In this work we present an experimental demonstration of the comparative study of imaging quality of one of the non-uniform sized (NUS) optically-sparse aperture (OSA) mirror configurations - the Taylor-ln design as proposed in [1] against that of a conventional segmented mirror having sub-apertures of equal size, in laboratory. The perfomance of the proposed NUS OSA mirrors are noted to be equivalent to those of uniform-sized segmented mirrors, using simulations in the ideal scenario of noiseless imaging systems. Therefore, it is of utmost importance to demonstrate the efficiency of such NUS OSA mirror configurations, in the laboratory frame at least, so that real-life applications of these designs can be surmised. Therefore, in this paper we design masks that mimic an uniform sized segmented mirror with each sub-aperture as large as 2 cm and a NUS OSA mirror whose aperture sizes vary as the coefficients of the Taylor expansion series for ln. We consider only the average intensity information of the images captured by these two types of segmented mirrors. Thereafter, simple deconvolution with respective Point Spread Functions (PSFs) results in nearly similar reconstructed images, at least by visual inspection. In case of the uniform sized segmented mirror the Peak Signal-to-Noise-Ratio (PSNR) is 7.8 dB alongwith the Structural Similarity Index Measure (SSIM) being 0.1. The respective values for the NUS system are similar (8.8 dB and 0.24, respectively). This explicitly demonstrates very little degradation of performance even though the fill factor for the NUS design is reduced to 31% of the uniform OSA mirror. However, we emphasize on the fact that this is a very basic experimental setup, demonstrating the efficiency of imaging for such NUS systems and suggest further improvement of the reconstructed image quality with better quality mirrors used for the purpose as well as incorporating advanced reconstruction algorithms coupled with a suitable denoising algorithm. The success of this kind of an experiment is a promising breakthrough to encourage implementation in real-life ultra-lightweight space-based telescopic systems, encompassing a wide plethora of scientific objectives viz., astronomy, natural resources as well as infrastructure monitoring, security and surveillance and even disaster prediction like earthquakes.

Any high spatial resolution space-borne electro-optical sensing system operating in long wavelengths, like Earth-observation facilities operating in the Longwave Infrared are subjected to an inherent design and implementation challenge of deploying large monolithic primary aperture mirrors. To outflank this issue, many present-date missions design and commission lightweight segmented mirrors, mostly with equal sized sub-apertures. To go one step ahead, these sub-apertures could be of non-uniform sizes, in a given proportion, thereby ensuring an even smaller and lighter primary, with a marginal compromise in imaging quality. However, a fragmented mirror is very sensitive to failure of one or more of the sub-apertures, in some random fashion (most likely being hit by space debris or surge of high energy particles) or in a cluster (possibly due to failure of the actuators or control system). In such scenarios, the system suffers loss of the high-frequency components to various extents, so that the edge detection in the reconstruction process (deconvolution) becomes questionable. This is particularly true for the central sub-aperture, rendering it as the most critical component in the partially-filled primary aperture design. In such cases, the imaging system malfunctions as a whole, thereby defeating the purpose of designing of such ultra-lightweight optically-sparse primary aperture mirror with non-uniform sized sub-apertures for satellite-based sensing systems.

In today’s world, environmental pollution is worsening and having detrimental effects on the environment. The current focus of human endeavors is aimed at reducing this pollution to ensure a sustainable future for our planet. In this pursuit, we are striving to create a framework that can estimate the carbon fluxes within a city. The accurate estimation of carbon fluxes from cities is crucial for understanding and mitigating their impact on global greenhouse gas emissions. This paper presents a novel approach for estimating carbon fluxes at a spatial resolution of 1 km x 1 km grids using remotely sensed data. Our research relies on indicators such as population and impervious surface area as proxies for estimating carbon dioxide (CO2) levels with a linear regression model. To test the generality of the method and models, we have validated our grid-model coefficients on point data and vice versa.

