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Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing mechanism to improve the sensing margin of a CIM architecture for boolean binary logic (BBL) operations. We generate reference signals using multiple STT-MRAM devices and place them strategically into the array such that these signals can address the variations and trace the wire parasitics effectively. We have demonstrated this behavior using an STT-MRAM model, which is calibrated using 1Mbit characterized array. Results show that our proposed architecture for binary neural networks (BNN) achieves up to 17.8 TOPS/W on the MNIST dataset and 130× performance improvement for the text encryption compared to the software implementation on Intel Haswell processor.

The aim of this work is to achieve smooth multiple parallel paths (lanes) confined within a bounded volume (corridor) through controlled airspace in urban scenarios. The problem is formulated as two subproblems: cross-section planning and corridor planning. The corridor cross-section is optimized for minimizing the corridor width for the required number of parallel paths while taking into account the downwash effects. Corridor planning utilizes a modified $A^{\ast}$ algorithm for waypoint generation in conjunction with a logistic curve based path for smoothly connecting these waypoints while accounting for curvature limits of individual paths. Waypoint heading angles are optimized to find minimum length corridor. The work provides an optimal solution of corridor planning with the capability of accommodating multiple UAVs simultaneously.

Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes. Rather than modifying the victim object with visible artifacts, we modify light that illuminates the object. We demonstrate how an attacker can craft a modulated light signal that adversarially illuminates a scene and causes targeted misclassifications on a state-of-the-art ImageNet deep learning model. Concretely, we exploit the radiometric rolling shutter effect in commodity cameras to create precise striping patterns that appear on images. To human eyes, it appears like the object is illuminated, but the camera creates an image with stripes that will cause ML models to output the attacker-desired classification. We conduct a range of simulation and physical experiments with LEDs, demonstrating targeted attack rates up to 84%.

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter.

Address matching is an important task for many businesses especially delivery and take out companies which help them to take out a certain address from their data warehouse. Existing solution uses similarity of strings, and edit distance algorithms to find out the similar addresses from the address database, but these algorithms could not work effectively with redundant, unstructured, or incomplete address data. This paper discuss semantic Address matching technique, by which we can find out a particular address from a list of possible addresses. We have also reviewed existing practices and their shortcoming. Semantic address matching is an essentially NLP task in the field of deep learning. Through this technique We have the ability to triumph the drawbacks of existing methods like redundant or abbreviated data problems. The solution uses the OCR on invoices to extract the address and create the data pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the best matching entries. Then to observe the best result, this will pass through BERT for giving the best possible result from the similar queries. Our investigation exhibits that our methodology enormously improves both accuracy and review of cutting-edge technology existing techniques.

This paper presents a predictive model to potentially identify high-risk COVID-19 infected patients based on easily analyzed circulatory blood markers. These findings can enable effective and efficient care programs for high-risk patients and periodic monitoring for the low-risk ones, thereby easing the hospital flow of patients and can further be utilized for hospital bed utilization assessment. The present machine learning-based SV-LAR model results in a high 87% f1 score, harmonic mean of 91% precision, and 83% recall to classify COVID-19, infected patients, as high-risk patients needing hospitalization.

As continuous-wave time-of-flight (C-ToF) cameras become popular in 3D imaging applications, they need to contend with the problem of multi-camera interference (MCI). In a multi-camera environment, a ToF camera may receive light from the sources of other cameras, resulting in large depth errors. In this paper, we propose stochastic exposure coding (SEC), a novel approach for mitigating. SEC involves dividing a camera's integration time into multiple slots, and switching the camera off and on stochastically during each slot. This approach has two benefits. First, by appropriately choosing the on probability for each slot, the camera can effectively filter out both the AC and DC components of interfering signals, thereby mitigating depth errors while also maintaining high signal-to-noise ratio. This enables high accuracy depth recovery with low power consumption. Second, this approach can be implemented without modifying the C-ToF camera's coding functions, and thus, can be used with a wide range of cameras with minimal changes. We demonstrate the performance benefits of SEC with theoretical analysis, simulations and real experiments, across a wide range of imaging scenarios.

