Abstract

The current trend in business world involves interaction between multiple parties to fulfill certain business tasks. These tasks are defined at the time when a contract is signed among the parties. A contract is a legal document, which binds together two or more parties to meet a legal obligation. With the advancement of technology, electronic-contracts have evolved. An e-contract is a contract specified, modeled and executed by a software system. E-contract fulfillment has many challenges as contracts involve interdependencies among its various elements such as (i) activities carried out by various parties, (ii) satisfaction of clauses associated with activity executions, (iii) handling of exceptions when there is a clause violation and (iv) ensuring payments and commitments. Thus, there is a need of an (semi) automated system that can help in e-contract enactment. One way of enacting the e-contract is by a workflow management system. A workflow management system (WFMS) that executes an e-contract should have coordination among contract elements such as activities, parties, clauses, payments, exceptions and commitments in order to enable its successful fulfillment. The enterprises have to interact with many partners and, hence it results in inter-organizational workflows. While enterprises implement their own workflows, they might overlook few of their dependencies on other elements such as clauses, payments, exceptions and commitments. Hence, a WFMS needs to handle not only the activities but also exceptions, payments and commitments. Thus, there is a need for a framework that binds all of the above elements together with the workflow and can comprehend to ensure the successful execution of e-contract. In this work, a Hexagonal Integrated Workflow and View Framework for e-contract enactment has been proposed and implemented. The e-contract can have multiple perspectives for each of its elements. To derive the framework that supports multiple views of the e-contract, an integrated workflow was identified as the core component. An integrated workflow was developed that combines individual workflows (of parties) in the required order that allows for flexibility of augmenting additional tasks and events (with the help of user interaction) as required by the contract specifications. Additionally, workflow views for all six elements namely Activity, Party, Clauses, Exceptions, Payments and Commitments were also developed. Here, the views are explicit and clearly specify the tasks and the dependencies among all the elements. Furthermore, the proposed framework helps in monitoring the progress of e-contract execution for its enactment. The proposed system has two major components, namely Pre-enactment workflow and E-contract Execution workflow. The pre-enactment workflow builds an integrated workflow that comprises of parties workflows. The e-contract execution workflow drives e-contract execution and enables workflow views to support the contract elements. The components work in a sequential manner to facilitate contract fulfillment as per the e-contract specifications and enactment. Businesses often find it difficult to manage the e-contracts as it involves interorganizational workflows. The proposed hexagonal views in combination with the workflow components make the e-contract enactment easier. The views account for the e-contract elements and the workflow components help in execution. The workflow components assign the tasks of the e-contract to the concerned parties involved and monitor the progress of the tasks. After the task is completed, it notifies the next party to start the next task. It simplifies execution and monitoring of interorganizational tasks by ignoring the protocols and procedures of an organization, rather focusing on outputs of tasks which can be used by parties to notify E-Contract Execution Workflow. Moving forward, the output becomes the prerequisite for subsequent tasks. The proposed system has tried to bridge the gap that lies between the conceptual model of e-contracts and workflow, by using the concept of views, mapping them to the workflow and executing it using the E-contract Execution Workflow.

Abstract

Abstract In this thesis, we investigate the possibility of sourcing late-time cosmic acceleration by a slowly rolling scalar field and by the large-scale modification of gravity without extra degrees of freedom. As for the slowly rolling scalar field, we consider a model based on a simple potential. We use geometric diagnostics to differentiate the underlying model from the cosmological constant one. With regard to the large-scale modification of gravity, we investigate a scenario in which the late-time cosmic acceleration might arise due to the coupling between dark matter and baryonic matter without resorting to the dark energy or large scale modification of gravity associated with extra degrees of freedom. The scenario can give rise to the late time acceleration in the Jordan frame and no acceleration in the Einstein frame - generic modification of gravity caused by the disformal coupling. In the Einstein frame, we have only matter and thereby no new degree of freedom. Dynamics gets complicated or modified in the Jordan frame with the appearance of an arbitrary coupling function. The altered dynamics gives rise to the acceleration. In this picture, there is no extra degree of freedom as there is none in the Einstein frame where the Lagrangian is diagonalized. Using a simple parametrisation for the coupling function, we constrain the model parameters by using the age constraints due to the globular cluster data. We also obtain the observational constraints on the parameters using H ( z ) + SNIa + BAO data sets. In this case, we distinguish between the phantom and the non-phantom acceleration and show that the model can give rise to the phantom behaviour in a narrow region of the parameter space.

