Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Deep learning controller for nonlinear system based on Lyapunov stability criterion

Neural Computing and Applications - For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC...     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

Thermal modeling in cylindrical coordinates using effective conductivity

Predictive thermometry, utilizing minimally invasive sampling techniques, is an essential ingredient in the development of hyperthermia treatment planning capabilities. The authors demonstrate a powerful, but simple approach toward predicting temperature distributions in tissues, based on analytic solution, using in cylindrical symmetry, of the heat diffusion equation. Conduction and localized perfusion effects are combined as an effective conductivity term, readily measurable, and parametrized in a general exponential form. The proposed approach allows a first-order approximation to modeling three typical situations: hypoxic or necrotic tumor core with homogeneously perfused periphery; highly perfused periphery (in rapidly growing tumors); or perfused central cover with a less well-supplied periphery (such as for some invasive tumors). The utility and strength of this approach is that it provides a rapid, accurate model of directly observing the technical quality to be expected for different heating methods, making it possible to optimally configure source distributions in a treatment planning setting.     

Current Understandings on Magnesium Deficiency and Future Outlooks for Sustainable Agriculture

The productivity of agricultural produce is fairly dependent on the availability of nutrients and efficient use. Magnesium (Mg²⁺) is an essential macronutrient of living cells and is the second most prevalent free divalent cation in plants. Mg²⁺ plays a role in several physiological processes that support plant growth and development. However, it has been largely forgotten in fertilization management strategies to increase crop production, which leads to severe reductions in plant growth and yield. In this review, we discuss how the Mg²⁺ shortage induces several responses in plants at different levels: morphological, physiological, biochemical and molecular. Additionally, the Mg²⁺ uptake and transport mechanisms in different cellular organelles and the role of Mg²⁺ transporters in regulating Mg²⁺ homeostasis are also discussed. Overall, in this review, we critically summarize the available information about the responses of Mg deficiency on plant growth and development, which would facilitate plant scientists to create Mg²⁺-deficiency-resilient crops through agronomic and genetic biofortification.     

A panoramic view of Li7P3S11 solid electrolytes synthesis,structural aspects and practical challenges for all-solid-state lithium batteries

The development of an inorganic electrochemical stable solid-state electrolyte is essentially responsible for future state-of-the-art all-solid-state lithium batteries (ASSLBs). Because of their advantages in safety, working temperature, high energy density, and packaging, ASSLBs can develop an ideal energy storage system for modern electric vehicles (EVs). A solid electrolyte (SE) model must have an economical synthesis approach, exhibit electrochemical and chemical stability, high ionic conductivity, and low interfacial resistance. Owing to its highest conductivity of 17 mS·cm^-1, and deformability, the sulfide-based Li₇P₃S₁₁ solid electrolyte is a promising contender for the high-performance bulk type of ASSLBs. Herein, we present a current glimpse of the progress of synthetic procedures, structural aspects, and ionic conductivity improvement strategies. Structural elucidation and mechanistic approaches have been extensively discussed by using various characterization techniques. The chemical stability of Li₇P₃S₁₁ could be enhanced via oxide doping, and hard and soft acid/base (HSAB) concepts are also discussed. The issues to be undertaken for designing the ideal solid electrolytes, interfacial challenges, and high energy density have been discoursed. This review aims to provide a bird's eye view of the recent development of Li₇P₃S₁₁-based solid-state electrolyte applications and explore the strategies for designing new solid electrolytes with a target-oriented approach to enhance the efficiency of high energy density all-solid-state lithium batteries.