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In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of our proposed system and landing scheme.

Haze seriously affects the reliability of industrial systems, especially vision-based outdoor industrial systems such as autopilot systems. A majority of existing dehazing methods are not specififically designed for industrial systems and do not consider the reliability and resource cost of industrial system implementation. In this study, a novel meta-attention dehazing network (MADN) is proposed for direct restoration of clear images from hazy images without using the physical scattering model. Combined with parallel operation and enhancement modules, the meta-network automatically selects the most suitable dehazing network structure based on the current input hazy image by a meta-attention module. In addition, a novel feature loss calculated by the meta-network is proposed, which can accelerate the convergence of the dehazing network to meet the application requirements of practical industrial systems. A large number of experimental results on synthetic and real-world datasets show that the proposed MADN satisfifies the needs of industrial systems.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding). However, in practice, the performance of computer vision algorithms is challenged by insufficient or biased training, observation noise, cluttered background, etc. By leveraging Bayesian deep learning (BDL), we have developed a novel, reliable vision-based framework to assist upper limb prosthesis grasping during arm reaching. This framework can measure different types of uncertainties from the model and data for grasping target recognition in realistic and challenging scenarios. A probability calibration network was developed to fuse the uncertainty measures into one calibrated probability for online decision making. We formulated the problem as the prediction of grasping target while arm reaching. Specifically, we developed a 3-D simulation platform to simulate and analyze the performance of vision algorithms under several common challenging scenarios in practice. In addition, we integrated our approach into a shared control framework of a prosthetic arm and demonstrated its potential at assisting human participants with fluent target reaching and grasping tasks.

In modern manufacturing, vision-based defect recognition is an essential technology to guarantee product quality, and it plays an important role in industrial intelligence. With the developments of industrial big data, defect images can be captured by ubiquitous sensors. And, how to realize accuracy recognition has become a research hotspot. In the past several years, many vision-based defect recognition methods have been proposed, and some newly-emerged techniques, such as deep learning, have become increasingly popular and have addressed many challenging problems effectively. Hence, a comprehensive review is urgently needed, and it can promote the development and bring some insights in this area. This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective. This review divides the recent methods into designed-feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios. Furthermore, this paper also summarizes the performance metrics for vision-based defect recognition methods. And some challenges and development trends are also discussed.

This study proposes a novel approach for a vision-based navigation problem using semantically segmented aerial images generated by a convolutional neural network. Vision-based navigation provides a...

SummaryThis article discusses the intervertebral motion present in the craniovertebral junction (CVJ) region. The CVJ region is bounded by the first three vertebras from the spinal column. It helps in bringing most of the neck motion. Intervention in this region requires surgery in which an implant is placed to stabilize the whole system. The various available implants need to undergo performance evaluation as their performance varies from region and anatomical diversity. For the Indian population, we are targeting to evaluate the performance of such an implant, testing it into a cadaver. The region of interest will be loaded as per the loading condition of an average human. Motion in these regions is evaluated using the camera. A preliminary test was done on a saw bone model of CVJ to assess the performance of segmentation methods. Multiple such ArUco markers are used to increase pose accuracy further, and the pose of the entire board of multiple tags provides us with reliable pose estimation. The absolute error ranged from a minimum of 0.1 mm to a maximum of 16 mm. At the same time, the mean and median absolute errors were 3.8961 mm and 3.35 mm. By considering the absolute lengths, the percentage error showed the following trends. The percentage error was between 3.9168% and 0.0230%.

Currently, fire detection systems based on computer vision techniques are highly appreciated for their intelligent detections at earliest. These systems use surveillance cameras to capture high-lev...

SUMMARYThis paper proposes a vision-based kinematic analysis and kinematic parameters identification of the proposed architecture, designed to perform the object catching in the real-time scenario. For performing the inverse kinematics, precise estimation of the link lengths and other parameters needs to be present. Kinematic identification of Delta based upon Model10 implicit model with ten parameters using the iterative least square method is implemented. The loop closure implicit equations have been modelled. In this paper, a vision-based kinematic analysis of the Delta robots to do the catching is discussed. A predefined library of ArUco is used to get a unique solution of the kinematics of the moving platform with respect to the fixed base. The re-projection error while doing the calibration in the vision sensor module is 0.10 pixels. Proposed architecture interfaced with the hardware using the PID controller. Encoders are quadrature and have a resolution of 0.15 degrees embedded in the experimental setup to make the system closed-loop (acting as feedback unit).