Abstract

Gas sensors find many applications in the biomedical and industrial fields. Among various gas sensors, sensors for the detection and measurement of acetone and ethanol are very important. We explored here the sensing properties of flower-shaped nanoparticles of pure zinc oxide (ZnO), an n-type semiconducting metal oxide, and Ag and Pd nanoparticle-modified ZnO nanoflower systems towards the sensing and detection of acetone and ethanol. The nanoparticles were prepared by literature-reported methods. We demonstrated that pure ZnO nanoflowers showed high sensitivity and very fast response and recovery times towards both the gases. The sensors showed a significant sensor response even at 1 ppm concentration of the target gas. We demonstrated that the selectivity preference for the gases could be modulated by loading metal nanoparticles onto the surface of the ZnO nanoflowers. The sensors had no interference from water vapor and CO2. Further, the sensors showed excellent sensing cycle reversibility. Such sensors can be promising candidates as industrial and environmental gas sensors.

Abstract

The High-Definition Multimedia Interface(HDMI) is used for transmitting digital television audiovisual signals from DVD players, set-top boxes and other audiovisual sources to television sets, projectors and other video displays.It can carry high quality multi-channel audio data and can carry all standard and high-definition consumer electronics video formats.HDMI is a digital replacement for existing analog video standards.At its heart, HDMI is a high-speed, serial, digital signaling system that is designed to transport extremely large amounts of digital data over a long cable length with very high accuracy and reliability.In order to achieve such high accuracy and reliability,HDMI uses Transition minimized Differential Signalling (TMDS) to move information from one place to another.In this scheme, the data is encoded so as to reduce the number of 0-1 transition which in turn reduces the inter-symbol interference.The encoded data is then transmitted serially over the cable.The physical layer for TMDS is current mode logic, DC Coupled and terminated to 3.3V.The use of Current Mode Logic(CML) ensures superior performance in noisy environments and in the presence of lossy transmission lines. Need for a low-power HDMI Source At 3.4 Gb/s data rate the power consumption of a conventional HDMI source is signicant and so there is a need for new HDMI source architectures for saving power while meeting the specifications defined by HDMI standards. Reduced power consumption is achieved in previous literature by using single-ended source structure. But, it is well known that such an arrangement is more susceptible to supply noise due to its poor power supply rejection and also susceptible to other noise sources such as crosstalk from neighboring lines. Proposed Solution Here, we propose an HDMI source with reduced power consumption while retaining the differential signalling so as to maintain better noise immunity. Proposed power-efcient back-terminated HDMI source transmits the data at 3.4 Gb/s per channel while meeting all other specications of HDMI standards. The proposed source provides high output impedance for current mode driving as well as a back-impedance of 100 Ω for matching the characteristic impedance of the line. With the help of the proposed circuit topology it is possible to achieve the output voltage swing dened by HDMI standard with a smaller tail current thus bringing down the power consumption of the HDMI source. This HDMI Source is mplemented in 3.3V, 0.18μm CMOS Technology which takes into account the device parasitic and second-order effects. The HDMI system also includes an 8-bit pseudo random binary sequence (PRBS) Generator driven by a phase locked loop (PLL) which provides the source with differential test data. The PLL provides the required 3.4 GHz clock signal to the PRBS Generator. The PRBS generator which is differential in nature, uses a current-mode XOR gate and sense-amplier based ip-ops with reduced delay and improved driving capability for better performance.The PLL features a differential delay based ring oscillator with reduced supply-sensitivity. The designed HDMI source is capable to transmit data at the rate of 3.4 Gb/s per channel and meets all the specifications defined by HDMI Specification Version 1.4. The power consumed by this HDMI source is 27.7 mW at 3.3 V, which is 30% lower than the power consumed by a conventional HDMI source.

