted while working, even for a few seconds a year, making it difficult to repair bugs found during their normal operations. The main purpose of this work is to build efficient verification techniques for asynchronous concurrent systems. Because of the pivotal roles these systems assume in a given application, designers of such systems must keep development and maintenance costs under control and meet nonfunctional constraints on the design of the system...

The continuous development that the world is on must be pursued into human services, among these services provided by the modern world are the ability to detect faces, controlling and monitoring crowded places, the roads, auto-drive cars, and other smart systems that we can see through our daily life, these systems are known as object detection systems. This branch is getting development continuously and it gets big attention and support from international institutes, military institutions, and security companies. Object detection is a big branch and is used in many industries like medical, food production, plastic industries, controlling and monitoring roads, and more. In this paper we will explain information about object detection, its types, the YOLO algorithm, and a small idea that we made for controlling and monitoring arms showed by monitoring cameras or images that contain arms, we made it using the YOLO algorithm with the help of OpenCV and python libraries.

This paper focuses on an electronic toll collection (ETC) system using radio frequency identification (RFID) technology. Research on ETC has been around since 1992, during which RFID tags began to be widely used in vehicles to automate toll processes [1]. The proposed RFID system uses tags that are mounted on the windshields of vehicles, through which information embedded on the tags are read by RFID readers, The proposed system eliminates the need for motorists and toll authorities to manually perform ticket payments and toll fee collections, respectively.The transmitter will be charged by the operator of the booth office and the data will be stored.It will be get sensed by the IR. receiver mounted at the toll plaza, the fare will get deductedautomatically according to the toll charged and the remaining amountwill be displayed. Stepper motor is used to open and close the gate. Data information are also easily exchanged between the motorists and and toll authorities, thereby enablin...

Transportation system is the backbone of developing country. Intelligent transportation is need of today to provide better conditions to the public. Automatic licence plate system is one of the important elements of intelligent transportation system. In this paper, a novel technique for Indian license plate recognition based on deep learning and random forest is proposed. It has been evident that features are extracted from region with different input characters segmented in images of license plate so that their analysis has been carried out. Certain Features has been parametric and subjected to various characterization of license plate therefore heuristic and non-heuristic approaches are being implemented; however in this paper; deep learning random forest technique has been categorized and implemented for recognition of plate with their feature extraction mechanism. Comparative Analysis has been performed with existing techniques and results obtained from analysis that rate at whi...

This paper describes the different robotics tasks which can be realized using the visual servoing approach. A classification of these tasks, based on the virtual linkage formalism, is proposed and the different properties that the visual features and their related interaction matrix have to satisfy are presented. For the most classical virtual linkages between a camera and its environment, several combinations of visual features are selected. Finally, we recall the application of the task function approach to visual servoing by focusing on automatic control aspects.

Evaluating sums of multivariate Gaussian kernels is a key computational task in many problems in computational statistics and machine learning. The computational cost of the direct evaluation of such sums scales as the product of the number of kernel functions and the evaluation points. The fast Gauss transform proposed by Greengard and Strain (1991) is a $\ epsilon $-exact approximation algorithm that reduces the computational complexity of the evaluation of the sum of $ N $ Gaussians at $ M $ points in $ d $ dimensions from $\ ...

Every year, millions of people around the world die from road accidents, excessive or unsuitable speed is one of the main factors responsible for accidents, and it is implicated in a third of deaths. The main objective of this paper is to follow the driver from the speed of the vehicle as well as to know his behavior and his physical state on the roads to reduce the risk of accidents. In this paper, we monitor the instantaneous speed of the vehicle using a control system that can be mounted in any vehicle equipped with the OBD-2 protocol (on-board diagnostics). A monitoring chain based on a PIC microcontroller will be mounted in the vehicle as well as other peripherals such as an SD memory card module, a GPS geographic positioning sensor, a shock sensor and a global system for mobile communications GSM module. Unauthorized speed and the location data of the vehicle will be recorded in the SD memory card with accurate date and clock. In the event of an accident, an emergency message containing the latest speed and position data will be sent through the GSM module to the nearby medical center and local police as well as to the operator or owner (in case of car rental).

