PROACTIVE CAR PARKING SYSTEM USING IOT

Every City in India as well as other countries are moving towards the concept of Smart Cities. Increase in population is directly proportional to the increase in traffic and almost all cities are densely populated. This paper provides a novel approach of identifying the vehicles and appropriately redirecting them to parking slots This Proactive Car Parking System framework encompasses IOT module that is applied to screen and signalize the condition of accessibility of single parking spot. In addition to this it signals in case of empty slot and hence the driver of the vehicle need not waste time in searching the slot. The status on availability of parking is informed in advance and this in turn helps to avoid traffic. The IR sensor with Arduino is used for identifying slots. The proposed system is an embedded system where internet, Sensors, detection of objects and algorithms are used.

A New Methodology for the Detection and Extraction of Hyperbolas in GPR Images

Reliable detection of underground infrastructure is essential for infrastructure modernization works, the implementation of BIM technology, and 3D cadasters. This requires shortening the time of data interpretation and the automation of the stage of selecting the objects. The main factor that influences the quality of radargrams is noise. The paper presents the method of data filtration with use of wavelet analyses and Gabor filtration. The authors were inspired to conduct the research by the fact that the interpretation and analysis of radargrams is time-consuming and by the wish to improve the accuracy of selection of the true objects by inexperienced operators. The authors proposed automated methods for the detection and classification of hyperboles in GPR images, which include the data filtration, detection, and classification of objects. The proposed object classification methodology based on the analytic hierarchy process method introduces a classification coefficient that takes into account the weights of the proposed conditions and weights of the coefficients. The effectiveness and quality of detection and classification of objects in radargrams were assessed. The proposed methods make it possible to shorten the time of the detection of objects. The developed hyperbola classification coefficients show promising results of the detection and classification of objects.

Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection

Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.

False biosignatures on Mars: anticipating ambiguity

It is often acknowledged that the search for life on Mars might produce false positive results, particularly via the detection of objects, patterns or substances that resemble the products of life in some way but are not biogenic. The success of major current and forthcoming rover missions now calls for significant efforts to mitigate this risk. Here, we review known processes that could have generated false biosignatures on early Mars. These examples are known largely from serendipitous discoveries rather than systematic research and remain poorly understood; they probably represent only a small subset of relevant phenomena. These phenomena tend to be driven by kinetic processes far from thermodynamic equilibrium, often in the presence of liquid water and organic matter, conditions similar to those that can actually give rise to, and support, life. We propose that strategies for assessing candidate biosignatures on Mars could be improved by new knowledge on the physics and chemistry of abiotic self-organization in geological systems. We conclude by calling for new interdisciplinary research to determine how false biosignatures may arise, focusing on geological materials, conditions and spatiotemporal scales relevant to the detection of life on Mars, as well as the early Earth and other planetary bodies.Thematic collection: This article is part of the Astrobiology: Perspectives from the Geology of Earth and the Solar System collection available at: https://www.lyellcollection.org/cc/astrobiology-perspectives-from-geology-of-earth-and-solar-system

Hybrid Approach for Detection of Objects from Images Using Fisher Vector and PSO Based CNN

Owing to the near connection between object recognition and video processing and picture perception, a lot of research interest has been received in recent years. Standard methods of object detection are focused on manufactured technologies and slow-moving architectures. Fisher Vectors (FV) and Convolutional Neural Networks (CNN) are two picture arrangement pipelines with various qualities. While CNNs have indicated predominant exactness on various order assignments, FV classifiers are normally less exorbitant to prepare and assess. In this paper we propose a mechanism for detection of objects in image based on Fisher kernel and CNN with a PSO optimization technique. Here fisher kernel draws the global or statically features from the image object and CNN is used for local and more complex feature extraction from an image and here we use CNN with PSO to reduce the training complexity. Performance results shows that the proposed model is detect the object better than the existing models.

Real-Time Detection of Linear Structure Objects Using Mean Shift Segmentation and Heuristic Search

With the development of intelligent industrial production, industrial components with linear structure tend to be regular, such as TV LCD module, mobile phone screen, and electronic equipment shell. Recognition of linear structure objects by machine vision is an important aspect of intelligent industry. At present, shape matching algorithm is mostly used for arbitrary structure objects. It will be time-consuming if it is directly used to detect the linear structure objects as it needs to traverse the parameter space of the object. To solve the traversal problem and detect the linear structure objects in real time, a heuristic detection algorithm is designed according to the characteristics of linear structure objects. First, the coarse position and orientation are obtained by mean shift filtering and heuristic region grouping to reduce the searching range. Then, the heuristic search method is used to get the precise location information. The heuristic search method is designed based on the particle swarm optimization algorithm and heuristic information. The proposed method has been evaluated on two image databases of common industrial parts and backlight units which are typical linear structure objects. The experimental results showed that the proposed algorithm could reduce the detect time by more than 70% averagely while the detection accuracy is kept. It proves that the proposed algorithm can detect linear structure objects in real time and is suitable for the detection of objects with linear structures.

Performance analysis of machine learning libraries

The paper presents results of performance analysis of machine learning libraries. The research was based on ML.NET and TensorFlow tools. The analysis was based on a comparison of running time of the libraries, during detection of objects on sets of images, using hardware with different parameters. The library, consuming fewer hardware resources, turned out to be TensorFlow. The choice of hardware platform and the possibility of using graphic cores, affecting the increase in computational efficiency, turned out to be not without significance.

From ERL to MBZIRC: Development of An Aerial-Ground Robotic Team for Search and Rescue

This chapter describes the efforts of the LARICS team in the 2019 European Robotics League (ERL) Emergency Robots and the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC) robotics competitions. We focus on the implementation of hardware and software modules that enable the deployment of aerial-ground robotic teams in unstructured environments for joint missions. In addition to the overall system specification, we outline the main algorithms for operation in such conditions: autonomous exploration of unknown environments and detection of objects of interest. Analysis of the results shows the success of the developed system in the competition arena of two of the largest outdoor robotics challenges. Throughout the chapter, we highlight the evolution of the robotic system based on the experience gained in the ERL competition. We conclude the chapter with key findings and additional improvement ideas to advance the state of the art in search and rescue applications of heterogeneous robotic teams.