We propose Micro-baseline Structured Light (MSL), a novel 3D imaging approach designed for small form-factor devices such as cell-phones and miniature robots. MSL operates with small projector-camera baseline and low-cost projection hardware, and can recover scene depths with computationally lightweight algorithms. The main observation is that a small baseline leads to small disparities, enabling a first-order approximation of the non-linear SL image formation model. This leads to the key theoretical result of the paper: the MSL equation, a linearized version of SL image formation. MSL equation is under-constrained due to two unknowns (depth and albedo) at each pixel, but can be efficiently solved using a local least squares approach. We analyze the performance of MSL in terms of various system parameters such as projected pattern and baseline, and provide guidelines for optimizing performance. Armed with these insights, we build a prototype to experimentally examine the theory and its practicality.

The depth resolution of a continuous-wave time-of-flight (CW-ToF) imaging system is determined by its coding functions. Recently, there has been growing interest in the design of new high-performance CW-ToF coding functions. However, these functions are typically designed in a hardware agnostic manner, i.e., without considering the practical device limitations, such as bandwidth, source power, digital (binary) function generation. Therefore, despite theoretical improvements, practical implementation of these functions remains a challenge. We present a constrained optimization approach for designing practical coding functions that adhere to hardware constraints. The optimization problem is non-convex with a large search space and no known globally optimal solutions. To make the problem tractable, we design an iterative, alternating least-squares algorithm, along with convex relaxation of the constraints. Using this approach, we design high-performance coding functions that can be implemented on existing hardware with minimal modifications. We demonstrate the performance benefits of the resulting functions via extensive simulations and a hardware prototype.

This paper presents techniques for tracking non-line-of-sight (NLOS) objects using speckle imaging. We develop a novel speckle formation and motion model where both the sensor and the source view objects only indirectly via a diffuse wall. We show that this NLOS imaging scenario is analogous to direct LOS imaging with the wall acting as a virtual, bare (lens-less) sensor. This enables tracking of a single, rigidly moving NLOS object using existing speckle-based motion estimation techniques. However, when imaging multiple NLOS objects, the speckle components due to different objects are superimposed on the virtual bare sensor image, and cannot be analyzed separately for recovering the motion of individual objects. We develop a novel clustering algorithm based on the statistical and geometrical properties of speckle images, which enables identifying the motion trajectories of multiple, independently moving NLOS objects. We demonstrate, for the first time, tracking individual trajectories of multiple objects around a corner with extreme precision (< 10 microns) using only off-the-shelf imaging components.

We propose a novel imaging method for near-complete, surround, 3D reconstruction of geometrically complex objects, in a single scan. The key idea is to augment a time-of-flight (ToF) based 3D sensor with a multi-mirror system, called a light-trap. The shape of the trap is chosen so that light rays entering it bounce multiple times inside the trap, thereby visiting every position inside the trap multiple times from various directions. We show via simulations that this enables light rays to reach more than 99.9% of the surface of objects placed inside the trap, even those with strong occlusions, for example, lattice-shaped objects. The ToF sensor provides the path length for each light ray, which, along with the known shape of the trap, is used to reconstruct the complete paths of all the rays. This enables performing dense, surround 3D reconstructions of objects with highly complex 3D shapes, in a single scan. We have developed a proof-of-concept hardware prototype consisting of a pulsed ToF sensor, and a light trap built with planar mirrors. We demonstrate the effectiveness of the light trap based 3D reconstruction method on a variety of objects with a broad range of geometry and reflectance properties.

Three dimensional imaging techniques have been widely used in both industry and academia. Time-of-flight (ToF) sensors offer a promising method of 3D imaging due to compact size and low complexity. However, state-of-the-art ToF sensors only have depth resolutions of centimeters due to limitations in the modulation frequencies that can be used. In this paper, we propose a technique to generate modulation frequencies as high as 1 THz using optical superheterodyne interferometry. Our proposed system provides great flexibility in imaging range and resolution. We experimentally demonstrate an increase in depth resolution by an order of magnitude relative to currently available commercial ToF cameras.

