VLM Precoded SLM Technique for PAPR Reduction in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is perhaps the most spectrally efficient, robust transmission technique discovered so far for communication systems, and it also mitigates the problem of multipath environment. High peak-to-average power ratio (PAPR) has always been a major drawback of the OFDM systems. In this article, a new precoding technique has been proposed based on Vandermonde-like matrix (VLM) and selective mapping (SLM) to reduce PAPR in OFDM systems. VLM precoding reduces the autocorrelation of the input sequences while SLM takes an advantage of the fact that the PAPR is very sensitive to phase shifts of the signal. The main advantage of this proposed scheme is to achieve a significant reduction in PAPR without increasing the system complexity. Computer simulations show that, the proposed method outperforms the existing precoding techniques without degrading the error performance of the system.     

CCH: a clique based asymmetric rendezvous scheme for cognitive radio ad-hoc networks

Wireless Networks - Rendezvous (RDV) in cognitive radio ad-hoc networks (CRAHNs) is one of the key ways for a pair of unknown cognitive radio users to initiate communications. CRAHN is a...     

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.     

Design and Analysis of a Fractal Antenna Using Jeans Material for WiMax/WSN Applications

This paper presents a novel approach in designing fractal antennas operating at 2.45 GHz. Mainly focuses on design and simulation of a combination of Koch and Sierpinski fractal antenna with jeans as substrate material with dielectric constant 1.6 up to 2nd iteration. The design of antenna presented here can radiate at 2.45 GHZ, 3.64 GHZ, and 4.06 GHz frequencies with a reflection coefficient less than  ? 10 dB, VSWR between 1 and 2 and enhancement in bandwidth up to 200 MHz. This antenna has been simulated using CADFEKO software, fabricated and tested on VNA and has been observed that there is good conformity between simulation and experimental results. Besides that, Fractal antenna using jeans material as substrate for WiMAX/WSN Application at S and C frequency bands is small in size, flexible, less costly and low profile multiband antenna.

     

Four-wave mixing reduction technique based on smart filter approach

This paper proposed a new technique to suppress the four-wave mixing (FWM) effect by using a smart filter technique. The behaviour of FWM and the performance of wavelength division multiplexing systems with 4 and 16 channels were simulated in the presence of the proposed technique. The simulation was also performed under different parameters such as input power, number of channels and channel spacing. The FWM power drastically decreases by 12 and 19 dB for the 4 and 16 channels, respectively, when the smart filter is used as compared with the conventional system. In terms of system performance, the suggested approach for 4 and 16 channels at the first channel offers low bit error rate (BER) values of 3.23 × 10^?23 and 1.7 × 10^?21, respectively. The smart filter with the channel spacing variation for the 4-channel system subsequently improved the BER value at the fourth channel. Results confirm that the smart filter approach is an active solution that can suppress the FWM effect in optical transmission systems.     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

Fabrication of porous ZnO via electrochemical etching using 10 wt% potassium hydroxide solution

We described the fabrication of porous ZnO using the electrochemical etching method. ZnO thin films deposited by radiofrequency sputtering were etched electrochemically using 10 wt% KOH solution as an etching medium to obtain porous ZnO surface structure. A constant voltage of 15 V was applied to enhance the etching process. The etched samples were then characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy to examine their structural and optical properties. XRD spectra showed that by performing the electrochemical etching process, porous ZnO could be obtained without severely deteriorating the crystallinity of the samples. Moreover, SEM characterization revealed that hillock-type porous ZnO was fabricated successfully. In addition, the cross-sectional SEM images revealed that there were only minimal changes in the layer thickness after the ZnO had been etched for various lengths of time. This finding shows the dominance of the vertical etching process. Notably, the intensity of PL spectra increased and the PL excitation peak exhibited a red shift trend as the etching time increased. These observations are due to the increase of the surface to volume ratio of the ZnO surface and the strain relaxation along the dislocation and grain boundary.     

Exploiting cooperative diversity with non-orthogonal multiple access over slow fading channel

Non-orthogonal multiple access (NOMA) has attracted a significant attention to the research community as a potential candidate for 5G or future radio access. This article presents a NOMA-based cooperative network where a transmitter considered as a base station communicates simultaneously with two users treating as a far user and a near user via the help of a half-duplex decode-and-forward relay. We investigate the outage probability and the outage capacity of the proposed network over independent Rayleigh slow fading channels. Closed-form expressions of the outage probabilities are derived for both users. Approximate outage capacity of the users are also investigated at high signal to noise ratio regime. It has been shown that the proposed cooperative NOMA can achieve superior performance compared to the non-cooperative NOMA in terms of outage probability. The tightness between the simulation and theoretical results confirms the efficiency of the proposed protocol.     

Efficient Computation Offloading Decision in Mobile Cloud Computing over 5G Network

Due to the significant advancement of Smartphone technology, the applications targeted for these devices are getting more and more complex and demanding of high power and resources. Mobile cloud computing (MCC) allows the Smart phones to perform these highly demanding tasks with the help of powerful cloud servers. However, to decide whether a given part of an application is cost-effective to execute in local mobile device or in the cloud server is a difficult problem in MCC. It is due to the trade-off between saving energy consumption while maintaining the strict latency requirements of applications. Currently, 5th generation mobile network (5G) is getting much attention, which can support increased network capacity, high data rate and low latency and can pave the way for solving the computation offloading problem in MCC. In this paper, we design an intelligent computation offloading system that takes tradeoff decisions for code offloading from a mobile device to cloud server over the 5G network. We develop a metric for tradeoff decision making that can maximize energy saving while maintain strict latency requirements of user applications in the 5G system. We evaluate the performances of the proposed system in a test-bed implementation, and the results show that it outperforms the state-of-the-art methods in terms of accuracy, computation and energy saving.     

Uniform Virus‐Like Co–N–Cs Electrocatalyst Derived from Prussian Blue Analog for Stretchable Fiber‐Shaped Zn–Air Batteries

Zn‐air batteries (ZABs) offer promising commercialization perspectives for stretchable and wearable electronic devices as they are environment‐friendly and have high theoretical energy density. However, current devices suffer from limited energy efficiency and durability because of the sluggish oxygen reduction and evolution reactions kinetics in the air cathode as well as degenerative stretchability of solid‐state electrolytes under highly alkaline conditions. Herein, excellent bifunctional catalytic activity and cycling stability is achieved by using a newly developed Co–N–C nanomaterial with a uniform virus‐like structure, prepared via a facile carbonization of a prussian blue analogue (PBA). Furthermore, a solid‐state dual‐network sodium polyacrylate and cellulose (PANa‐cellulose) based hydrogel electrolyte is synthesized with good alkaline‐tolerant stretchability. A solid‐state fiber‐shaped ZAB fabricated using this hydrogel electrolyte, the virus‐like Co–N–Cs air cathode, and a zinc spring anode display excellent stretchability of up to 500% strain without damage, and outstanding electrochemical performance with 128 mW cm^?2 peak power density and good cycling stability for >600 cycles at 2 mA. The facile synthesis strategy demonstrated here opens up a new avenue for developing highly active PBA‐derived catalyst and shows, for the first time, that virus‐like structure can be favorable for electrochemical performance.