Classification of remotely sensed data is the mainstay of analysis methods for generating actionable insights. Classification of remotely sensed data is inherently a semi-supervised classification problem. Often, the labeled pixels and the unlabeled pixels in the image may have different distribution. Hence classification accuracy of such images is affected. We propose a umbrella framework for semi-supervised learning that considers the domains shifts in labeled and unlabeled pixels (called Domain Aware Semi-supervised learning- DASSL). The method learns the deep features in a such a that they are invariant of the pixel source, i.e, labeled or unlabeled. We employed DASSL for classification of hypersepctral image of Pavia University. We compared DASSL with self-training iterations performed using SVM and Convolutional Neural Network. We used spectral features, spatial features, and fused spectral-spatial features. The results are encouraging. We observed the reasonable improvement in classification by DASSL over self-training iterations.

Scientific objectives from earth observation to astronomy require high-resolution observations from space-based platforms. However, designing space telescopes with large primary apertures to achieve high-resolution and high Signal-to-Noise Ratio observations, especially for those operating in longer wavelengths (like Thermal Infrared, TIR), is not feasible due to difficulties in manufacturing, launching, and post-deployment stabilizing. This work proposes three novel lightweight, optically-sparse (also known as partially-filled) mirrors with non-uniform sub-aperture sizes. These designs reduce the mass of the primary mirror and its supporting framework. The crux of these designs is, however, significant suppression of sidelobes in the resulting Point Spread Functions (PSFs). The study includes restored images and image quality indices, demonstrating the effectiveness of such lightweight unequal sub-apertures as replacements for large monolithic mirrors with only a marginal loss in performance.

Automatic object recoloring or swatch generation aims to change color of an object or the entire scene without altering the structural consistency. The challenges exacerbate while dealing with retail items, such as clothing, shoes and accessories due to the presence of complex patterns, folds and shadows, deformation caused by the human model, and small components such as frills, buttons, belts, etc. The challenge associated with this application increases further for multi-colored item and multi-apparel setup. In this work, we aim to address these problems with our novel architecture SwatchNet based on generative adversarial network (GAN). It stems from the proposed apparel components aware feature extraction module to create rich feature embedding, which guides the proposed dual attention u-net to synthesize recolored product image. For seamless information flow, we have also proposed a dual attention module at the bottleneck of encoder and decoder. Finally, we unify a diverse set of recoloring applications using a fixed training and inference pipeline. The experimental results of fashion item recoloring for several test setups using four large-scale datasets demonstrate the effectiveness of our proposed approach.

Can a surveillance robot autonomously detect anomalous activity from its ego view camera perception? This is a challenging task as it requires identifying what is normal and what is an abnormal pattern - given the variations of possible anomalies and abnormalities. This paper presents an architecture and method based on a spatio-temporal convolution neural network to detect and classify anomalies. This work is inspired by the ‘Konio-Magno-Parvocellular’ cells of the human brain, which is claimed to aid humans in organizing changes in perceived scenes. The model is trained and tested on a benchmark video dataset [1] of human activity. We have obtained 91% testing accuracy on this dataset. Experiments in simulation as well as deployment on a real robot shows that the proposed methodology can identify anomalous activities effectively. We have also listed down the observations from practical deployment of the model.

Recommendation systems improve users' online shopping experience by recommending relevant items from a large pool of items in different categories. Fashion recommendation systems apart from recommending individual fashion items also recommend fashion outfits. In this work, we consider the problem of the outfit compatibility prediction task, an integral part of the fashion outfit recommendation system. A compatibility prediction module determines whether all the items in an outfit are visually compatible with each other and match the user's preferences. Existing approaches can be grouped based on the representation scheme: (i) pair-wise and (ii) set or sequence. Pair-wise representation does not consider the outfit as a whole, and the sequence representation approaches are sensitive to the ordering of the items. Further, these methods do not explicitly capture the visual relationship between the items. We propose a novel method for the personalized outfit-compatible prediction task. The proposed method represents the outfit as a graph and uses a dot-attention graph neural network to capture the visual relationship between items. The graph read-out layer generates the final outfit embedding. A novel approach is proposed to model the user's preference for different styles. The final outfit compatibility score is generated by computing the similarity between outfit embedding and user embedding. Experimental results and ablation study on the Polyvore-U dataset, highlight the effectiveness of the proposed method.

