Sameera Bharadwaja H. | Indian Institute Of Technology, Roorkee (original) (raw)

Papers by Sameera Bharadwaja H.

In this paper, we present and compare three novel model‐cum‐data‐driven channel estimation proced... more In this paper, we present and compare three novel model‐cum‐data‐driven channel estimation procedures in a millimeter‐wave Multi‐Input Multi‐Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communi‐ cation system. The transceivers employ a hybrid analog‐digital architecture. We adapt techniques fromawide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the un‐ known virtual beamspace domain dictionary, as well as the delay‐and‐beamspace sparse channel. We train the model‐based algorithmswith a site‐speci ic training dataset generated using a realistic ray tracing‐basedwireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site’s test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show thatmodel‐based approa...

Proceedings of the the 2012 Ieee Wic Acm International Joint Conferences on Web Intelligence and Intelligent Agent Technology Volume 01, Dec 4, 2012

ABSTRACT Text analytics on consumer-generated content has gained significant momentum over last f... more ABSTRACT Text analytics on consumer-generated content has gained significant momentum over last few years. A wide-range of text mining techniques has been proposed which can provide interesting insights about the text content. But, the challenge still exists in consuming the extracted information in form of actionable intelligence. Identifying actionable intelligence is difficult due to differences in consumer and business languages. Since feedbacks rarely talks of a single problem, determining the problems is also challenging. We propose a framework to address some of these challenges. Organizational websites or standard domain-ontologies are rich repositories of domain knowledge. The proposed method utilizes this knowledge to learn a discriminative classifier model for a domain using Fisher's discriminant metric. The consumer feedbacks are classified to different business categories using the learnt model. The output is further fed into a fuzzy reasoning unit where every feedback is assigned confidence values for each category. Initial experiments show that the proposed framework is capable of handling text feedbacks containing customer complaints in various domains.

Emails constitute the bulk of all official communications in any organization. Email repositories... more Emails constitute the bulk of all official communications in any organization. Email repositories are tacit store-houses of knowledge about people, projects and processes. Mining one's own email repository can also provide interesting and valuable insights about his or her engagements and contacts along different dimensions. In this paper, we propose an email analytics framework that combines text-mining, network analysis and data analytics principles to mine email repositories for useful insights. While individuals are more attuned to looking at emails as individual items along with a history that is embedded in the trail, mining the whole collection can also lead to knowledge-discovery about similarities and dissimilarities of different engagements. This in turn can lead to valuable information like comparative status reports on various projects or deeper insights about why certain projects succeed while others don't. Given the volumes, diversity and noisy nature of e-mail...

ABSTRACT It is well-accepted that when information from structured and unstructured data sources ... more ABSTRACT It is well-accepted that when information from structured and unstructured data sources is analyzed together, the potential of gaining meaningful insights increases manifolds. This paper provides a framework for integrating structured and unstructured data in the context of enterprise analytics. Structured data is assumed to be in the form of a time-series that encodes some aspect of enterprise performance over a specified period, like monthly or weekly sales figures or stock prices etc. Unstructured data may be gathered from news sources, internal repositories of consumer feedbacks, blogs and discussion forums or also from social-media like Twitter, Facebook etc. This paper focuses on intelligent methods of linking time-series data points to the unstructured content in an application-specific way such that the linked unstructured text creates a context for interpreting the time-series behavior. The aim is to generate new forms of data that can be employed in future to derive predictive models or perform causal analytics or also help in risk assessment for Enterprises.

An autonomous robot finds applications in process industries to perform control operations such a... more An autonomous robot finds applications in process industries to perform control operations such as manipulation of valves, especially in hazardous environments. This would require localization and mapping capabilities for efficient navigation, coupled with a dexterous arm for manipulation of the valves. Dynamic path planning and obstacle avoidance are the necessary requirements for autonomous navigation in the work area of a process industry. This paper presents a way to perform the above tasks using an autonomous robot having multi-sensory inputs, and using vision based inferences.