A library is considered as a central nervous system of any educational institution to expedite all its services either manually or by using ICT (Information communication technology) for the process of exchanging information. This paper discusses how the traditional libraries are slowly transforming in to virtual and digital libraries with the help of ICT and internet revolution. It explores various infrastructural requirements with diversified services, about how the e-learning alters to m-learning and their benefits to patrons. In the era of smart gadgets, the libraries and knowledge centres of today have to move with the pace of users e-tech demands. In the light of this background this paper analyses how library can reach to everyone's pocket with reference to Bundelkhand University library app.

Sequential pattern mining is one of the most important fields in data mining. The progressive sequential pattern mining problem explores the effect of deleting old data from the sequences in the database as well as adding new data to the sequences when sequential patterns are generated and hence generates the most accurate results in data mining applications. With the increasing amount of data, traditional algorithms running on uniprocessors have scalability troubles. We present a novel algorithm to optimize and scale the progressive mining of sequential patterns (PISA) problem on GPUs and achieve a speedup of more than 10× by efficiently utilizing coalesced memory accesses and SIMD execution on GPUs.

Sentiment analysis has been one of the most researched topics in Machine learning. The roots of sentiment analysis are in studies on public opinion analysis at the start of 20th century, but the outbreak of computer-based sentiment analysis only occurred with the availability of subjective text in Web. The task of generating effective sentence model that captures both syntactic and semantic relations has been the primary goal to make better sentiment analyzers. In this paper, we harness the power of deep convolution neural networks (DCNN) to model sentences and perform sentiment analysis. This approach automates the whole process otherwise done using advance NLP techniques. It is a modular approach analyzing syntactic and context based relation from word level to phrase level to sentence level and then to document level. Such approach not only stands outs in terms of better classification, it also fits the concept of transfer learning. We have achieved an accuracy of 80.69% using this technique and further working on the enhancement and refinement of this approach.

A very high efficiency Ultra wide band(UWB) hemispherical dielectric resonator antenna is simulated. The antenna is excited by commonly used feeding technique known as microstrip feeding. The dielectric resonator (DR) is inserted in the vertical edge of the substrate, which covers almost entire UWB bands(3.1 GHz to 10.6 GHz). A high impedance bandwidth of 3.74 GHz to 10.49 GHz is achieved in simulation. Compared to normal hemispherical dielectric resonator antenna, this design shows large volume reduction. A significant result analysis is also given in the paper.

Sensors that measure their motion with respect to the surrounding environment (ego-motion sensors) can be broadly classified into two categories. First is inertial sensors such as accelerometers. In order to estimate position and velocity, these sensors integrate the measured acceleration, which often results in accumulation of large errors over time. Second, camera-based approaches such as SLAM that can measure position directly, but their performance depends on the surrounding scene's properties. These approaches cannot function reliably if the scene has low frequency textures or small depth variations. We present a novel ego-motion sensor called SpeDo that addresses these fundamental limitations. SpeDo is based on using coherent light sources and cameras with large defocus. Coherent light, on interacting with a scene, creates a high frequency interferometric pattern in the captured images, called speckle. We develop a theoretical model for speckle flow (motion of speckle as a function of sensor motion), and show that it is quasi-invariant to surrounding scene's properties. As a result, SpeDo can measure ego-motion (not derivative of motion) simply by estimating optical flow at a few image locations. We have built a low-cost and compact hardware prototype of SpeDo and demonstrated high precision 6 DOF ego-motion estimation for complex trajectories in scenarios where the scene properties are challenging (e.g., repeating or no texture) as well as unknown.

