Fabrication of an Esaki Tunneling Diode by Proximity Rapid Thermal Diffusion

Tunnel diodes need degenerately doped junction to optimum performance. They are fabricated using the Molecular Beam Epitaxial method. This method yields good degenerately doped junctions. However it is not clear whether the process can be used with 300 mm wafer technology due to the demand for a high thermal budget. Proximity Rapid Thermal Diffusion (PRTD) uses the RTP tool for its thermal processing. The amount of time used for the processing is in seconds which yields to a low thermal budget. Moreover RTP tools are a common place in the industry and hence comparatively, PRTD is easy to integrate into the wafer fabrication process. Tunneling diodes are fabricated using the proximity diffusion technique where a dopant is diffused from a source wafer to a device wafer in a RTP chamber. The resulting devices yielded PVCR of 2.1 and a current density of 160 mA/cm2 at 300K. The performance of this device is better than the device which was fabricated before at 850 °C. This paper discusses the process flow and the results of the fabricated devices

Unsupervised Extraction of Representative Concepts from Scientific Literature

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we propose PhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201

Image space trajectory tracking of 6-DOF robot manipulator in assisting visual servoing

As vision is a versatile sensor, vision-based control of robot is becoming more important in industrial applications. The control signal generated using the traditional control algorithms leads to undesirable movement of the end-effector during the positioning task. This movement may sometimes cause task failure due to visibility loss. In this paper, a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task. The feature trajectory tracking helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector. SMC is the right choice to handle the depth uncertainties associated with translational motion. Stability of the closed-loop system with the proposed controller is proved by the Lyapunov method. Three feature trajectories are generated to test the efficacy of the proposed method. Simulation tests are conducted and the superiority of the proposed method over a Proportional Derivative – Sliding Mode Controller (PD-SMC) in terms of settling time and distance travelled by the end-effector is established in the presence and absence of depth uncertainties. The proposed controller is also tested in real-time by integrating the visual servoing system with a 6-DOF industrial robot manipulator, ABB IRB 1200

TALENT RETENTION IN PRIVATE ENGINEERING INSTITUTIONS IN RURAL PARTS OF ANDHRA PRADESH, INDIA

It has been observed that, Talent Retention in Higher Educational Institutions is an ongoing problem in rural parts of India and Andhra Pradesh is no exception to this phenomenon. Many private higher educational institutions especially Engineering Educational Institutions in rural Andhra Pradesh are on the wedge of closure and are not in a position to sustain due to lack of revenue generation to meet even the operational expenses and with this, the existing huge and expensive infrastructure of these sick institutions are going to turn in to National Waste. This has been the burning problem for the last six to seven years due to little importance given to the most important aspect, “Talent Retention”. It is strongly believed that, institutions are made up of staff both Teaching and Non-Teaching: Teaching Staff creating value through proven teaching & Learning processes, innovation, mentorship and many other relevant activities and Non-Teaching staff providing administrative and logistic support to fulfill the needs of the deliverables of Teaching staff. As, an Educational institution strives to meet its organizational goals, it must make sure that it has a continuous and integrated process for Recruiting, Training, Managing, Supporting and Compensating these people to achieve high success rate of Talent Retention

Audience-Centric Natural Language Generation via Style Infusion

Adopting contextually appropriate, audience-tailored linguistic styles is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons. First, it is difficult to collect large volumes of audience-specific stylistic data. Second, some stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this paper, we propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We then infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. The code and data are accessible at https://github.com/CrowdDynamicsLab/StyleInfusion.Comment: 14 pages, 3 figures, Accepted in Findings of EMNLP 202

Residue conservation and dimer-interface analysis of olfactory receptor molecular models

Olfactory Receptors (ORs) are members of the Class A rhodopsin like G-protein coupled receptors (GPCRs) which are the initial players in the signal transduction cascade, leading to the generation of nerve impulses transmitted to the brain and resulting in the detection of odorant molecules. Despite the accumulation of thousands of olfactory receptor sequences, no crystal structures of ORs are known tο date. However, the recent availability of crystallographic models of a few GPCRs allows us to generate homology models of ORs and analyze their amino acid patterns, as there is a huge diversity in OR sequences. In this study, we have generated three-dimensional models of 100 representative ORs from Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Sacharomyces cerevisiae which were selected on the basis of a composite classification scheme and phylogenetic analysis. The crystal structure of bovine rhodopsin was used as a template and it was found that the full-length models have more than 90% of their residues in allowed regions of the Ramachandran plot. The structures were further used for analysis of conserved residues in the transmembrane and extracellular loop regions in order to identify functionally important residues. Several ORs are known to be functional as dimers and hence dimer interfaces were predicted for OR models to analyse their oligomeric functional state

EXTRACELLULAR BIOFABRICATION OF SILVER AND GOLD NANOPARTICLES: TREASURES FROM THE ABYSSAL ZONE

The synthesis of nanoparticles can be accomplished by physical, chemical and biological strategies. Since this has become an expanding area of research in the field of medical sciences and Technology, owing to its potential applications, the need for eco-friendly, non-toxic and economical methods of synthesis have arisen. Biosynthesis of nanoparticles have become the main field of research as it is time efficient, cost effective, less toxic and has abundant resource. This review emphasizes on the biosynthesis of gold (Au) and silver nanoparticles (AgNPs) using marine sources with special reference to algae, their characterisation and its applications. The characterisation of metal nanoparticles is an essential step and can be carried out by various instruments. The various pharmacological, electrical, pest management, parasitology and medical applications of these marine source induced synthesis of nanoparticles have also been portrayed in this review.Â

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes