Motion in ambiguity: Coordinated active global localization for multiple robots

The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in ‘multiple hypotheses’ locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states — which is often referred to as ‘active localization’. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as ‘hypotheses resolving agents’. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as ‘coordinated localization’. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.     

Express Virtual Channels with Capacitively Driven Global Links

Networks on chip must deliver high bandwidth at low latencies while keeping within a tight power envelope. Using express virtual channels for flow control improves energy-delay throughput by letting packets bypass intermediate routers, but EVCs have key limitations. Nochi (NoC with hybrid interconnect) overcomes these limitations by transporting data payloads and control information on separate planes, optimized for bandwidth and latency respectively.     

Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS

The objective of this study is to explore the groundwater availability for agriculture in the Musi basin. Remote sensing data and geographic information system were used to locate potential zones for groundwater in the Musi basin. Various maps (i.e., base, hydrogeomorphological, geological, structural, drainage, slope, land use/land cover and groundwater prospect zones) were prepared using the remote sensing data along with the existing maps. The groundwater availability of the basin is qualitatively classified into different classes (i.e., very good, good, moderate, poor and nil) based on its hydrogeomorphological conditions. The land use/land cover map was prepared for the Kharif season using a digital classification technique with the limited ground truth for mapping irrigated areas in the Musi basin. The alluvial plain in filled valley, flood plain and deeply buried pediplain were successfully delineated and shown as the prospective zones of groundwater.     

Using In-frame Shear Constraints for Monocular Motion Segmentation of Rigid Bodies

It is a well known result in the vision literature that the motion of independently moving objects viewed by an affine camera lie on affine subspaces of dimension four or less. As a result a large number of the recently proposed motion segmentation algorithms model the problem as one of clustering the trajectory data to its corresponding affine subspace. While these algorithms are elegant in formulation and achieve near perfect results on benchmark datasets, they fail to address certain very key real-world challenges, including perspective effects and motion degeneracies. Within a robotics and autonomous vehicle setting, the relative configuration of the robot and moving object will frequently be degenerate leading to a failure of subspace clustering algorithms. On the other hand, while gestalt-inspired motion similarity algorithms have been used for motion segmentation, in the moving camera case, they tend to over-segment or under-segment the scene based on their parameter values. In this paper we present a principled approach that incorporates the strengths of both approaches into a cohesive motion segmentation algorithm capable of dealing with the degenerate cases, where camera motion follows that of the moving object. We first generate a set of prospective motion models for the various moving and stationary objects in the video sequence by a RANSAC-like procedure. Then, we incorporate affine and long-term gestalt-inspired motion similarity constraints, into a multi-label Markov Random Field (MRF). Its inference leads to an over-segmentation, where each label belongs to a particular moving object or the background. This is followed by a model selection step where we merge clusters based on a novel motion coherence constraint, we call in-frame shear, that tracks the in-frame change in orientation and distance between the clusters, leading to the final segmentation. This oversegmentation is deliberate and necessary, allowing us to assess the relative motion between the motion models which we believe to be essential in dealing with degenerate motion scenarios.We present results on the Hopkins-155 benchmark motion segmentation dataset [27], as well as several on-road scenes where camera and object motion are near identical. We show that our algorithm is competitive with the state-of-the-art algorithms on [27] and exceeds them substantially on the more realistic on-road sequences.     

Electronic Contracts

As service-oriented computing increases, so does the role of e-contracts in helping business partners automate contractual agreements and relationships. The key challenge is to translate traditional contracts into executable e-contracts in a way that facilitates runtime monitoring and management. As research in this area progresses, organizations will have different approaches for modeling, implementing, and managing e-contracts. For now, developers must contend with several key research issues and challenges.     

The Term Vector Database: fast access to indexing terms for Web pages

We have built a database that provides term vector information for large numbers of pages (hundreds of millions). The basic operation of the database is to take URLs and return term vectors. Compared to computing vectors by downloading pages via HTTP, the Term Vector Database is several orders of magnitude faster, enabling a large class of applications that would be impractical without such a database. This paper describes the Term Vector Database in detail. It also reports on two applications built on top of the database. The first application is an optimization of connectivity-based topic distillation. The second application is a Web page classifier used to annotate results returned by a Web search engine.     

A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks

Reliable routing of packets in a Mobile Ad Hoc Network (MANET) has always been a major concern. The open medium and the susceptibility of the nodes of being fault-prone make the design of protocols for these networks a challenging task. The faults in these networks, which occur either due to the failure of nodes or due to reorganization, can eventuate to packet loss. Such losses degrade the performance of the routing protocols running on them. In this paper, we propose a routing algorithm, named as learning automata based fault-tolerant routing algorithm (LAFTRA), which is capable of routing in the presence of faulty nodes in MANETs using multipath routing. We have used the theory of Learning Automata (LA) for optimizing the selection of paths, reducing the overhead in the network, and for learning about the faulty nodes present in the network. The proposed algorithm can be juxtaposed to any existing routing protocol in a MANET. The results of simulation of our protocol using network simulator 2 (ns-2) shows the increase in packet delivery ratio and decrease in overhead compared to the existing protocols. The proposed protocol gains an edge over FTAR, E2FT by nearly 2% and by more than 10% when compared with AODV in terms of packet delivery ratio with nearly 30% faulty nodes in the network. The overhead generated by our protocol is lesser by 1% as compared to FTAR and by nearly 17% as compared to E2FT when there are nearly 30% faulty nodes.     

Thiobacillus denitrificans immobilized biotrickling filter for NO2 removal

Nitrogen dioxide (NO₂) removal efficiency of a biotrikling filter was evaluated under different operating conditions. Activated alumina (AA) was used as the immobilization matrix for Thiobacillus denitrificans (T. denitrificans) in the biotrickling filter. Batch studies were conducted to find out the degradation kinetics of nitrate and nitrite for a concentration range of 600–10,000 mg/L expressed as nitrogen. Nitrite exhibited maximum degradation rate followed by nitrate. Electron acceptor in the form of NO₂ gas showed least removal efficiency. Bio-kinetic parameters for T. denitrificans, by utilizing nitrate and nitrite as electron acceptors, were also evaluated. The μ_max (Maximum specific growth rate) and Y_T (Yield coefficient) values for T. denitrificans in the presence of nitrate and nitrite were 1.03 h⁻¹, 0.275 and 0.63 h⁻¹, 0.1316 respectively. Column study was conducted to find the adsorption and desorption potential of activated alumina. The adsorbed NO₂ from AA could easily be desorbed using distilled water with an efficiency of 76±0.8%. Once fed batch studies were conducted to evaluate the NO₂ removal efficiency by a biotrickling filter. With an influent NO₂ gas concentration of 2,735 ppm, the reactor could achieve a removal efficiency of 99% within 2 min from gas phase and within 96 h from the liquid phase, with an average biomass concentration of 200 mg/g of AA. The mechanism of NO₂ gas removal in the biotrickling filter seems to be the dissolution of NO₂ in water to form NO₃⁻, conversion of NO₃⁻ to NO₂ ⁻, and finally to N₂ gas.