Abstract:The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

A power grid is a network that carries electrical energy from power plants to customer premises. One existing power grid is going through a massive and revolutionary transformation process. It is envisioned to achieve the true meaning of technology as “technology for all.” Smart grid technology is an inventive and futuristic approach for improvement in existing power grids. Amalgamation of existing electrical infrastructure with information and communication network is an inevitable requirement of smart grid deployment and operation. The key characteristics of smart grid technology are full duplex communication, advanced metering infrastructure, integration of renewable and alternative energy resources, distribution automation and absolute monitoring, and control of the entire power grid. Smart grid communication infrastructure consists of heterogeneous and hierarchical communication networks. Various layers of smart grid deployment involve diverse sets of wired and wireless communication standards. Application of smart grids can be realized in the facets of energy utilization. Smart grid communication architecture can be used to explore intelligent agriculture applications for the proficient nurturing of various crops. The utilization, monitoring, and control of various renewable energy resources are the most prominent features of smart grid infrastructure for agriculture applications. This paper describes an implementation of an IoT-based wireless energy management system and the monitoring of weather parameters using a smart grid communication infrastructure. A graphical user interface and dedicated website was developed for real-time execution of the developed prototype. The prototype described in this paper covers a pervasive communication infrastructure for field area networks. The design was validated by testing the developed prototype. For practical implementation of the monitoring of the field area network, multiple sensors units were placed for data collection for better accuracy and the avoidance of estimation error. The developed design uses one sensor and tested it for IoT applications. The prototype was validated for local and wide area networks. Most of the present literature depicts a design of various systems using protocols such as IEEE 802.15.1 and IEEE 802.15.4, which either provide restricted access in terms of area or have lower data rates. The protocols used in developed system such as IEEE 802.11 and IEEE 802.3 provide ubiquitous coverage as well as high data rates. These are well-established and proven protocols for Internet applications and data communication but less explored for smart grid applications. The work depicted in this paper provides a solution for all three smart grid hierarchical networks such as home/field area networks, neighborhood area networks, and wide area networks using prototype development and testing. It lays a foundation for actual network design and implementation. The designed system can be extended for multiple sensor nodes for practical implementation in field area networks for better accuracy and in the case of node failure.

Building Information Modelling (BIM) is an object-oriented approach that can virtually create a 3D digital model of a building capable of doing realistic engineering analysis well before the construction begins. BIM offers sophisticated tools for electrical trade design and estimation. This paper primarily investigates with supportive case studies, the potential applications of BIM in electrical system design and analysis. Further, its energy analysis, geographical scale significance and smart built environmental applications are also investigated. This research emphasizes on the deployment of BIM at both static and dynamic level for electrical trade such that BIM electrical system design can be made more attractive and more cost effective. The preliminary investigations indicate that seamless integration of semantic information system of BIM with Geographical Information System (GIS) can be very useful for electrical grid optimization and city energy modelling.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

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