arXiv subjects

Mohit Kumar

Publications and source records attributed to Mohit Kumar.

67 records · Page 4Linked to original sources

arXiv2020

Coding schemes and Applications for Weather Radars

In this paper, we describe the evolution of a pair of polyphase coded waveform for use in second trip suppression in weather radar. The polyphase codes were designed and tested on NASA weather radar. The NASA dual-frequency, dual-polarization Doppler radar (D3R) was developed primarily as a ground validation tool for the GPM satellite dual-frequency radar. Recently, the D3R radar was upgraded with new versions of digital receiver hardware and firmware, which supports larger filter lengths and multiple phase coded waveforms, and also newer IF sub-systems. This has enhanced the capabilities of radar manifolds.

eess.SP↗

arXiv2019

Intra-Pulse Polyphase Coding System for Second Trip Suppression in a Weather Radar

This paper describes the design and implementation of intra-pulse polyphase codes for a weather radar system. Algorithms to generate codes with good correlation properties are discussed. Thereafter, a new design framework is described, which optimizes the polyphase code and corresponding mismatched filter, using a cost/error function, especially for weather radars. It establishes the performance of these intra-pulse techniques with specific application towards second trip removal. The developed code is implemented on NASA D3R, which is a dual-frequency, dual-polarization, Doppler weather radar system.

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arXiv2019

Microwave Photonic Exciter Unit for Radar System

Modern radar systems require low phase noise and long term phase-stable radar carrier signals for high-resolution imaging, micro-Doppler signatures applications. High stable signal generation through conventional crystal followed by frequency multiplier techniques are limited phase noise performance under vibration conditions. Optoelectronic oscillators (OEOs) offer low phase noise and long-term phase stability compared to the conventional oscillators. In this design of the Radar Exciter unit based on microwave photonics components viz., OEOs, frequency dividers, optical filters, optical delay lines, optical arbitrary waveform generators are discussed. The microwave photonics offers frequency-independent Exciter design with tunable filters and tunable laser sources. In this paper the performance comparison of microwave photonic-based exciter unit also discussed.

eess.SP↗

arXiv2019

Distributed High Speed Optical Network for Digital Radar Systems

Modern Digital radar systems with multiple digital beamforming capability are built of a large number of receivers and requires high-speed data interface links for transmission of receiver baseband data to processor units. High data throughput (>250Mbyte/sec) from typical eight-channel receivers will be transmitted to Digital beamformer over high-speed serial interface links over an optical channel. Currently for digital radar systems with sub-array level beam former distribution of receiver data is through point to point optical interface links. For the modern element-level digital beamforming radars the distribution of baseband data increases the design complexity. In this paper novel scheme of usage of distributed optical interface network is discussed using high-speed optical transport networks, FPGAs as well as distributed techniques to over the above problem. The recent advances in optical communication and feature of FPGA devices are utilized in the implementation of optical distribution networks and these schemes are covered in this paper.

eess.SP↗

arXiv2019

A Novel Scheme of Digital Instantaneous Automatic Gain Control (DIAGC) for Pulse Radars

Several schemes for gain control are used for preventing saturation of receiver, and overloading of data processor, tracker or display in pulse radars. The use of digital processing techniques open the door to a variety of digital automatic gain control schemes for analyzing digitized return signals and controlling receiver gain only at saturating clutter zones without affecting the detection at other zones. In this paper, we present a novel scheme of Digital Instantaneous Automatic Gain Control (DIAGC) which is based on storing digitally the dwell based clutter returns and deriving the gain control. The returns corresponding to the first two PRTs in a dwell are used to analyze the presence of saturating clutter zones and the depth of saturation. Third PRT onwards proper gain control is applied at the IF stage to prevent saturation of the following stages. FPGA based scheme is used for digital data processing, storing, threshold calculation and gain control generation. The effect of DIAGC on pulse compression is also addressed in this paper.

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arXiv2019

Receive signal path design for Active phased array radars

Modern Active Phased array Radar systems with a large number of T/R modules, multi-channel receiver down converters and distributed power distribution networks leads to design and analysis of the receive signal path more complex. In this paper receive signal path design of a typical 1000 T/R modules based fully distributed active phased array radar is discussed in detail including the gain, Spurious Free Dynamic Range (SFDR) requirements at different levels. The techniques for optimization of SFDR and system performance also described along with the Systemvue model to receive path calculations is presented.