Abstract

The need for cross-lingual information access is more than ever with the easy access to the Internet. This is especially true in vastly multilingual societies like India. Cross-lingual summarization (CLS) aims to create summaries in a target language from a document or document set given in a different, source language. Cross-lingual summarization can have significant impact on enabling cross-lingual information access for millions of people across the globe who do not speak or understand languages that have a large representation on the web by making the most important information available in the target language in the form of summaries. It can also make documents originally published in local languages quickly accessible to a large audience which does not understand those local languages. Working towards a better cross-lingual summarization system, we first create a flexible, web-based tool, referred to as the workbench, for human editing of cross-lingual summaries to rapidly generate publishable summaries in a number of Indian languages for news articles originally published in English. The workbench simultaneously collects detailed logs about the editing process at article, summary and sentence level. Similar to translation post-editing logs, such logs can be used to evaluate the automated cross-lingual summaries in terms of effort needed to make them publishable. We use the workbench to generate two manually edited datasets for different tasks. We observed that quality of automatic translation is a major bottleneck when working on CLS. Translation Quality Estimation (QE) aims to estimate the quality of an automated machine translation (MT) output without any human intervention or reference translation. With the increasing use of MT systems in various cross-lingual applications, the need and applicability of QE systems is increasing. We study existing approaches and propose multiple neural network approaches for sentence-level QE with a focus on MT outputs in Indian languages. For this, we also introduce five new datasets for four language pairs: two for English–Gujarati, and one each for English–Hindi, English–Telugu and English–Bengali, which includes one manually post-edited dataset for English–Gujarati created using the workbench. We compare results obtained using our proposed models with multiple existing state-of-the-art systems including the winning system in the WMT17 shared task on QE and show that our proposed neural model which combines the discriminative power of carefully chosen features with Siamese Convolutional Neural Networks (CNNs) works significantly better for all Indian language datasets. Later, we integrate our efforts on QE with cross-lingual summarization to study its effect on CLS. We extend a popular mono-lingual summarization method to work with CLS, along with a new objective function to take QE scores into account while ranking sentences for summarization. We experimentwith a number of existing methods for CLS with different parameters and settings and show comparative analysis. At the end, we publish an end-to-end CLS software called clstk to make CLS accessible to a larger audience. Besides implementing a number of methods proposed by different CLS researchers over the years, the tool-kit also includes bootstrap code for easy implementation and experimentation with new CLS methods. We hope that this extremely modular tool-kit will help CLS researchers contribute more effectively to the area as well as developers to easily use existing methods in end-user applications.

Abstract

Since the inception of Web 2.0, the virtual data landscape has witnessed unprecedented flow of user generated texts. With applications ranging from product analysis and implementing business strategies to inculcating public opinion in governance processes, Sentiment analysis has become one of the focal points of scientific exploration. Sentiment analysis can be used to decipher subjective opinions expressed over various online platforms using natural language processing methods, statistical and machine learning techniques. With English language having witnessed extensive research in this domain, deciphering the same in vernacular languages remains a prodigious task. While significant strides have been made in Indian languages in the recent years, there has been negligible progress with respect to Sentiment analysis for Odia language, owing to the unavailability of essential tools and resources to identify subjective information in Odia texts. This thesis aims at bridging this gap by building such tools and resources. In order to capture subjective information at sentence-level, a sentiment lexicon becomes a valuable tool. In this thesis, we propose two distinct approaches towards building such a lexicon for resource-poor Indian languages such as Odia. The first methodology involves a translation-based approach which makes use of available resources in English to create a source lexicon. Further using an appropriate machine translation system, the target lexicon is created. A machine translation system between English and the target language being a prerequisite for this approach, the unavailability of the same for Odia language (and a few other resource-poor Indian languages) becomes a methodological drawback. In order to overcome such a disadvantage, we propound a synset-based approach to create the Odia sentiment lexicon. This approach makes use of a linked WordNet structure along with sentiment lexicons available for the used Indian languages. To address the lack of sentiment annotated corpora, we have built two datasets from different domains. Firstly, we have created a corpus of 730 Odia poems annotated with sentimental polarity. An annotation scheme comprising a polarity identification questionnaire clubbed with a taxonomy of emotions has been adopted to label each poem, as positive or negative, at a document level. In order to establish a strong baseline for the corpus, several experiments have been conducted. We employed several machine learning techniques along with word-level features for the initial classification task. Usage of character-level features showed consistent improvementsto the baseline in comparison to word-level features. By creating a word embedding model for Odia language, we have exploited word vector representations as features for classification of Odia poems, the results of which are comparable to that of character-level features. In the domain of news, the thesis also presents a sentiment annotated corpus of 2045 Odia sentences. For sentence-level annotation, proper annotation guidelines have been discussed in order to categorize each sentence as positive, negative or neutral. Baseline results for the corpus have been established with machine learning techniques, using both word and character-level features, for binary and ternary classification. Among these classifiers, Support Vector Machinesand Logistic Regression show comparable results in the classification task. Furthermore, in orderto bear witness to the reliability and utility of the Odia sentiment lexicon, affective words at sentence-level have been identified and used as features for classification. Inclusion of thesefeatures show consistent improvements for both binary and ternary classification in all metrics of evaluation. This testifies the usability of the created Odia sentiment lexicon for sentence-level sentiment classification.

