Human action recognition employing negative space features

We proposed a region based method to recognize human actions from video sequences. Unlike other region based methods, it works with the surrounding regions of the human silhouette termed as negative space. This paper further extends the idea of negative space to cope with the changes in viewpoints. It also addresses the problem of long shadows which is one of the major challenges of human action recognition. Some systems attempt suppressing shadows during the segmentation process but our system takes input of segmented binary images of which the shadow is not suppressed. This makes our system less dependent on segmentation process. Further, this approach can complement the positive space (silhouette) based methods to boost recognition. The system consists of a hierarchical processing: histogram analysis on segmented input image, followed by motion and shape feature extraction, pose sequence analysis by employing Dynamic Time Warping and at last classification by Nearest Neighbor classifier. We evaluated our system by most commonly used datasets and achieved higher accuracy than the state of the arts methods. Our system can also retrieve video sequences from queries of human action sequences.     

Spectroscopic studies of sol–gel grown CdS nanocrystalline thin films for optoelectronic devices

CdS is one of the highly photosensitive candidate of II–VI group semiconductor material. Therefore CdS has variety of applications in optoelectronic devices. In this paper, we have fabricated CdS nanocrystalline thin film on ultrasonically cleaned glass substrates using the sol–gel spin coating method. The structural and surface morphologies of the CdS thin film were investigated by X-ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM) respectively. The surface morphology of thin films showed that the well covered substrate is without cracks, voids and hole. The round shape particle has been observed in SEM micrographs. The particles sizes of CdS nanocrystals from SEM were estimated to be～10–12 nm. Spectroscopic properties of thin films were investigated using the UV–vis spectroscopy, Photoluminescence and Raman spectroscopy. The optical band gap of the CdS thin film was estimated by UV–vis spectroscopy. The average transmittance of CdS thin film in the visible region of solar spectrum found to be～85%. Optical band gap of CdS thin film was calculated from transmittance spectrum ～2.71 eV which is higher than bulk CdS (2.40 eV) material. This confirms the blue shifting in band edge of CdS nanocrystalline thin films. PL spectrum of thin films showed that the fundamental band edge emission peak centred at 459 nm also recall as green band emission.     

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.     

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...     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

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.     

Multicast Network Security with Asymmetric Cooperative Relaying

International Journal of Wireless Information Networks - This paper exhibits the confidentiality performance study of a cooperative multicast network consisting of $${\mathcal {K}}$$ asymmetric...     

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.     

Deterministic Polymorphic Engineering of MoTe2 for Photonic and Optoelectronic Applications

Developing selective and coherent polymorphic crystals at the nanoscale offers a novel strategy for designing integrated architectures for photonic and optoelectronic applications such as metasurfaces, optical gratings, photodetectors, and image sensors. Here, a direct optical writing approach is demonstrated to deterministically create polymorphic 2D materials by locally inducing metallic 1T′-MoTe₂ on the semiconducting 2H-MoTe₂ host layer. In the polymorphic-engineered MoTe₂, 2H- and 1T′- crystalline phases exhibit strong optical contrast from near-infrared to telecom-band ranges (1–1.5 µm), due to the change in the band structure and increase in surface roughness. Sevenfold enhancement of third harmonic generation intensity is realized with conversion efficiency (susceptibility) of ≈1.7 × 10⁻⁷ (1.1 × 10⁻¹⁹ m² V⁻²) and ≈1.7 × 10⁻⁸ (0.3 × 10⁻¹⁹ m² V⁻²) for 1T′ and 2H-MoTe₂, respectively at telecom-band ultrafast pump laser. Lastly, based on polymorphic engineering on MoTe₂, a Schottky photodiode with a high photoresponsivity of 90 AW⁻¹ is demonstrated. This study proposes facile polymorphic engineered structures that will greatly benefit realizing integrated photonics and optoelectronic circuits.