Tracking of inventory and rearrangement of mis-placed items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.

Calculating CO2emissions from a city using Global Protocol for Communities (GPC) guidelines is a data intensive exercise. Moreover, the outcome is difficult to verify because of the data quality issues. We have developed a quick and approximate method for calculating scope 1 and scope 2 CO2emitted by a city, using Landsat 8 data. We used Vegetation-Impervious surfaces-Soil (VIS) classes extracted from the satellite image, and city population as independent variables and related it with CO2emissions. The model results match well with the reported total annual carbon emissions by the world carbon budget report.

Fusing SAR and multi-spectral images to generate a precise land cover map in a weakly supervised setting is a challenging yet essential problem. The inaccurate, noisy, and inexact ground truth labels pose difficulty training any machine learning models. In this paper, we make a fundamental and pivotal contribution towards improving the ground truth label quality using domain knowledge. We present a simple yet effective mechanism to refine the low-resolution noisy ground truth labels. The proposed approach is trained and tested on a publicly available DFC2020 dataset. Through experiments, we show the effectiveness of our method by training a deep learning model on the refined labels that outperform even the models trained with clean ground truth.

In this paper, we present a novel few-shot cross-sensor domain adaptation technique between SAR and multispectral data for LULC classification. Cross-sensor, such as SAR and multispectral, domain adaptation is a long standing challenge in remote sensing. Due to scarcity of large annotated dataset for every domain, it is desirable to have a method that enables cross-domain training with limited supervisory signal in that domain. We address this problem in this paper with a novel few-shot domain adaptation technique. We leverage large corpus of annotated multispectral dataset to improve performance for SAR based LULC classification. We propose a novel Feature Domain Alignment (FDA) loss function to align higher dimension features between multispectral and SAR domain. We validate our approach in publicly available DFC2020 dataset and achieve 78% overall LULC classification accuracy using only 5% annotated SAR samples.

Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph. Recent works have demonstrated that scene graph representation improves the performance of various computer vision tasks such as image retrieval, action recognition, visual question answering. Computationally efficient scene graph generation methods are required to leverage scene graphs in various real-world applications (e.g., autonomous driving, robotics). A typical scene graph generation model consists of two modules: (i) object detector and (ii) scene graph classifier. The scene graph classifier module predicts the object category and object-object relationships. The presence of a quadratic number of potential edges poses a major challenge in the scene graph classification task. Detecting the relationship between each object pair using the traditional approach is computationally intensive and non-scalable. To address this issue, we propose a novel module named EdgeNet that directly predicts the set of relevant edges and helps to prune out a significant number of unrelated object pairs, thereby improving the effectiveness and efficiency of the scene graph classifier. The proposed EdgeNet is a generic module and can be plugged into an existing scene graph classifier. Experimental results highlight the effectiveness and efficiency of the proposed approach on the Visual Genome dataset.

