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The work described in this paper focuses on the utilisation of silicon nanowires as the information storage element in flash-type memory devices. Silicon nanostructures have attracted attention due to interesting electrical and optical properties, and their potential integration into electronic devices. A detailed investigation of the suitability of silicon nanowires as the charge storage medium in two-terminal non-volatile memory devices are presented in this report. The deposition of the silicon nanostructures was carried out at low temperatures (less than 400 °C) using a previously developed a novel method within our research group. Two-terminal non-volatile (2TNV) memory devices and metal-insulator-semiconductor (MIS) structures containing the silicon nanowires were fabricated and an in-depth study of their characteristics was carried out using current-voltage and capacitance techniques.

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Intensive research is currently underway to exploit the highly interesting properties of nano-bits ("nano-sized particles and molecules") for optical, electronic and other applications. The basis of these unique properties is the small-size of these structures which result in quantum mechanical phenomena and interesting surface properties. The small molecules and/or nanoparticles are selected in such a way so that it can create an internal electric in the nanocomposite. We define a nanocomposite is an admixture of small molecules and/or nano-particles and a polymer. We have demonstrated the internal electric field in our devices, made from nanobits (nano-particles and/or molecules) and insulating materials, can contribute to the electrical bistability i.e. two conductive states.

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a Deep Attentive Multimodal Graph Embedding model named DAMGE for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.

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In this work, a new ultra-wideband (UWB) antenna design with 2.08GHz to 12GHz impedance bandwidth and triple-band specifications is presented. The proposed antenna is formed by a truncated square patch, a partial ground plane, and a 50Ω microstrip line. Three different types of slots were used in order to induce notched bands. A C-shaped slot is etched on the radiating patch to obtain a notched band in 3.31-4.21GHz for WiMAX. An inverted U-shaped slot in the micro-strip line induces a second notched band in order to reduce the interference with the WLAN [5.04-6.81GHz]. Finally, two inverted L-shaped slots around the micro-ribbon line on the ground plane allow the X-band [9.13 to 10.75GHz]. The antenna has dimensions of 32×28×1.6mm3. The Ansoft software (HFSS) was used to simulate the proposed structure. The simulation results are in good agreement with the measurement results. The antenna shows an omnidirectional radiation pattern.

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