Recent years have seen an unprecedented spread of Unmanned Aerial Vehicles (UAVs, or "drones"), which are highly useful for both civilian and military applications. Flight safety is a crucial issue in UAV navigation, having to ensure accurate compliance with recently legislated rules and regulations. The emerging use of autonomous drones and UAV swarms raises additional issues, making it necessary to transfuse safety- and regulations-awareness to relevant algorithms and architectures. Computer vision plays a pivotal role in such autonomous functionalities. Although the main aspects of autonomous UAV technologies (e.g., path planning, navigation control, landing control, mapping and localization, target detection/tracking) are already mature and well-covered, ensuring safe flying in the vicinity of crowds, avoidance of passing over persons or guaranteed emergency landing capabilities in case of malfunctions, are generally treated as an afterthought when designing autonomous UAV platforms for unstructured environments. This fact is reflected in the fragmentary coverage of the above issues in current literature. This overview attempts to remedy this situation, from the point of view of computer vision. It examines the field from multiple aspects, including regulations across the world and relevant current technologies. Finally, since very few attempts have been made so far towards a complete UAV safety flight and landing pipeline, an example computer vision-based UAV flight safety pipeline is introduced, taking into account all issues present in current autonomous drones. The content is relevant to any kind of autonomous drone flight (e.g., for movie/TV production, news-gathering, search and rescue, surveillance, inspection, mapping, wildlife monitoring, crowd monitoring/management, etc.), making this a topic of broad interest.

Hace ya casi treinta años que ha comenzado el denominado giro afectivo en el ámbito de las ciencias sociales y con él han aparecido los primeros trabajos que profundizan en la importancia de la inclusión del componente emocional para el análisis del comportamiento, y en el caso que nos ocupa del comportamiento político. Partiendo de una revisión de estas aportaciones desde los años noventa del siglo xx en adelante, este trabajo pretende aportar evidencia empírica respecto de la importancia de la consideración de las emociones expresadas hacia los partidos políticos y los líderes a través de una visión basada en tres dimensiones de la emoción: presencia, intensidad y persistencia. Para llevar a cabo el análisis se utilizarán dos estudios demoscópicos propios realizados tras las elecciones generales de 2016 y 2019 en España.

Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture.

This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.

Purpose Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out. Findings The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel. Originality/value The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.

The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice., Comment: In IMWUT '21 (Distinguished Paper Award); UbiComp '21 Best Presentation Award

In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation. This work was supported in part by the National Key Research and Development Program under Grant 2018YFB0105402 and Grant 2018YFB0105703, in part by the Fundamental Research Funds for the Central Universities under Grant 2019CDXYQC0003, Grant 244005202014, 2019, and Grant 2018CDXYTW0031, in part by the National Natural Science Foundation of China under Grant 51806024, in part by the Chongqing Research Program of Foundation and Advanced Technology under Grant cstc2017jcyjAX0276, and in part by the Venture and Innovation Support Program for Chongqing Overseas Returnees under Grant cx2018051.

It is very important to have a slag detection system (SDS) in steelmaking to detect slag and prevent its flow for improving the quality of steel produced. Slag raking is used for removing slag from...

The tremendous applications of unmanned aerial vehicles (UAVs), such as inspection in complex environments, search, and rescue missions, have established this area of research. The domain of UAVs a...