Passive exoskeletons are unpowered wearable robotic devices aimed at providing biomechanical assistance. They can be applied in industries such as manufacturing, construction and logistics to reduce repetitive stress injuries among workers. Their design process typically considers a static user, with muscle outputs computed later during dynamic tasks to evaluate performance. Attempting to reduce human muscle effort at this stage requires manual redesign. Instead, we propose a parameter optimization approach that minimizes muscle effort rates during realistic dynamic tasks in the design stage itself. We extract human kinematics in assembly tasks from an industry-oriented motion capture dataset, and compute the induced joint torques. Using a passive exoskeleton for shoulder joint gravity compensation from the literature as a baseline, we optimize its design parameters through a multi-objective Pareto Local Search, minimizing the muscle effort rates during these tasks. As the estimation of muscle outputs through biomechanical simulation techniques is computation-ally expensive, we train ensemble regression models for each muscle of interest during the task motions. These models serve as surrogates for the objective function in the design optimization procedure, speeding up search in the parameter space. The resulting exoskeleton with optimized design param-eters reduces estimated muscle effort rates by an average of 5.73% and peak of 35.1 % compared to default parameters, and an average of 14.5% and peak of 32.2% compared to not wearing an exoskeleton in overhead assembly tasks. A larger peak reduction compared to default parameters may be due to hindrance in motion caused by device. This approach may be adapted to other exoskeletons and applications, improving biomechanical assistance by design.

Large-scale attribute recognition in fashion retail images is a crucial task in image-based recommendation systems. The challenges are due to the visually-similar instances, localized minute information and overlapping features. Moreover, the class imbalance further exacerbates the challenge, needing for a specific solution to alleviate the problem. In this work, F-AttNet architecture is proposed, which is designed by the hierarchical alignment of the novel Attentive Multi-scale Feature (AMF) encoder blocks. AMF encoders extract mid-level multi-scale fine-grained attribute features involving multiple representations of low-level features and finally, the high-level global description is encoded by adaptively calibrating the channel weights. For improving the training performance, a novel gamma-variant focal loss is developed to handle class imbalance by assigning more penalty and assigning relative weights to positive and negative instances shifting the focus of the network to false instances. Experimental results and ablation studies of F-AttNet using a large-scale fashion attribute recognition database iMaterialist-2018 demonstrate significant performance improvement than the state-of-the-art methodologies.

Mammalian neural circuits respond to different sensory stimuli by firing spikes at particular times. Closely mimicking this phenomenon, the evolving 3rd generation neural networks, known as Spiking Neural Networks (SNNs), are found to be capable of memorizing and learning from the spatio-temporal spike patterns. This makes SNN applicable in identification of human actions and gestures, especially in the robotics domain. The paradigm is also suited for Neuromorphic Systems leading to less energy intensive applications. In this work, we present a novel spiking neural network constituting multiple convolutional layers and a reservoir layer to extract spatial and temporal features respectively from human gesture videos captured with DVS camera. We achieved more than 95% Top-3 accuracy on IBM DVS dataset and we claim that the performance of our network is better in terms of accuracy vs. learning parameters ratio when compared to other networks.

In this paper, we present a deep convolutional neural network (CNN) architecture for segmenting semantic changes between two images. The main objective is to segment changes at the semantic level than detecting background changes, which are irrelevant to the application. The difficulties include seasonal changes, lighting differences, artifacts due to alignment and occlusion. The existing approaches fail to address all the problems together; thus, none of them achieve state-of-the-art performance in three publicly available change detection datasets: VL-CMU-CD [1], TSUNAMI [2] and GSV [2]. Our proposed approach is a simple yet effective method that can handle even adverse challenges. In our approach, we leverage the correlation between high-level abstract CNN features to segment the changes. Compared with several traditional and other deep learning-based change detection methods, our proposed method achieves state-of-the-art performance in all three datasets.

A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multi-purpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.

Industry 4.0 deployments involve machines, robots, Internet of Things, business processes and human participants coordinating in time constrained and safety critical environments. As these deployments make use of complex workflow patterns, accurate formal modeling, verification and timing analysis of such industrial systems are needed. In this paper, we model the workflow interactions of Industry 4.0 warehouse operations using the concurrent programming language Orc. Complex deployments involving multiple robotic agents and business processes further require analysis of correctness, liveness and safety properties. In order to verify the workflows, the Orc specifications are translated into Workflow net representations, with verification done using the TAPAAL model checker. Additional composition of timing constraints are analyzed to enable hard robotic deadlines to interact with best effort Service Level Agreement (SLA) requirements. We demonstrate these aspects over a realistic use case in automated warehouse management with pick and delivery robots.