Light field cameras allow us to digitally refocus a photograph after the time of capture. However, recording a light field requires either a significant loss in spatial resolution [11, 21, 10] or a large number of images to be captured [12]. In this paper, we propose incoherent holography for digital refocusing without loss of spatial resolution from only 3 captured images. The main idea is to capture 2D coherent holograms of the scene instead of the 4D light fields. The key properties of coherent light propagation are that the coherent spread function (hologram of a single point source) encodes scene depths and has a broadband spatial frequency response. These properties enable digital refocusing with 2D coherent holograms, which can be captured on sensors without loss of spatial resolution. Incoherent holography does not require illuminating the scene with high power coherent laser, making it possible to acquire holograms even for passively illuminated scenes. We provide an in-depth performance comparison between light field and incoherent holographic cameras in terms of the signal-to-noise-ratio (SNR). We show that given the same sensing resources, an incoherent holography camera outperforms light field cameras in most real world settings. We demonstrate a prototype incoherent holography camera capable of performing digital refocusing from only 3 acquired images. We show results on a variety of scenes that verify the accuracy of our theoretical analysis.

Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.

Strong ambient illumination severely degrades the performance of structured light based techniques. This is especially true in outdoor scenarios, where the structured light sources have to compete with sunlight, whose power is often 2-5 orders of magnitude larger than the projected light. In this paper, we propose the concept of light concentration to overcome strong ambient illumination. Our key observation is that given a fixed light (power) budget, it is always better to allocate it sequentially in several portions of the scene, as compared to spreading it over the entire scene at once. For a desired level of accuracy, we show that by distributing light appropriately, the proposed approach requires 1-2 orders lower acquisition time than existing approaches. Our approach is illumination-adaptive as the optimal light distribution is determined based on a measurement of the ambient illumination level. Since current light sources have a fixed light distribution, we have built a prototype light source that supports flexible light distribution by controlling the scanning speed of a laser scanner. We show several high quality 3D scanning results in a wide range of outdoor scenarios. The proposed approach will benefit 3D vision systems that need to operate outdoors under extreme ambient illumination levels on a limited time and power budget.

We consider the problem of shape recovery for real world scenes, where a variety of global illumination (inter-reflections, subsurface scattering, etc.) and illumination defocus effects are present. These effects introduce systematic and often significant errors in the recovered shape. We introduce a structured light technique called Micro Phase Shifting, which overcomes these problems. The key idea is to project sinusoidal patterns with frequencies limited to a narrow, high-frequency band. These patterns produce a set of images over which global illumination and defocus effects remain constant for each point in the scene. This enables high quality reconstructions of scenes which have traditionally been considered hard, using only a small number of images. We also derive theoretical lower bounds on the number of input images needed for phase shifting and show that Micro PS achieves the bound.

Cameras face a fundamental tradeoff between the spatial and temporal resolution - digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution.

Global illumination effects such as inter-reflections, diffusion and sub-surface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors caused by global illumination in structured light-based shape recovery. Based on this analysis, we design structured light patterns that are resilient to individual global illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows us to detect any residual errors that are corrected by acquiring a few additional images. Our techniques do not require explicit separation of the direct and global components of scene radiance and hence work even in scenarios where the separation fails or the direct component is too low. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results on a variety of scenes with complex shape and material properties and challenging global illumination effects.

Conventional low frame rate cameras result in blur and/or aliasing in images while capturing fast dynamic events. Multiple low speed cameras have been used previously with staggered sampling to increase the temporal resolution. However, previous approaches are inefficient: they either use small integration time for each camera which does not provide light benefit, or use large integration time in a way that requires solving a big ill-posed linear system. We propose coded sampling that address these issues: using N cameras it allows N times temporal superresolution while allowing ∼ N/2 times more light compared to an equivalent high speed camera. In addition, it results in a well-posed linear system which can be solved independently for each frame, avoiding reconstruction artifacts and significantly reducing the computational time and memory. Our proposed sampling uses optimal multiplexing code considering additive Gaussian noise to achieve the maximum possible SNR in the recovered video. We show how to implement coded sampling on off-the-shelf machine vision cameras. We also propose a new class of invertible codes that allow continuous blur in captured frames, leading to an easier hardware implementation.

