Wireless Personal Communications

Plants have elemental importance for all life forms. The research areas in the field of plant sciences for botanists and agriculturists include the identification of plant species, classification of weeds from crops, detection of various diseases that hamper the growth of plant, and monitoring the growth and its semantic interpretation. Trained botanists can easily identify plant species based on the leaf shape, texture, structure or arrangement of leaves, however, the recent trend in smart agriculture demands the use of intelligent systems for the same task. Last decade has seen an enormous rise in the use of deep learning in the field of automatic plant species recognition based on the leaf images. In this work, we have surveyed various state-of-the-art deep learning techniques (Convolutional Neural Networks, Mask RCNN, Recurrent Neural Networks, Generative Adversarial Networks) that have been applied in the field of leaf image segmentation (separation of leaf from the whole image) and classification of leaves into various species. This contribution will help the new researchers in the field to get a foundation on the trends being employed in deep learning for generation of synthetic leaf images, segmentation and classification of leaves into various species. Various difficulties and future scope have also been presented.

As an important approach to resist the threat of key leakage, key insulated security allows secret keys to be periodically updated by using a physically-secure but computation-limited device. Recently, key insulated mechanism has been introduced into identity based (ID-based) signature to solve the key-leakage problem in ID-based signature scenarios. In this paper, we present two compact ID-based key-insulated signature schemes that try to minimize the total amount of message and signature. Compared with the up-to-date ID-based key-insulated signatures, our schemes enjoy the minimum net bandwidth and computation overhead. We also provide formal security proofs of our schemes under the Computational Diffie–Hellman assumption in the random oracle model. We focus on potential applications of our schemes to secret handshakes, but we believe they will find many other applications as well.

In this paper, we presented an automated system for identification and classification of fish species. It helps the marine biologists to have greater understanding of the fish species and their habitats. The proposed model is based on deep convolutional neural networks. It uses a reduced version of AlexNet model comprises of four convolutional layers and two fully connected layers. A comparison is presented against the other deep learning models such as AlexNet and VGGNet. The four parameters are considered that is number of convolutional layers and number of fully-connected layers, number of iterations to achieve 100% accuracy on training data, batch size and dropout layer. The results show that the proposed and modified AlexNet model with less number of layers has achieved the testing accuracy of 90.48% while the original AlexNet model achieved 86.65% over the untrained benchmark fish dataset. The inclusion of dropout layer has enhanced the overall performance of our proposed model. It contain less training images, less memory and it is also less computational complex.

The Planar microwave antennas with simultaneously selectively narrowband and wideband resonance characteristics are essential for the diversity applications. The paper presents modeling, fabrication and experimental verification of a coplanar waveguide excited antenna with a complementary patch which exhibits a narrow-band resonance at 2.47 GHz, and a wideband resonance between 5.52 and 11 GHz. A reconfigurability in the antenna design was achieved by placing a PIN diode across the slot position, and the wideband resonance was switched to the narrowband resonance with a center frequency of 9.56 GHz. The device was fabricated on a foam-based substrate of relative permittivity ε r  = 2.2 and loss factor tanδ = 0.007. The tested antenna shows a comparable matching with the simulated results and a superior performance over the other reported reconfigurable CPW antennas was achieved.

Grid computing has recently become one of the most important research topics in the field of computing. The Grid computing paradigm has gained popularity due to its capability to offer easier access to geographically distributed resources operating across multiple administrative domains. The grid environment is considered as a combination of dynamic, heterogeneous and shared resources in order to provide faster and reliable access to the Grid resources, the resource overloading must be prevented which can be obtained by proper load balancing and job migration mechanisms. This paper presents an extensive survey of the existing load balancing and job migration techniques proposed so far. A detailed classification has also been included based on different parameters which are depending on the analysis of the existing techniques, a new Load balancing technique, along with Job Migration approach has been proposed and discussed to fulfill the existing research gaps.

Traditional wireless ad hoc network power-efficient design proceeds separately on access and clustering algorithms by assuming perfect distance (that is, no fading and channel impairments) at most. In this paper, we discard the traditional layer-concept to tackle this important power-efficient wireless ad hoc networks under shadow fading, by identifying distance between a node pair as a sort of random distance to accommodate fading effect, which of course can be considered as a cross-layer design from traditional concept. By deriving the probability distribution of the distance between two nodes and the probability distribution of the distances between nodes and a randomly selected common reference node, the impacts of shadow fading on the link connectivity and node degree of the randomly constructed network topology are studied. Next, we propose a critical node first (CNF) based clustering algorithm to organize such a shadow faded random network topology into a power-efficient network architecture. By taking the shadow fading effects into considerations, our results show that the cluster-based network architecture generated by the proposed CNF-based clustering algorithm is power-efficient since the required number of exchanges of the cluster maintenance overheads is reduced.

Compressive sensing (CS) has attracted much attention in wireless communications due to its ability to attain acceptable channel estimates with a small number of pilots. To further reduce the pilot overhead in multi-input multi-output (MIMO) systems, CS-based channel estimation may employ superimposed pilot pattern. Previous works on superimposed pilot design generally allocate pilots randomly, which may give ill-posed measurement matrices. In this paper, we focus on deterministic pilot allocation for large-scale MIMO systems with superimposed pilot pattern to improve the performance of structured CS based channel estimation. By exploiting the spatial common sparsity and the error bound of block sparse reconstruction, a new criterion is firstly proposed to optimize the pilots in the Hadamard space. The proposed criterion makes full use of the information about the principal angles across the blocks in the measurement matrix, which can enhance the average recovery ability and exclude the worst pilots simultaneously. Secondly, a genetic algorithm is proposed to minimize the merit factor of the proposed criterion efficiently. Simulation results show that the proposed optimized pilots outperform the random pilots in terms of mean-squared error by about 3 dB. Moreover, the proposed criterion is more likely to achieve better measurement matrices than the traditional criteria.

By pre-equalizing inter-stream interference at the transmitter, Tomlinson–Harashima precoding (THP) algorithm provides a solution for the downlink of multiple antenna multi-user systems, in which the decentralized structure of the receivers makes the receiver-processing impossible. However, for the zero-forcing (ZF) THP algorithm available in the literature there are significant performance differences between specific mobile stations. In this paper, a novel version of the THP algorithm is proposed. It greatly improves the worst mobile's performance and ensures balanced performance of all the mobiles. For the new THP algorithm, better performance can be obtained by suitably ordering the rows of the channel matrix. We show that the “best-first” ordering method achieves optimal order for BER performance in systems with two mobile stations and achieves near optimal order in systems with more than two mobile stations. Simulation is used to show the advantages of the new THP algorithm and the “best-first” ordering method.

In order to provide secure remote access control, a robust and efficient authentication protocol should realize mutual authentication and session key agreement between clients and the remote server over public channels. Recently, Chun-Ta Li proposed a password authentication and user anonymity protocol by using smart cards, and they claimed that their protocol has satisfied all criteria required by remote authentication. However, we have found that his protocol cannot provide mutual authentication between clients and the remote server. To realize ‘real’ mutual authentication, we propose a two-factor remote authentication protocol based on elliptic curve cryptography in this paper, which not only satisfies the criteria but also bears low computational cost. Detailed analysis shows our proposed protocol is secure and more suitable for practical application.