The evolution of artificial intelligence has served as the catalyst in the field of technology and is making our imaginations real. One of such creation is the birth of self- driving car (autonomous robot). In this paper, a self-driving car physical prototype based on traditional visual method of lane keeping, implemented on Raspberry Pi is proposed which is capable of maneuvering on various kind of tracks autonomously. The proposed method comprises of taking an image from a front facing dashboard camera of the car, detecting the lane from the image and analysing the deviation of the car from the road which is further used to keep the car on the track. The proposed method is implemented on a 1/10 scale car which contains Raspberry Pi 3 Model-B computer and Pi Cam Rev 1.3 for computations and processing. The testing was done without any human intervention on self-made lined track having all kind of turns. The experimental results demonstrate the effectiveness and the robustness of the self-driving car in terms of the navigation error while following the reference trajectory.

This paper means to comprehend vision-based relative navigation strategies which are utilized forsmall/micro satellites. Advancements dependent on this technique are utilized separately or joined with oneanother to deal with relative position issues. The benefits and detriments of vision-based relative navigationmodels change as indicated by space of utilization and stage type. Various strategies and approaches exist andneed distinctive assessment and advanced algorithms for variation, control, and sensor combination. Each ofthe models centering inside expects those ideal disposition conditions. This paper just spotlights on relativeroute and distance point of view. Additionally, the point of this article is to comprehend the connectionbetween's general navigation control frameworks and the adequacy of calculation, which are utilized inassessing the states during gathering or development flight/development.

Underwater monitoring activities are crucial for the preservation of marine ecosystems. Currently, scuba divers are involved in data collection campaigns that are repetitive, dangerous, and expensive. This article describes the application of Information Communication Technology (ICT) tools to underwater visual data for monitoring purposes. The data refer to a Posidonia Oceanica survey mission carried out by a scuba diver using a Smart Dive Scooter equipped with visual acquisition and acoustic localization systems. An acoustic-based strategy for geo-referencing of the optical dataset is reported. It exploits the synchronization between the audio track extracted from a camera and the transponder pings adopted for the acoustic positioning. The positioning measurements are employed within an extended Kalman filter to estimate the diver's path during the mission. A visual odometry algorithm is implemented within the filter to refine the navigation state estimation of the diver with respect to the acoustic positioning only. Moreover, a smoothing step based on the Rauch-Tung-Striebel smoother is applied to further improve the estimated diver's positions. Finally, the article reports the results of two different data processing for monitoring applications. The first one is an image mosaicking obtained by concatenating subsequent frames, whereas the second one refers to a qualitative distribution of the Posidonia Oceanica over the mission area accomplished through an image segmentation process. The two outcomes are plotted over a satellite image of the surveyed area, showing that the proposed process is an effective tool capable of facilitating divers in their monitoring and inspection activities.

In India round 1.5 lakh humans died per year in avenue twist of fate because of road accidents most of them were due to low vision and weariness problems. Weariness (Extreme Tiredness) or Fatigue is a major purpose of avenue accidents and has extensive implications for street safety. several deadly accidents can be avoided if the drowsy drivers are warned in time. In many cases it is observed that a car hits some object/obstacle on road due to low vision in that case an object detection and warning system can help to avoid such accidents. Basically, Weariness is a state of sleepiness which abnormally happen when we are very tired or whilst drunken. A spread of drowsiness detection strategies exist that monitor the driving force’s drowsiness state at the same time as driving and alarm the drivers if they're no longer concentrating on driving. The relevant features can be extracted from facial expressions including yawning, eye closure and head actions for inferring the level of weariness. The organic condition of driver’s body is analyzed for driver weariness detection. So, this utility overcomes the trouble of driver weariness detection and object/obstacle detection & warning whilst driving using eye extraction, facial extraction, object and its distance detection using different algorithms.

This study investigated learned helplessness (LH) experienced by male secondary students of the English language and examined the effects of a motivational program based on the L2 Motivational Self System (L2MSS). Primarily, we administered the Learned Helplessness Scale to 189 students in a public school in Tehran, along with the Student Behavior Checklist completed by their teachers to identify and screen students with helplessness. The final sample (n = 74) was randomly assigned to experimental and control groups after evaluating the initial results. By designing and implementing an intervention program based on Dornyei’s L2MSS and supportive teaching for a semester, positive results were found with alleviated helplessness symptoms and improved academic achievements in the experimental group. Based on the results, teachers and their motivational practices could help alleviate students’ LH. Furthermore, the durability of the improvements remained elevated at a 6-month follow-up after the intervention. Further implications are discussed.