In this paper we propose a pose regression method employing a convolutional neural network (CNN) fed with single 2D images to estimate the 3D orientation of a specific industrial part. The network training dataset is generated by rendering pose-views from a textured CAD model to compensate for the lack of real images and their associated position label. Using several lighting conditions and material reflectances increases the robustness of the prediction and allows to anticipate challenging industrial situations. We show that using a geodesic loss function, the network is able to estimate a rendered view pose with a 5 • accuracy while inferring from real images gives visually convincing results suitable for any pose refinement processes.

Classical Image-Based Visual Servoing (IBVS) makes use of geometric image features like point, straight line and image moments to control a robotic system. Robust extraction and real-time tracking of these features are crucial to the performance of the IBVS. Moreover, such features can be unsuitable for real world applications where it might not be easy to distinguish a target from rest of the environment. Alternatively, an approach based on complete photometric data can avoid the requirement of feature extraction, tracking and object detection. In this work, we propose one such probabilistic model based approach which uses entire photometric data for the purpose of visual servoing. A novel image modelling method has been proposed using Student Mixture Model (SMM), which is based on Multivariate Student's t-Distribution. Consequently, a vision-based control law is formulated as a least squares minimisation problem. Efficacy of the proposed framework is demonstrated for 2D and 3D positioning tasks showing favourable error convergence and acceptable camera trajectories. Numerical experiments are also carried out to show robustness to distinct image scenes and partial occlusion.

The sheriffs, wrote John Carpenter in his 1419 book of the customs of London, 'are called "the eyes of the mayor"'. They are 'the eyes of the mayor, watchful and supportive of the responsibilities which the said mayor, as one person, is not able to bear on his own' (Sunt quoque Vicecomites Majoris oculi, conspicientes et supportantes partem sollicitudinis quae dicti Majoris personae singularitas portare non sufficit). 2 At first glance, Carpenter's metaphor does not seem at all surprising: the inhabitants of late medieval English towns were accustomed to think of their communities as urban bodies. The organological metaphor was so familiar that it could serve multiple and, sometimes, conflicting functions. It informed contemporary attitudes towards public health and animated far-reaching social, moral, and environmental policies. 3 Politically, organic imagery could appeal for a state of reciprocity between the limbs of the urban body politic. More contentiously, it could demand the subordination of the various members of the body to the chief magistrate, the 'head'. Carpenter's appropriation of the metaphor was unusual because of his interest not only in the 'head', but also in the 'eyes'. If the 'head' represented intellect and reason, and was the source of wisdom, the 'eyes' were the senses. The burden of office in London was too great for any one man, Carpenter suggested. The mayor could not do everything; he needed help to discharge his official duties. The sheriffs were there to share the heavy weight of public responsibility. They were the mayor's 'eyes'; they could see what he could not. 2 Carpenter was not the first writer to approach the practice of government as a sensory experience. In Book 5 of the Policraticus, John of Salisbury deployed his classical learning to imagine the twelfth-century kingdom of England as a human body, in which the king was the head, and the judges and sheriffs-'provincial governors' in the language of the Roman Empirewere the eyes, the ears, and the tongue. These three organs endowed the prince's judicial officers with the faculties of sight, hearing, and taste. Exercised by others on his behalf, these senses joined body and mind, increased the prince's cognitive powers, and enabled him to administer justice through greater knowledge and understanding. 4 Carpenter's description of the London sheriffs differs in three respects. First, where Salisbury is concerned with princely virtue and with the positive role of the prince, Carpenter's tone is less confident and more apprehensive. Secondly, where Salisbury's framework is multi-sensory, Carpenter's attention is exclusively to the eyes. And thirdly, Carpenter's conception of the sheriffs' vision is much more literal. The nineteenth-century editor of the London custumal translated the participle conspicientes as 'ever on the watch'. 5 Against whom were the sheriffs to be vigilant? Who were they to watch? Which areas of the city were they to watch? The short passage from Carpenter's Liber albus helpfully introduces the theme of this essay: the relationship between surveillance and urban disorder. Carpenter's choice of the noun sollicitudo, which could be translated not only as 'duty', but also as 'anxiety', conveyed the general uneasiness that surrounded the mayoral office in early fifteenth-century London. The memory of the 1380sof confrontations on the streets of the capital between the supporters of rival claimants to the London mayoralty, John of Northampton and Nicholas Brembrehad not dimmed by the late 1410s, when Carpenter completed his book. 6 But in prioritizing the eyes over the ears, Carpenter's