Most active scene recovery techniques assume that a scene point is illuminated only directly by the illumination source. Consequently, global illumination effects due to inter-reflections, sub-surface scattering and volumetric scattering introduce strong biases in the recovered scene shape. Our goal is to recover scene properties in the presence of global illumination. To this end, we study the interplay between global illumination and the depth cue of illumination defocus. By expressing both these effects as low pass filters, we derive an approximate invariant that can be used to separate them without explicitly modeling the light transport. This is directly useful in any scenario where limited depth-of-field devices (such as projectors) are used to illuminate scenes with global light transport and significant depth variations. We show two applications: (a) accurate depth recovery in the presence of global illumination, and (b) factoring out the effects of defocus for correct direct-global separation in large depth scenes. We demonstrate our approach using scenes with complex shapes, reflectances, textures and translucencies.

The main objective of this paper is to explore the effectiveness of LP derived features and DCTC for the purpose of twins identification based on their speech samples. We propose features such as LPCC, LSF and DCTC in evaluating the performance of the system. The features LPCC and LSF are derived by performing LP analysis on speech segments of 16 msecs duration. For extracting DCTC, speech samples are transformed into DCT coefficients first and IDCT is performed on logarithm of DCT coefficients. These features are captured and quantized into M clusters representing L feature vectors by means of K-means clustering approach. Twins identification is done on the basis of finding distance between cluster centroids and feature vectors of the noisy test speech. Speaker is identified based on the minimum average distance between speaker models and feature vectors of noisy test speech. The proposed features are analyzed in this work and experimental results reveal the good performance of the system in terms of sub optimal and true success rates and also perform the comparative analysis between the proposed features.

People who struggle financially are often some of the most discriminating consumers; value for money takes on more urgency when resources are limited. This scarcity also leads to innovative solutions to everyday problems which can be leveraged by supporting them through use of ICT to create sustainable services/products. This research paper is set within the background of management of natural resources and their related environments in Rural India. It proposes a framework to support the emergence of a Knowledge Infrastructure that allows for creation of localized products/services. The infrastructure would model and document processes mapping the bricoleur approach to resource management prevalent in India to structured grids of information. With this objective, we provide for knowledge to be managed in ways that enable the benefits from bottom-up, and allow for geographically dispersed, incrementalist teams. The paper makes an attempt to look at existing structures, open-source methodologies and organizations that support rural innovation. It comments on how these can be leveraged using an Experience Management Framework to create more opportunities for innovation. The central lessons is that the mutual dependencies between different layers in the distributed team of the Enterprise involving farmers, technical experts, management, etc. enable continuous innovation in methods involved in any process. In conclusion, the paper objectifies the context of these innovations and attempts to emphasize the need for effective translation of methods from bricoleur tacit knowledge to a documented method for innovation.

In this paper, we present our latest work on facial expression analysis, synthesis and face recognition. The advent of new technologies that allow the capture of massive amounts of high resolution, high frame rate face data, leads us to propose data-driven face models that accurately describe the appearance of faces under unknown pose and illumination conditions as well as to track subtle geometry changes that occur during expressions. In this paper, we also demonstrate our results for expression transfer among different subjects. We reduce the dimensionality of our data onto a lower dimensional space manifold and then decompose it into style and content parameters. This allows us to transfer subtle expression information (in the form of a style vector) between individuals to synthesize new expressions, as well as smoothly morph geometry and motion. Finally, we demonstrate the accuracy of our face modeling methods through an integrated example of image-driven re-targeting and relighting of facial expressions, where transfer of expression and illumination information between different individuals is possible.

We present a novel fully automatic method for high resolution, nonrigid dense 3D point tracking. High quality dense point clouds of nonrigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. To use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient nonrigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. The novelty of this paper is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. While the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints guarantees that nonrigid deformations are accurately tracked as well. The harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. The map is stable, insensitive to resolution changes and is robust to noise. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. Our method is validated through a series of experiments demonstrating its accuracy and efficiency.