The perception of the surrounding circumstances is an essential task for fully autonomous driving systems, but its high computational and network loads typically impede a single host machine from taking charge of the systems. Decentralized processing is a candidate to decrease such loads; however, it has not been clear that this approach fulfills the requirements of onboard systems, including low latency and low power consumption. Embedded oriented graphics processing units (GPUs) are attracting great interest because they provide massively parallel computation capacity with lower power consumption compared to traditional GPUs. This study explored the effects of decentralized processing on autonomous driving using embedded oriented GPUs as decentralized units. We implemented a prototype system that off-loaded image-based object detection tasks onto embedded oriented GPUs to clarify the effects of decentralized processing. The results of experimental evaluation demonstrated that decentralized processing and network quantization achieved approximately 27 ms delay between the feeding of an image and the arrival of detection results to the host as well as approximately 7 W power consumption on each GPU and network load degradation in orders of magnitude. Judging from these results, we concluded that decentralized processing could be a promising approach to decrease processing latency, network load, and power consumption toward the deployment of autonomous driving systems.

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

An automated container terminal (ACT) is a cutting-edge type of container terminal that uses automated equipment and sensors to achieve autonomous applications such as container loading/unloading, horizontal transportation, and yard operations. It has integrated state-of-the-art sensing technologies, ensuring low operating costs, high throughput capacity, and enhanced management security. Vision-based measurement (VBM) is an especially advanced technology that obtains richer surrounding information from captured image data in comparison with other technologies. Due to its great potential capabilities, it has played a critically important role in ACT to realize productive vision-based tasks, e.g., target recognition, positioning, and geometric determination. This paper generally presents an overview of a VBM system, typical applications of VBM systems in ACT, as well as the challenges and future development trends of VBM in ACT.

عرض البحث لقضية زيادة الحروف في القرآن الکريم برؤية جديدة قامت على التحقق من مفهوم الزيادة لدى العلماء ‘ واتخذ خمسة من حروف الزيادة کنموذج للدراسة ,وحاول جاهدا الربط بين السياقات القرآنية المتعددة التي يرد فيها الحرف الواحد ليدفع القول بزيادته وجمع التوجيهات التي تعين على ذلک مع الاستعانة بالناحية البلاغية والشرعية دون الاکتفاء بأقوال النحويين في الحرف. وقد جاء البحث في تمهيد وخمسة مباحث وخاتمة ‘ أما التمهيد فقد عرض لمفهوم الزيادة وبين اختلاف العلماء في المصطلح نفسه قديما وحديثا ‘ أما المباحث فقد تناولت خمسة حروف وهي : (من ‘ ما ‘ الباء ‘ لا ‘ الواو) جعلت کل حرف في مبحث وفق ترتيبها السابق قمت بدراسة السياقات وجمع أقوال العلماء فيها وحاولت التوفيق بينها والخروج بها عن حد الزيادة ‘أما الخاتمة فقد ذکرت فيها أهم النتائج ومنها: ـ بينت الدراسة اضطراب کلمة النحويين في مفهوم الزيادة , أو الصلة ـ کما هو الحال عند الکوفيين ـ ما بين قائل : إنها تعني أن الحرف لم يفد معنى جديدا وغيره. ـ بينت الدراسة أن أکثر القائلين بزيادة الحروف من النحويين ,وأنهم يراعون القواعد النحوية التي قامت على استقراء ناقص , أکثر ما يراعون المعنى. ـ تتبعت الدراسة أشهر وأهم الآيات التي حکم فيها بزيادة الحروف محل الدراسة ,وأثبتت أن لها تخريجات قوية غير القول بالزيادة. کما أوصت الدراسة بقيام دراسات متخصصة مستقلة لاستقراء کتاب الله ـ عز وجل ـ وتتبع المواضع التي يقال عن الحروف فيها بأنها زائدة , وجمع التخريجات الواردة في کتب التفسير والنحو لهذه الآيات التي يقال عن حروف المعاني فيها بأنها زائدة ثم تحمل على وجه صحيح يخرج الحرف عن حد الزيادة ؛ لأن هذا القول يفتح بابا من أبواب الطعن على کتاب الله ,وبخاصة في زماننا . ثم ذيلت البحث بثبت للمراجع والمصادر ‘والله من وراء السبيل. The research presented the topic of Affixes in The Glorious Quran with a new vision based on verifying the concept of Affixes to scholars, applying on five of these affixes as a model for this study. The researcher has done his best to link between the multiple Qur’anic contexts in which one affix is included in order to do away with the idea of its “meaningless”. Not only had the researcher, quoted the grammarians’ points of view, but he also provided Rhetorical and legal interpretations to support his argument. The study comes in an introduction, five chapters, and a conclusion, As for the introduction, it points out the concept of Affix and showed how scholars view it differently through the past and present times. As for the next five chapters, I singled a chapter for each affix of the five whereas I carefully examined contexts, collected scholarly statements, and tried to reconcile them to refute the claims of its meaninglessness. As for the conclusion, I mentioned the most important results, including: The study showed that the grammarians' word is disturbed in the concept of addition, or the link - as is the case with the Kufians - between those who say: It means that the letter did not benefit a new meaning and others. The study has shown that most of grammarians’ points of views on affix phenomenon are indecisive weather it is meaningful or meaningless as it is the case of Kufi grammarians. The study traces the commonest verses in which affixes are found obviously, and provides pieces of evidence which, unlike what is commonly claimed, they are meaningful. The study recommended the execution of independent specialized studies to go thoroughly in the Glorious Quran whereas these affixes are alleged to be meaningless and provide sound arguments of refutation. In the light of this correction of the perception of meaningfulness of affixes, new interpretation and grammatical explanation of these verses shall be a must as an urgent step to defend The Holy Book against recurrent attacks which, have been waged nowadays to challenge it. The paper is entailed with index, bibliography. May Allah guide us to the most righteous path.