Drones are used in a number of industrial applications such as asset tracking and inspection. Indoor industrial applications based on visual data pose various challenges such as low lighting conditions and presence of non-planar scenes. Due to the nature of the indoor applications, image data is acquired at close range and this leads to the loss of context. In order to get global context, image stitching is a key step for data interpretation. We propose an approach to stitch drone-captured indoor video frames, where feature based stitching fails. In order to achieve this, the image feature data extracted is fused with drone inertial measurement unit (IMU) data. The approach is tested in a warehouse and the performance is compared with other state-of-the-art image stitching algorithms. The proposed approach shows robust performance in cases of highly non-planar scenes.

Micro aerial vehicles (MAVs), an emerging class of aerial drones, are fast turning into high value mobile sensing assets. While MAVs have a large sensory gamut at their disposal; vision continues to dominate the external sensing scene, with limited usability in scenarios that offer acoustic clues. Therefore, we endeavor to provision a MAV auditory system (i.e., ears); and as part of this goal, our preliminary aim is to develop a robust acoustic localization system for detecting sound sources in the physical space-of-interest. However, devising this capability is extremely challenging due to strong ego-noise from the MAV propeller units, which is both wideband and non-stationary. It is well known that beamformers with large sensor arrays can overcome high noise levels; but in an attempt to cater to the platform (i.e., space, payload and computation) constraints of a MAV, we propose DroneEARS: a binaural sensing system for geo-locating sound sources. It combines the benefits of sparse (two elements) sensor array design (for meeting the platform constraints), and our proposed mobility-aided beamforming (for overcoming the severe ego-noise and its other complex characteristics) to significantly enhance the received signal-to-noise ratio (SNR). We demonstrate the efficacy of DroneEARS by empirical evaluations, and show that it provides a SNR improvement of 15-18 dB compared to many conventional and widely used techniques. This SNR gain translates to a source localization accuracy of approximately 40 cm within a scan region of 6m × 3m , that is, one order of magnitude better than competing methodologies.

Robotic automation has made significant inroads into industrial manufacturing and supply chains. With Industry 4.0 requirements proposing further autonomy to robotic participants, it is necessary to reason about robotic tasks within a knowledge dependent software framework. In this work, we model robotic automation tasks using hierarchical decomposition models, that are used to extract action plans to satisfy end goals. By abstracting components as intelligent agents that have perception, action, goal and knowledge base elements, we provide a reusable model to abstract robotic automation behavior. Through the use of the formal typed specification language Orc, implementations that confirm to the goal decomposition process are formulated. We demonstrate our techniques over Smart Warehouse deployments, with domain specific ontologies to ensure accurate type based descriptions of knowledge elements. This provides a generic framework for deploying intelligent automation systems across a host of industrial settings.

Selection of appropriate features is important for classification of urban materials using hyperspectral data. Urban materials lack dominant diagnostic absorption and hence features representing complete spectrum are likely to provide better classification performance. Furthermore, selection of appropriate features for a given data requires empirical assessment. In the present work, we introduce artificial color features that take into account complete spectrum. In addition to the color features, we use reflectance values of all the noise free wavelengths of EO-l Hyperion (set A), and a wavelength set H={445,576,638,759,1100, 1316, 1989} reported in literature. We classify EO-l Hyperion image of Pune city using multiple classifiers and compare their outcome. The color values, set A, and H provide similar results. Set H and color features results in minor drop of accuracies in urban classes such as industrial roofs and residential concrete roofs.

Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.

