Not finding a parking space for you sometimes is indeed a critical issue. The number of vehicles is also increasing daily adding to the parking vows at public places. Cities noticed that their drivers had real problems to find a parking space easily especially during peak hours, the difficulty roots from not knowing where the parking spaces are available at the given time. Even if this is known, many vehicles may pursue a small number of parking spaces which in turn leads to traffic congestion. The traffic on roads and parking space has been an area of concern in majority of cities. So, parking monitoring is an important solution. To avoid these problems, recently many new technologies have been developed that help in solving the parking problems to a great extent. Firstly, this paper gives an overview about the concept of smart parking system, their categories and different functionalities. Then we present the latest developments in parking infrastructures. We describe the technologies around parking availability monitoring, parking reservation and dynamic pricing and see how they are utilized in different settings. In addition, a theoretical comparison is presented to show advantages and drawbacks of each different smart parking system to discuss results and open directions for future research.

Smart parking systems: A survey

Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studies.

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Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

Human emotional states can be detected by various ways such as analysingthe brain signals (EEG), speech, skin responses, facial expressions, body gestures etc. But facial expressions recognition is one of the popular techniquesand easy to detect with computers using various computer visions techniques. In this work, we have proposed a new approach to detect basic facial expressions from textural images using deep learning model Convolution neural network (CNN). In the proposed approach textural images of human faces are generated using Local binary pattern(LBP) method and trained the CNN model with the LBPimages. In the proposed approach all the images of extended Cohn-Kanade(CK+)dataset are converted into LBP images that are used to train the CNN model. The performance of proposed facial expression recognition system has been validated on modifiedCK+dataset. Facial emotionRecognition Accuracy of conventional CNN model with CK+ dataset and proposed method on modified CK+ dataset is also compared.

Facial emotion recognition based on Textural pattern and Convolutional Neural Network

Concrete is the widely used composite material to build infrastructures that should remain durable and serviceable. This study focuses on sustainable concrete produced by replacing cement with waste products (fly ash and slag) to obtain good mechanical long-term and desirable durability. The use of fly ash and slag in concrete in place of cement lowers global energy demand and saves money on the verge of depletion. Fly ash provides increased workability, mechanical and durability properties and a wide range of advantages, including decreased strain on natural resources and a lower CO2 footprint. Fly ash in concrete structures is used as a partial substitute for Ordinary Portland Cement. Experimental work about concrete contributes to a waste of resources, time and money. Nominal concrete is a heterogeneous mixture of cement, water, coarse and fine aggregates. Therefore, for efficient use of such materials in various engineered structures, a clear understanding of such complex behavior is important. In this paper, the ANN is used to execute and provide output for compressive strength and workability of concrete, which we are predicting by use of 103 datasets obtained from available technical literatures. The focus is to find out the ideal equation for workability and compressive strength for concrete with the help of ANN.

Development of Prediction Models to Determine Compressive Strength and Workability of Sustainable Concrete with ANN

Research and improvement of ECG compression algorithm based on EZW

The objective of this paper is to evaluate a set of wavelets for image compression. Image compression using wavelet transforms results in an improved compression ratio. Wavelet transformation is the technique that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients amongst the wavelet coefficients. DWT (Discrete Wavelet Transform) represents image as a sum of wavelet function (wavelets) on different resolution levels. So, the basis of wavelet transform can be composed of function that satisfies requirements of multiresolution analysis. The choice of wavelet function for image compression depends on the image application and the content of image. A review of the fundamentals of image compression based on wavelet is given here. This study also discussed important features of wavelet transform in compression of images. In this study we have evaluated and compared three different wavelet families i.e. Daubechies, Coiflets, Biorthogonal. Image quality is measured, objectively using peak signal-to-noise ratio, Compression Ratio and subjectively using visual image quality.

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JPEG Image Compression based on Biorthogonal, Coiflets and Daubechies Wavelet Families

The rapid urbanization and industrialization of the population have resulted in a critical shortage of agricultural land and manpower to meet the growing population’s needs. The future of tomorrow requires impetus through concepts such as IoT Automation in farmlands to meet the increased demand for labour and inspection while maintaining economic efficiency without depleting the environment. Monitoring becomes difficult with a reduced manpower base, which may result in crop failures. The purpose of this paper is to propose how IoT, in conjunction with basic service automation, can be used to influence climate control. Thus, a working model is created using sensors that control the humidity and required temperature conditions, thereby maintaining a suitable growth environment while monitoring and analyzing in real time.

https://doi.org/10.1088/1757-899x/1218/1/012048

The Automated Farmlands of Tomorrow: An IoT Integration with Farmlands

This study aims to simulate hydrological processes at farm and basin scale in the Manjira sub-basin of the Godavari river basin in India. In addition, the study also aims to simulate crop yields in the region at three scales: the scale of the sub-basin, the scale of a watershed and the scale of a farm within the sub-basin. The simulations have been performed using SWAT and APEX model. The study focuses on farm advisory services to be provided at farm level to the farmers based on agro-hydrological model simulation results. This study pave the way for farmers, researchers and advisors to work in a collaborative manner to enhance the farm produce and manage the farm resources in optimum way.

Farm Advisory Services for Farmers Using SWAT and APEX Model

Blockchain technology serves a breakthrough for many solutions like that of poor payment facilities, lesser productivity, trust among different levels of the industry in sharing of the gathered field reports, or the commands of the higher authority in the construction industry. Thus, the decentralized ledger system has facilitated an automatic chain of the flow of data or information among the peers and the subordinates. The aim is to make a digital world more trustable giving a new open arena to a better security solution, resilient nature and the enhancement of the systems. It is already known that industry progress upon its embracing of newer technologies that bind a better relationship with the customers and the giving of the best quality project basing on the low cost of trade estimates; the blockchain facilitates the use of smart contracts, agreements with the party directly without the involvement of a third party. The new version of the industries is supposedly aiming towards more digitized industrialization with more involvement in construction management with building information modelling (BIM). The aim is to provide a pre-step towards the use cases of blockchain technology in the construction industry after the reviving of the industry using the utmost digitization.

Priyanka Singh

Papers

JPEG Image Compression based on Biorthogonal, Coiflets and Daubechies Wavelet Families

Development of Prediction Models to Determine Compressive Strength and Workability of Sustainable Concrete with ANN

The Automated Farmlands of Tomorrow: An IoT Integration with Farmlands

Farm Advisory Services for Farmers Using SWAT and APEX Model

Combating Challenges in the Construction Industry with Blockchain Technology