In the early times the concept of Smart Cities have gained great popularity. The proposed Vision based parking system consist of an on-site development of an DOT NET FRAME WORK. This paper explains the approach to make easy to monitor and manage a parking area using a vision based automatic parking system. To find the available parking space in the most efficient manner and to avoid traffic congestion in the parking area, it is very necessity to manage the car parking. Nowadays by human personnel also we do manage, the car parking area and by using Sensors, we do monitor the availability of car park spaces. Even though in both we get to know the number of free parking spaces, but we don’t get the actual location available. The installation and maintenance cost of a Sensor based system varies as per the number of Sensors required. This paper shows that the performance has a more accuracy for a model of 4 space car park. The model indicated that the application of a vision based car parking management system would be able to detect and indicate the duration of car parking in the given space.

The process of determining the working parameters of reverse bucket excavators is mainly consulted through the Ministry of Construction norm. However, in the era of industrialization and modernization, machine and equipment are increasingly modern and innovative, making the determination of excavator productivity or parameters through the regulations in the old norms unsuitable. Furthermore, updating the norms through data collected in the field take tremendous amount of time and procedures as it is labor intensive. Therefore, this paper proposes a vision-based analysis in calculating excavator productivity using image processing applications and coding language to automatically determine the excavator productivity and bring results on the basis of analysing big data collected from validated construction sites. To be specific, this paper introduces a new method in calculating the excavator productivity by extracting crucial coefficients from hundred images of the excavators using an open-source software, then compare with the traditional method to identify and analyse the importance of this new method and the practical use it might bring to the construction industry.

This paper presents an on-board, centralized autonomous navigation algorithm able to reconstruct the trajectories of a fleet of CubeSats relative to an asteroid binary system. The algorithm is executed on-board the main spacecraft, which takes relative measurements using a narrow optical camera and an additional relative measurement, being inter satellite ranging in this paper. The image processing algorithm detects and tracks the CubeSats, deriving line-of-sight measurements. A non-linear estimation filter is employed to reconstruct the trajectory. The algorithm is tested using high-fidelity trajectory simulator and synthetic images generation routines.