With a view of robotic path planning inside any cluttered and confined indoor environment, a-priori generation of convex free-spaces offers additional benefit to the motion planner. Traditionally the convex free-space grows from a seed, which either selected randomly or generated from a heuristic function. However arbitrary seeding cannot guarantee maximal free-space coverage. In addition, improper seeding may ignore a valuable free-space information (e.g., narrow corridor, broken window, partially closed door etc.), because of which planning might fail. In the current work, a logically Extended Iterative Regional Inflation by Semidefinite Programming (EIRIS) is proposed to address the problem. A modified occupancy grip map (MOGM) creates an approximate binary map of the environment. It is thereby used to identify the mutually isolated largest possible free-regions. For each free-region proper seeding (PS) algorithm allows the generated seed to inflate along a coordinate axis. Unlike IRIS where unguided growth results in random free-space generation, the greedy expansion of a seed is inhibited temporarily by treating all the free-regions as virtual obstacles other than the region of interest. It is a guided iterative inflation (GIl) algorithm. Regarding robotic path planning the convex free-space information needs to be contiguous and subsequently traditional graph search technique can be exploited for efficient path generation. But it is beyond the scope of present discussion and considered as a future work. However few typical paths are shown in the paper to emphasize the utility of free-space generation in prior of path planning. Simulation results with empirical analysis establish the improvement of proposed method (EIRIS) over the state-of-the-art approach.

Infrastructure detection and monitoring is a difficult task. Due to the advances in unmanned vehicles and image analytics, it is possible to decrease the human effort and achieve consistent results in infrastructure assessments using aerial image processing. Reliable detection and integrity checking of power infrastructure including conductor lines, pylons and insulators in a diverse background is the most challenging task in drone based automatic infrastructure monitoring. Most techniques in literature use first principle approach that tries to represent the image as features of interest. This paper proposes a deep learning approach for power infrastructure detection. Graph based post processing is applied for improving the outcomes of the generated deep model. A f-score of 75% is achieved using the deep model which is further improved using spectral clustering for the conductor lines, pylons and insulators that form the core parts of power infrastructure.

Remote sensing techniques are being increasingly used for periodic structural health monitoring of vast infrastructures such as power transmission systems. The current efforts concentrate on analysis of visual and other signals captured from the sensing devices, to diagnose the faults. Such data collection and analysis is expensive in terms of both computational overheads as well as towards robotic maneuvering of the data collection platform, such as a UAV. In this paper, we model the data gathering platform as an intelligent situated agent, and propose to autonomously control its data gathering and analysis activities through a cognitive cycle, to optimize the cost of efforts in identifying the faults that may exist. In this context, we explore use of less expensive qualitative reasoning with the background knowledge expressed as a Qualitative Bayesian Network (QBN). We introduce a reactive, economical planning algorithm around QBN that controls the sequence of data collection and analysis, much like how human inspectors do. We substantiate our claims with the results of simulation of the corresponding cognitive cycle.

Aerial drones (or flying robots) based inspection monitoring is fast turning into an high value asset for applications across sectors. Our focus in this work is to develop the "Ear" or the auditory sensing system of the drone so as to advance it sensing abilities beyond vision (the default external sensor) for it to better serve a wide range of inspection scenarios. However, devising this capability is extremely challenging due to severe acoustic self-interference from its rotor blades. In this work, we investigate the related challenges and overcome some of the initial hurdles by proposing a subspace based denoising algorithm, followed by its application in electricity power distribution systems.

The ubiquitous location sensing trend has increased the demand for low-cost GPS receivers; but their energy needs are still too high. For delay-tolerant applications, investigations show that significant energy saving can be obtained by offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS+. Based on the sparse decomposition model, it overcomes many limitations of SparseGPS [1] to yield better signal-to-noise ratio and detection accuracy; which translates to 30% more energy savings compared to the state-of-the-art.