Automated Vehicle License Plate (VLP) detection and recognition have ended up being a significant research issue as of late. VLP localization and recognition are some of the most essential techniques for managing traffic using digital techniques. In this paper, four smart systems are developed to recognize Egyptian vehicles' license plates. Two systems are based on character recognition, which are (System1: Characters Recognition with Classical Machine Learning) and (System2: Characters Recognition with Deep Machine Learning). The other two systems are based on the whole plate recognition which are (System3: Whole License Plate Recognition with Classical Machine Learning) and (System4: Whole License Plate Recognition with Deep Machine Learning). We use object detection algorithms, and machine learning based object recognition algorithms. The performance of the developed systems has been tested on real images, and the experimental results demonstrate that the best detection accuracy rate for VLP is provided by using the deep learning method. Where the VLP detection accuracy rate is better than the classical system by 32%. However, the best detection accuracy rate for Vehicle License Plate Arabic Character (VLPAC) is provided by using the classical method. Where VLPAC detection accuracy rate is better than the deep learning-based system by 6%. Also, the results show that deep learning is better than the classical technique used in VLP recognition processes. Where the recognition accuracy rate is better than the classical system by 8%. Finally, the paper output recommends a robust VLP recognition system based on both statistical and deep machine learning.

Sur quelques modules d'Iwasawa semi-simples * Jean-François Jaulent & Jonathan W. Sands Résumé. Nous introduisons la notion de Z-extension cyclotomiquement ramiée sur un corps de nombres K pour obtenir des conditions arithmétiques générales de semisimplicité algébrique du module d'Iwasawa standard CK ∞ associé à une telle extension. Revenant ensuite sur les exemples déjà étudiés par Jaulent ou Carroll et Kisilevsky, nous montrons que cette nouvelle approche permet de retrouver des résultats connus mais aussi de rendre compte naturellement de la plupart des cas de non semi-simplicité. Les peuves reposent essentiellement sur la théorie-adique du corps de classes.

Knowing the limits of transverse handling and vehicle stability is very important from the aspect of designing a system aimed at improving handling and safety. One of the phenomena that have a great impact on vehicle stability and handling is the sideslip angle. Unfortunately, direct measurement of the sideslip angle requires very complex and expensive equipment that is not suitable for installation on ordinary passenger vehicles; therefore, this quantity must be estimated on the basis of measurements of other parameters. As a result of limited information, this parameter is mainly estimated or linearized relative to lateral force when used for calculations though such calculations can be less accurate. In this paper it is concluded that a clear relationship can not be formed between the lateral force and the sideslip angle and that for precise determination of the sideslip angle several parameters need to be known such as tire construction, tire pressure, rim diameter, wheel camber as well as micro and macro road structure.

the rise in cyber attacks has overwhelmed the monetary resources and human ability to analyze and combat every new form of cyber threat in the cyber security industry. With the increasing digital presence, there is a large amount of personal and financial information that should be protected from cyber attacks. In fact, cyber attacks can ruin the reputation of an organization or letdown the organization completely. This research examines the use of AI in the enhancement of cyber security. Recent developments in artificial intelligence are transformational and have exceeded the level of human performance in tasks such as data analytics. The study adopted the thematic literature review method, and data were sourced from Google scholar, science direct, research gates, academia, and others. The investigation revealed that application of AI in controlling cyber attack has advantages and disadvantages; however, the advantages outweigh the disadvantages. This researcher discovers that with...