Abstract

Small and medium sized obstacles such as rocks, small boulders, bricks left unattended on the road can pose hazards for autonomous as well as human driving situations. Many times these objects are too small on the road and go unnoticed on depth and point cloud maps obtained from state of the art range sensors such as 3D LIDAR. In the first part of thesis, a novel algorithm is proposed that fuses both appearance and 3D cues such as image gradients, curvature potentials and depth variance into a Markov Random Field (MRF) formulation that segments the scene into obstacle and non obstacle regions. Appearance and depth data obtained from a ZED stereo pair mounted on a Husky robot is used for this purpose. While identifying true positive obstacles such as rocks, large stones accurately our algorithm is simultaneously robust to false positive sources such as appearance changes on the road, papers and road markings. High accuracy detection in challenging scenes such as when the foreground obstacle blends with the background road scene vindicates the efficacy of the proposed formulation. In the second part of thesis, a novel neural network architecture called MergeNet for discovering small obstacles for on-road scenes in the context of autonomous driving is proposed. The basis of the architecture rests on the central consideration of training with less amount of data since the physical setup and the annotation process for small obstacles is hard to scale. For making effective use of the limited data, we propose a multi-stage training procedure involving weight-sharing, separate learning of low and high level features from the RGBD input and a refining stage which learns to fuse the obtained complementary features. The model is trained and evaluated on the Lost and Found dataset and is able to achieve state-of-art results with just 135 images in comparison to the 1000 images used by the previous benchmark. Additionally, we also compare our results with recent methods trained on 6000 images and show that our method achieves comparable performance with only 1000 training samples.

Abstract

Motion planning is an important primitive for a robotic or mechanical system with closed kinematic chains that lets the robot find the shortest or otherwise optimal path in order to reach its goal position. Sampling based methods provide an efficient solution for motion planning in basic robot systems. However, complex robotic systems such as humanoids have a large number of degrees- of-freedom (DoF) which makes motion planning extremely dicult. Further, these systems also have constraints such as self-collision avoidance and active balancing. Thus, incorporating these constraints in existing motion planners is complex and computationally expensive. Therefore, real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. In the first part of this thesis, a methodology to generate joint-space trajectories of stable configurations for motion planning using Deep Reinforcement Learning (RL) is proposed. The methodology is based on Deep Deterministic Policy Gradient (DDPG), where the robot autonomously learns the optimal behavior through a series of trial-and-error interaction with the environment. The proposed strategy was evaluated for various motion planning tasks on the highly articulated upper body of a humanoid model with 27 degree-of-freedom (DoF). Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where actions between different tasks are not fully separable and share a set of actions. For cases where task-specific actions are fully separable, each task policy can be learnt independently using different policy networks. However when the task-specific actions are not fully separable, the policies cannot be learnt independently. In such environments, a compound policy may be learnt with shared neural network parameters which performs multiple tasks concurrently. However such compound policy may get biased towards a task or the gradients from dierent tasks negate each other, making the learning unstable and sometimes less data efficient. The main contribution of this work is a novel framework for simultaneous learning of multiple tasks sharing a set of common actions in continuous action spaces, which we call as DiGrad (Differential Policy Gradient). The proposed framework is based on differential policy gradients (DPG) and can accommodate multi-task learning in a single actor-critic network. We also propose a simple heuristic in the differential policy gradient update in case of partially separable actions to further improve learning. The experimental results show that the framework supports ecient multi-task learning in complex robotic systems, outperforming related methods in continuous action spaces such as DDPG. With the advent of artificial intelligence and machine learning, humanoid robots are made to learn a variety of skills which humans possess. One of fundamental skills which humans use in day-to-day activities is performing tasks with coordination between both the hands. In case of humanoids, learning such skills require optimal motion planning which includes avoiding collisions with the surroundings. In the nal part of this thesis, a methodology based on DiGrad to learn coordinated tasks in cluttered environments is proposed. Further, we propose an algorithm to smooth the joint space trajectories obtained by the proposed framework in order to reduce the noise instilled during training. The proposed framework was tested on a 27 DoF humanoid with articulated torso for performing coordinated object-reaching task with both the hands in four different environments with varying levels of difficulty.