In this paper, we present and compare three novel model‐cum‐data‐driven channel estimation proced... more In this paper, we present and compare three novel model‐cum‐data‐driven channel estimation procedures in a millimeter‐wave Multi‐Input Multi‐Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communi‐ cation system. The transceivers employ a hybrid analog‐digital architecture. We adapt techniques fromawide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the un‐ known virtual beamspace domain dictionary, as well as the delay‐and‐beamspace sparse channel. We train the model‐based algorithmswith a site‐speci ic training dataset generated using a realistic ray tracing‐basedwireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site’s test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show thatmodel‐based approa...

Proceedings of the the 2012 Ieee Wic Acm International Joint Conferences on Web Intelligence and Intelligent Agent Technology Volume 01, Dec 4, 2012

ABSTRACT Text analytics on consumer-generated content has gained significant momentum over last f... more ABSTRACT Text analytics on consumer-generated content has gained significant momentum over last few years. A wide-range of text mining techniques has been proposed which can provide interesting insights about the text content. But, the challenge still exists in consuming the extracted information in form of actionable intelligence. Identifying actionable intelligence is difficult due to differences in consumer and business languages. Since feedbacks rarely talks of a single problem, determining the problems is also challenging. We propose a framework to address some of these challenges. Organizational websites or standard domain-ontologies are rich repositories of domain knowledge. The proposed method utilizes this knowledge to learn a discriminative classifier model for a domain using Fisher's discriminant metric. The consumer feedbacks are classified to different business categories using the learnt model. The output is further fed into a fuzzy reasoning unit where every feedback is assigned confidence values for each category. Initial experiments show that the proposed framework is capable of handling text feedbacks containing customer complaints in various domains.

Emails constitute the bulk of all official communications in any organization. Email repositories... more Emails constitute the bulk of all official communications in any organization. Email repositories are tacit store-houses of knowledge about people, projects and processes. Mining one's own email repository can also provide interesting and valuable insights about his or her engagements and contacts along different dimensions. In this paper, we propose an email analytics framework that combines text-mining, network analysis and data analytics principles to mine email repositories for useful insights. While individuals are more attuned to looking at emails as individual items along with a history that is embedded in the trail, mining the whole collection can also lead to knowledge-discovery about similarities and dissimilarities of different engagements. This in turn can lead to valuable information like comparative status reports on various projects or deeper insights about why certain projects succeed while others don't. Given the volumes, diversity and noisy nature of e-mail...

ABSTRACT It is well-accepted that when information from structured and unstructured data sources ... more ABSTRACT It is well-accepted that when information from structured and unstructured data sources is analyzed together, the potential of gaining meaningful insights increases manifolds. This paper provides a framework for integrating structured and unstructured data in the context of enterprise analytics. Structured data is assumed to be in the form of a time-series that encodes some aspect of enterprise performance over a specified period, like monthly or weekly sales figures or stock prices etc. Unstructured data may be gathered from news sources, internal repositories of consumer feedbacks, blogs and discussion forums or also from social-media like Twitter, Facebook etc. This paper focuses on intelligent methods of linking time-series data points to the unstructured content in an application-specific way such that the linked unstructured text creates a context for interpreting the time-series behavior. The aim is to generate new forms of data that can be employed in future to derive predictive models or perform causal analytics or also help in risk assessment for Enterprises.

An autonomous robot finds applications in process industries to perform control operations such a... more An autonomous robot finds applications in process industries to perform control operations such as manipulation of valves, especially in hazardous environments. This would require localization and mapping capabilities for efficient navigation, coupled with a dexterous arm for manipulation of the valves. Dynamic path planning and obstacle avoidance are the necessary requirements for autonomous navigation in the work area of a process industry. This paper presents a way to perform the above tasks using an autonomous robot having multi-sensory inputs, and using vision based inferences.