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

Aquaculture farming can help soften the environmental impact of overfishing by fulfilling seafood demands with farmed fishes. However, to maintain big scale farms can be challenging, even with the help of underwater cameras affixed in farm cages, because there are hours’ worth of footages to sift through, which can be a laborious task if performed manually. Vision-based system therefore could be deployed to filter useful information from video footages, automatically. This work proposed to solve the above mentioned problems by deploying the; 1) Extended UTAR Aquaculture Farm Fish Monitoring System Framework (UFFMS), being the handcrafted method, and 2) Faster Region Convolutional Neural Network (Faster RCNN), being the CNN-based method, for fish detection. These two methods could extract information about fishes from video footages. Experimental results show that for well-lit footages, Faster RCNN performs better, compared to the extended-UFFMS. However, accuracy of Faster RCNN drops drastically for non-well-lit footages, at an average of 28.57%, despite still having perfect precision scores.

Depth data from conventional cameras in monitoring fields provides a thorough assessment of human behavior. In this context, the depth of each viewpoint must be calculated using binocular stereo, which requires two cameras to retrieve 3D data. In networked surveillance environments, this drives excess energy and also provides extra infrastructure. We launched a new computational photographic technique for depth estimation using a single camera based on the ideas of perspective projection and lens magnification property. The person to camera distance (or depth) is obtained from understanding the focal length, field of view and magnification characteristics. Prior to finding distance, initially real height is estimated using Human body anthropometrics. These metrics are given as inputs to the Gradient-Boosting machine learning algorithm for estimating Real Height. And then magnification and Field of View measurements are extracted for each sample. The depth (or distance) is predicted on the basis of the geometrical relationship between field of view, magnification and camera at object distance. Using physical distance and height measurements taken in real time as ground truth, experimental validation is performed and it is inferred that with in 3m–7 m range, both in indoor and outdoor environments, the camera to person distance (Preddist) anticipated from field of view and magnification is 91% correlated with actual depth at a confidence point of 95% with RMSE of 0.579.

يحاول هذا البحث إعادة قراءة شعر الطائيين من خلال دلالة الصورة الفنية للذات والآخر عبر تشکلها في الصور البلاغية من استعارة وتشبيه وکناية، وعلاقتها بالبنى الصوتية والترکيبية والدلالية؛ لاستجلاء الدلالات الکامنة في بنية النص الشعري ومن ثم تأويل هذه الدلالات وردها إلى العنصر الأصلي الذي انبثقت منه بما يؤکد رؤية الشاعرين وموقفهما تجاه الذات والآخر من خلال رؤية منهجية تقوم على المنهج الموضوعي الذي يرتکز على تحليل العنصر الفکري المهيمن في النص أو ما يسمى بالتيمة (Theme). فبدأت بالصورة التشبيهية في شعر أبي تمام مبينا دورها الفاعل في التعبير عن مواقفه تجاه الآخر کما سنرى في قصيدة فتح عمورية وغيرها..، وکذلک قضايا الکون والوجود مثل الموت والدهر، ثم انتقلنا إلى الصورة التشبيهية عند البحتري والتي غلبت على بقية الصور الفنية في شعره، فجاءت عنده سهلة بسيطة واضحة، بعيدة عن التعقيد والإبهام والتکلف والوعورة، ثم انتقلنا إلى الاستعارة التي حظيت باهتمام کبير من الطائيين، فاعتمد عليها أبو تمام اعتمادا کبيرا في صوره الفنية، ثم توقفت عند الصورة الاستعارية في شعر البحتري التي اتسمت عنده بالمباشرة والوضوح والبعد عن الغموض والجنوح الخيالي البعيد، ثم تناولنا الصور الکنائية في شعر أبي تمام التي بعدت عن السطحية وجاءت متکاملة مع نسيجه الشعري، وقدم صوره الکنائية بدقة وبراعة وعلى درجة عالية من الإتقان والتصوير، أما الصور الکنائية في شعر البحتري فقد کانت قليلة قياسا بالصور التشبيهية والاستعارية. "The Image of the Self and the Other in the Poetry of Al-Taeyan A Thematic Reading" ABSTRACT This research attempts to re-read new poetry of Al-Taeyanthrough the Technical image of the self and the other in their poetry and its relations with sound, syntactic and semantic structures through a systematic vision based on the thematic approach, given that all levels of discourse coalesce with each other in order to integrate the Technical form, through studying the Technical image through its formation in the rhetorical images of Simile, metaphor and Metonymy, because they have a great presence in the structure of the poetic text of Al-Taeyan, and because they contain elements of suggestion and ambiguity who need a length of meditation in order to reveal the underlying implications of what is behind them, and then interpret these indications and return them to the original element that emerged from him confirming the vision of the poets and their attitude towards the self and the other, so I started with the analogous image in the poetry of Al-Taeyan, indicating its active role in expressing their positions Toward the other, as well as issues of the universe, life, and reality, then we moved to the metaphor that received great attention from Al-Taeyan, and finally the metaphoric images.