The current 2G and 3G network infrastructures were not originally designed with multimedia transmission in consideration. Unlike data transmission, multimedia has significantly different Quality of Service requirements. It requires significantly higher bandwidth in comparison with data transmission. Multimedia transmission over handheld devices (such as mobile phones) poses further challenges in maintaining a good Quality of Experience as wireless networks are inherently of low bandwidth and relatively unreliable. Handheld devices are resource constraint devices and therefore huge processing at the handheld devices may introduce delay in the multimedia reception. In this paper, we present an algorithm for bandwidth aggregation for multimedia transmission from a handheld device using the available bandwidth of other handheld devices. We use a distributed approach to play a non-zero sum game among the handheld devices to minimize the end-to-end delay and maximize the throughput. We incorporate a cooperative game to dynamically add or delete other handheld devices based on the requirements of the network. We validate our work and demonstrate its benefits.

The Global Positioning System (GPS) receivers are now an integral part of smartphones. However, phone based GPS measurements display much less accuracy as compared to professional grade receivers. On the other hand, the deep penetration of smartphones in consumer market offers opportunity for customizing new solutions. One such possible application is targeted towards identifying risky driving profile for the purpose of customizing auto-insurance premium. For this to be successful, one needs to estimate the true vehicle speed. In this paper, we have presented a method to estimate the true speed of a moving vehicle derived solely from GPS measurements. In this case the accelerometer sensors are not used in conjunction with GPS measurement. The results are compared with OBD2 speed measurement. The proposed method computes a better estimate of vehicle speed, where correctness is measured relative to OBD2 measurement.

The suitability of using Qualitative Probabilistic Networks (QPN) for knowledge modeling and inference in Cognitive Wireless Networks is studied in this paper. This can be considered as a light weight approach compared to the complexity associated with the use of Bayesian Networks. This study brings out the advantages and issues involved in using QPN for modeling the dynamic behavior of wireless networks. Application and limitations of using QPN is illustrated with the modeling of a cognitive radio link and subsequently its performance while driving a link adaptation. The same methodology is extendable to model network layer behaviors as well.

Ubiquitous integration of high-speed WLANs with wide-range 3GPP systems results in the service extension of the backbone cellular network. This paper envisions such heterogeneous wireless network architecture by integrating IEEE 802.11p VANETs with 3GPP LTE to achieve seamless data connectivity for uninterrupted multimedia sessions amongst spatially-apart vehicular clusters. Issues on cluster head-based multicasting and QoS are explored in this paper. An adaptive multi-metric Cluster Head (CH) election mechanism is proposed to manage the VANET sub-clusters. In addition to this, construction of a 2-hop virtual overlay mesh-based shared multicast tree for lower-level multicasting within VANETs is discussed. Following this, the process of VANET-LTE upper-level communication is detailed, addressing the issues of CH and gateway handover, and resource allocation of the LTE eNB. The envisioned architecture enables the LTE to effectively schedule multimedia sessions based on the service requirements of the VANET gateways, thus satisfying QoS. Requisite simulation results are presented to evaluate the integrated network.

Complex Event Processing (CEP) has received wider acceptability due to its systematic and multilevel architecture driven concept approach. CEP is an emerging technology in the field of data processing and identifying patterns of interest from multiple streams of events. High levels of integrated self learning applications can be developed. CEP is used in development of applications which have to deal with voluminous streams of incoming data with the task of finding meaningful events or patterns of events, and respond to the events of interest in real time. In this paper a CEP based application for object detection tracking in a Wireless Sensor Network (WSN) environment is proposed. Also the detection of an intruder using semantic query processing is proposed. ESPER, an open source Complex Event Processing engine is used to develop the application.

In this paper, an efficient method for face recognition based on the Discrete Cosine Transform (DCT), Fisher Linear Discriminant (FLD) and classifier is presented. First, the dimensionality of the original face image is reduced using the DCT and illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. FLD is applied to the selected DCT coefficients to discriminate the invariant facial features. The KNN classifier is used for the recognition of the faces using the features extracted from the FLD. Simulation results show that the proposed system achieves better performance with high training and high recognition rate as well as very good illumination robustness.