Abstract

The rapid advancements in the field of semiconductor technologies has paved the way for high speed embedded systems that require high speed of operation, lower power dissipation and robust to electromagnetic noises and interference. The synchronous digital system have been a successful alternative due to simplicity of design but the advancements in the embedded system has imposed major challenges in the simultaneous reduction of delay and power consumption. The reduction in clock period in synchronous design enables higher operating frequency that enhances the clock synchronization complexity and dynamic power consumption leading to the surge in the demand of asynchronous architecture. The clock routing and power consumption issue in synchronous design have been eradicated with the concept of (globally asynchronous locally synchronous) GALS architecture in the year 1975’s. There was no global clock in the design and the sub-modules in the design interact with each other using asynchronous handshaking protocols. The highest operating frequency in GALS architecture was still constrained and limited with respect to the slowest sub-module clock frequency. The fully asynchronous architecture eradicates this limitation since the data transfer is irrespective of clock transitions and depends upon the availability of valid data in order to transfer from source to receiver end. In this thesis, we have proposed the design of modified 4-phase handshaking protocol on the lines of existing standard 4-phase protocol developed earlier for data communication in asynchronous circuits. The proposed protocol has achieved better performance in terms of speed by taking 1-cycle lesser than the existing 4-cycle implementation of standard 4-phase with minimal area overhead. The simulation results obtained for the design of asynchronous ALU based on modified 4-phase handshaking protocol performs better than the existing synchronous and asynchronous standard 4-phase design by executing higher number of instructions in a given time. The time taken in simulation experiment was computed by taking fixed data set of random instructions including ADD, SUB, OR, XOR etc that showed 1.4x increase in the performance in comparison with the asynchronous ALU implemented using standard 4-phase protocol design. The simulation result for the design of asynchronous FSM based on modified 4-phase handshaking protocol outperforms the number of bit sequences detected in a non-overlapping fashion(speed) by existing synchronous and asynchronous standard 4-phase design. The results indicate an increase in speed of 1.65x as compared to the synchronous design and 1.4x in comparison with the design based on asynchronous standard 4-phase protocol.

Abstract

Wireless devices are typically powered by batteries to ensure their portability. The finite energy storage capacity of the batteries limits operational period of the network. Recharging and replacing batteries of devices is time consuming or expensive, particularly when the sensors are deployed in hostile or hard-to-access environments. Energy harvesting (EH), a technique to collect energy from the ambient energy sources including radio frequency (RF) signals, has received considerable attention as a viable solution to this problem. Simultaneous wireless information and energy transfer (SWIET) explores a dual use of RF signals to transfer information jointly with energy using the same waveform. This can guarantee a reliable supply of energy unlike the case of other ambient energy sources. In this work, we consider SWIET in a cooperative wireless network involving two transceiver nodes whose communication is assisted by energy-constrained two-way amplify-and-forward (AF) relay nodes. The transceiver nodes can simultaneously transmit information and energy, and the relay nodes harvest the energy from the received signal and use the harvested energy to amplify and retransmit the received signal to the transceiver nodes. We consider two practical receiver architectures, namely, time switching (TS) and power splitting (PS). In TS, the receiver harvests energy for a fraction of transmission block and then decodes information for the remaining fraction. In PS, a fraction of the received signal is used for energy harvesting and the rest for information decoding. Based on the TS and PS receiver architectures, we propose the TSR and PSR protocols to facilitate simultaneous energy harvesting and information processing at the relay. For both protocols, we address the problem of resource allocation along with energy harvesting and optimal relay selection. The performance of the proposed PSR and TSR protocols for different parameters is illustrated by numerical simulations. We extend the relaying protocols discussed above to a cognitive radio (CR) scenario. Cognitive radio networks (CRNs) achieve better spectral efficiency by allowing the licensed users to share their spectrum with unlicensed users. The interference produced by the unlicensed users is kept below an accepted threshold. We consider a CRN with a licensed primary user (PU), two secondary transceivers, and a set of secondary two-way amplify and forward (AF) relays. The secondary user (SU) nodes share the spectrum with the PU node, and each node is assumed to be equipped with a single antenna. SU transceivers communicate with each other only via the relay optimally selected form the set of available relays. The transmit signals from the SU transceivers are designed so as to carry both information and energy. The relay harvests energy from these signals and uses this energy to process the received signals for further transmission.