Deep learning artificial intelligence (AI) is a booming area in the research field. It allows the development of end-to-end models to predict outcomes based on input data without the need for manual extraction of features. This paper aims for evaluating the automatic crack detection process that is used in identifying the cracks in building structures such as bridges, foundations or other large structures using images. A hybrid approach involving image processing and deep learning algorithms is proposed to detect automatic cracks in structures. As cracks are detected in the images they are segmented using a segmentation process. The proposed deep learning models include a hybrid architecture combining Mask R-CNN with single layer CNN, 3-layer CNN, and8-layer CNN. These models utilizes depth wise convolution with varying dilation rates for efficiently extracting diversified features from the crack images. Further, performance evaluation shows that Mask R-CNN with a single layer CNN achieves an accuracy of 97.5% on a normal dataset and 97.8% on a segmented dataset. The Mask R-CNN with 2-layer convolution resulted in an accuracy of 98.32% on a normal dataset and 98.39% on a segmented dataset. The Mask R-CNN with 8-layers convolution achieves an accuracy of 98.4% on a normal dataset and 98.75% on a segmented dataset. The proposed Mask R-CNN have proved its feasibility in detecting cracks in huge building and structures.

Research on cooperation missions of multiple unmanned vehicles (UVs) is growing to overcome the limitations of conventional missions and to increase the variety of missions. In this study, a vision-based recognition and position measurement system was developed to cooperate with an unmanned aerial vehicle (UAV) in case an unmanned ground vehicle (UGV) is unable to drive due to terrain obstacles. Phase, mode, and requirements were derived to develop the vision system. After installing the developed vision system on UAV, several flight tests were conducted, which proved that the system satisfies all requirements. The vision system showed an average measurement error within 1 m for 80 measurements. It is expected that the vision-based system developed in this study can be applied for the cooperative mission of UAVs and UGVs.

Falls are one of the major health risks for the elderly in public health care issues, thus fall detection is particularly necessary for the seniors. In this paper, we propose a novel computer visio...

Stool examination is the gold standard for the detection of intestinal parasites. We assessed the performance of a newly developed AVE-562 analyzer (AVE Science & Technology Co., Hunan, China) for the vision-based detection of eggs of Clonorchis sinensis-the most common intestinal parasite in Korea-in stool samples. In total, 30 stool samples with a high or low egg count or without eggs (as negative control samples) (N=10 each) were prepared and analyzed. The performance of the AVE-562 analyzer was compared with that of the formalin-ether concentration (FEC) method. The overall correct identification rate of the AVE-562 analyzer based on FEC results was 66.6%. The sensitivity, specificity, positive predictive value, and negative predictive value of the AVE-562 analyzer for detecting C. sinensis eggs were 36.4%, 100.0%, 100.0%, and 73.1%, respectively. The average time required to run five tests simultaneously was 27 min using the AVE-562 analyzer and 58 min using the FEC method. Although the AVE-562 analyzer enables rapid and convenient stool examination, its sensitivity needs to be improved, particularly considering the prevalence of low-burden C. sinensis infection in Korea.