Experimental study of a combined biomass and solar energy-based fully grid-independent air-conditioning system

Clean Technologies and Environmental Policy - In this paper, a small-scale triple-hybrid air-conditioning system operated by biomass and solar energy resources is experimentally investigated....     

Mutual Insight on Ferroelectrics and Hybrid Halide Perovskites: A Platform for Future Multifunctional Energy Conversion

An insight into the analogies, state‐of‐the‐art technologies, concepts, and prospects under the umbrella of perovskite materials (both inorganic–organic hybrid halide perovskites and ferroelectric perovskites) for future multifunctional energy conversion and storage devices is provided. Often, these are considered entirely different branches of research; however, considering them simultaneously and holistically can provide several new opportunities. Recent advancements have highlighted the potential of hybrid perovskites for high‐efficiency solar cells. The intrinsic polar properties of these materials, including the potential for ferroelectricity, provide additional possibilities for simultaneously exploiting several energy conversion mechanisms such as the piezoelectric, pyroelectric, and thermoelectric effect and electrical energy storage. The presence of these phenomena can support the performance of perovskite solar cells. The energy conversion using these effects (piezo‐, pyro‐, and thermoelectric effect) can also be enhanced by a change in the light intensity. Thus, there lies a range of possibilities for tuning the structural, electronic, optical, and magnetic properties of perovskites to simultaneously harvest energy using more than one mechanism to realize an improved efficiency. This requires a basic understanding of concepts, mechanisms, corresponding material properties, and the underlying physics involved with these effects.     

Injectable Bone Cement Reinforced with Gold Nanodots Decorated rGO-Hydroxyapatite Nanocomposites,Augment Bone Regeneration

Interest in the development of new generation injectable bone cements having appropriate mechanical properties, biodegradability, and bioactivity has been rekindled with the advent of nanoscience. Injectable bone cements made with calcium sulfate (CS) are of significant interest, owing to its compatibility and optimal self-setting property. Its rapid resorption rate, lack of bioactivity, and poor mechanical strength serve as a deterrent for its wide application. Herein, a significantly improved CS-based injectable bone cement (modified calcium sulfate termed as CS_mod), reinforced with various concentrations (0–15%) of a conductive nanocomposite containing gold nanodots and nanohydroxyapatite decorated reduced graphene oxide (rGO) sheets (AuHp@rGO), and functionalized with vancomycin, is presented. The piezo-responsive cement exhibits favorable injectability and setting times, along with improved mechanical properties. The antimicrobial, osteoinductive, and osteoconductive properties of the CS_mod cement are confirmed using appropriate in vitro studies. There is an upregulation of the paracrine signaling mediated crosstalk between mesenchymal stem cells and human umbilical vein endothelial cells seeded on these cements. The ability of CS_mod to induce endothelial cell recruitment and augment bone regeneration is evidenced in relevant rat models. The results imply that the multipronged activity exhibited by the novel-CS_mod cement would be beneficial for bone repair.     

Underwater Image Dehazing via Unpaired Image-to-image Translation

Underwater imaging has long been focused on dehazing and color correction to address severe degradation in the water medium. In this paper, we propose a learning-based image restoration method that uses Generative Adversarial Networks (GAN). For network generality and learning flexibility, we constituted unpaired image translation frameworks into image restoration. The proposed method utilizes multiple cyclic consistency losses that capture image characteristics and details of underwater images. To prepare unpaired images of clean and degraded scenes, we collected images from Flickr and filtered out false images using image characteristics. For validation, we extensively evaluated the proposed network on simulated and real underwater hazy images. Also, we tested our method on conventional computer vision algorithms, such as the level of edges and feature matching results.

     

High Diversity Gain MIMO-Antenna for UWB Application with WLAN Notch Band Characteristic Including Human Interface Devices

Wireless Personal Communications - In this paper, a UWB–MIMO antenna with the WLAN band-notch (5.1–5.85 GHz) characteristic is offered. This antenna consists of two radiated...     

Blind Signal Modulation Recognition through Density Spread of Constellation Signature

Wireless Personal Communications - Cluster analysis is a commonly used technique by investors to create a diversified portfolio. The approach aims at maximizing returns for a tolerable degree of...     

A Novel Hybrid PSO Assisted Optimization for Classification of Intellectual Disability Using Speech Signal

Wireless Personal Communications - Speech signals convey speaker’s neurodevelopmental state along with phonological information. Recognize a speech disorder by analyzing the speech is...     

Stability Analysis of Epidemic Modeling Based on Spatial Correlation for Wireless Sensor Networks

Wireless Personal Communications - In wireless sensor networks (WSNs), the main task of each sensor node is to sense the physical activity and then report it to a remote central monitoring sink...     

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.