Comparative pollen and foliar micromorphological studies using light microscopy and scanning electron microscopy of some selected species of Lamiaceae from Alpine Zone of Deosai Plateau, Western Himalayas.

The study was conducted to highlight a detailed account of morphology of pollen chosen species of Lamiaceae through scanning electron microscopy, and the anatomical characteristics of leaf epidermis of seven species using simple light microscopy. In results, Anisomeles indica and Otostegia aucheri belong to subfamily Lamioideae because it has tricolpate pollen while the rest eight species belong to subfamily Nepetoideae (hexacolpate pollen). The exine sculpturing of pollen of studied species was found to be reticulate. In the family Lamiaceae, four kinds of stomata were found anomocytic, anisocytic, diacytic, and actinocytic, respectively. The cell wall patterns of epidermal cells were irregular or polygonal with straight or undulate walls. It was noted that the variety of the epidermal trichomes seems of taxonomically important for the identification of species of Lamiaceae. Both nonglandular and glandular trichomes were analyzed. The nonglandular trichomes were characterized with long, thin, and pointed apical unicellular cells. The nonglandular trichomes were A-shaped in Thymus linearis. In Perovskia abrotanoides, stellate glandular trichomes were observed whereas in A. indica and Mentha royleana both glandular and nonglandular trichomes were found. In A. indica, the nonglandular trichomes were sessile and peltate in M. royleana. For the first time in this study, pollen and foliar micromorphological features of selected species of this area are carried out. These taxonomic characters were found to be important in discrimination of species from each other. In future, the detailed study with comprehensive morphology coupled with other important characters is required for delimitation of taxa at various levels.

Microscopic investigation of pollen morphology of Brassicaceae from Central Punjab-Pakistan.

The intention of the current study is to provide an account on the palynological features of Brassicaceae from Central Punjab-Pakistan as a basis for future studies. Different morpho-palynological characteristics both qualitative and quantitative were analyzed during this research which includes shape of pollen, diameter of pollen, P/E ratio, exine sculpturing, thickness of exine, type of pollen, shape and size of lumens, and thickness of murus. Taxonomic keys were also constructed based on pollen morphological characters for correct identification of species. This study aims to provide detailed information of pollen diversity and their exine structure based on both qualitative and quantitative characters by using Light microscopy and Scanning electron microscopy. Shape of pollen is mostly prolate, but some species also have sub-prolate to spheroidal prolate types. Exine ornamentation in most species was reticulate, whereas micro reticulate (one species) and coarsely reticulate (one species) exine also observed in some pollen. All the pollen mentioned in this study have tricolpate apertures. Variation found in thickness of exine and other characters proved to be helpful at generic and specific level. The results reinforced the significance of pollen morphological features of family Brassicaceae and aid for valuable taxonomic tool in plant systematics.

A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System.

In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors' names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users' association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.

Machine Learning Based Localization in Large-Scale Wireless Sensor Networks.

The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.

A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks.

With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only those wireless nodes that are within one-hop distance from the anchor nodes. Multi-hop localization algorithms, on the other hand, aim at localizing the wireless nodes that can physically be residing at multiple hops away from anchor nodes. These latter algorithms have attracted a growing interest from research community due to the smaller number of required anchor nodes. One such algorithm, known as DV-Hop (Distance Vector Hop), has gained popularity due to its simplicity and lower cost. However, DV-Hop suffers from reduced accuracy due to the fact that it exploits only the network topology (i.e., number of hops to anchors) rather than the distances between pairs of nodes. In this paper, we propose an enhanced DV-Hop localization algorithm that also uses the RSSI values associated with links between one-hop neighbors. Moreover, we exploit already localized nodes by promoting them to become additional anchor nodes. Our simulations have shown that the proposed algorithm significantly outperforms the original DV-Hop localization algorithm and two of its recently published variants, namely RSSI Auxiliary Ranging and the Selective 3-Anchor DV-hop algorithm. More precisely, in some scenarios, the proposed algorithm improves the localization accuracy by almost 95%, 90% and 70% as compared to the basic DV-Hop, Selective 3-Anchor, and RSSI DV-Hop algorithms, respectively.