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arXiv2019

Finite State Markov Modeling of Fading Channels Towards Decoding of LDPC Codes

Here we have proposed two decoding strategies of low-density parity-check (LDPC) codes over Markov noise channels with bit flipping noise. The sum-product algorithm used for decoding LDPC codes over memoryless channels is extended to include channel estimation and how much gain we obtain by doing so is simulated and verified. LDPC codes have been studied for years over memoryless channels and are known to have excellent performance. However, these codes over channels with memory is the topic of current research. Here, channels with memory are characterized by Markov modeling which is a useful busty channel model. With sufficient no. of states, they are able to model sufficient noise characteristics. We have gone for a two-state system as it shows a good compromise between complexity and performance.

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arXiv2018

Did We Get It Right? Predicting Query Performance in E-commerce Search

In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. This is a critical question in evaluating any e-commerce search engine. While this question is traditionally answered using simple metrics like query click-through rate (CTR), we observe that in e-commerce search, such metrics can be misleading. Upon inspection, we find cases where CTR is high but the results are poor and vice versa. Similar cases exist for other metrics like time to click which are often also used for evaluating search engines. We aim to learn the quality of the results served by the search engine based on users' interactions with the results. Although this problem has been studied in the web search context, this is the first study for e-commerce search, to the best of our knowledge. Despite certain commonalities with evaluating web search engines, there are several major differences such as underlying reasons for search failure, and availability of rich user interaction data with products (e.g. adding a product to the cart). We study large-scale user interaction logs from Flipkart's search engine, analyze behavioral patterns and build models to classify queries based on user behavior signals. We demonstrate the feasibility and efficacy of such models in accurately predicting query performance. Our classifier is able to achieve an average AUC of 0.75 on a held-out test set.

cs.IR↗

arXiv2018

Automating Personnel Rostering by Learning Constraints Using Tensors

Many problems in operations research require that constraints be specified in the model. Determining the right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learning principles. So far there has been only little work on learning constraints within the operations research community. We focus on personnel rostering and scheduling problems in which there are often past schedules available and show that it is possible to automatically learn constraints from such examples. To realize this, we adapted some techniques from the constraint programming community and we have extended them in order to cope with multidimensional examples. The method uses a tensor representation of the example, which helps in capturing the dimensionality as well as the structure of the example, and applies tensor operations to find the constraints that are satisfied by the example. To evaluate the proposed algorithm, we used constraints from the Nurse Rostering Competition and generated solutions that satisfy these constraints; these solutions were then used as examples to learn constraints. Experiments demonstrate that the proposed algorithm is capable of producing human readable constraints that capture the underlying characteristics of the examples.

cs.AI↗

arXiv2018

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.

stat.ML↗

arXiv2017

FairJudge: Trustworthy User Prediction in Rating Platforms

Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80% accuracy). The FairJudge algorithm is already being deployed at Flipkart.

cs.SI↗

arXiv2016

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, including 1) they occur in short bursts of time; 2) fraudulent user accounts have skewed rating distributions. However, these may both be true in any given dataset. Hence, in this paper, we propose an approach for detecting fraudulent reviews which combines these 2 approaches in a principled manner, allowing successful detection even when one of these signs is not present. To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior. Based on our model we formulate a likelihood-based suspiciousness metric, Normalized Expected Surprise Total (NEST). We propose a linear-time algorithm for performing Bayesian inference using our model and computing the metric. Experiments on real data show that BIRDNEST successfully spots review fraud in large, real-world graphs: the 50 most suspicious users of the Flipkart platform flagged by our algorithm were investigated and all identified as fraudulent by domain experts at Flipkart.

cs.AI↗

arXiv2015

EdgeCentric: Anomaly Detection in Edge-Attributed Networks

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this information to discern suspicious from typical node behavior. Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new problem of leveraging edge information, (b) methodology: we introduce EdgeCentric, an intuitive and scalable compression-based approach for detecting edge-attributed graph anomalies, and (c) practicality: we show that EdgeCentric successfully spots numerous such anomalies in several large, edge-attributed real-world graphs, including the Flipkart e-commerce graph with over 3 million product reviews between 1.1 million users and 545 thousand products, where it achieved 0.87 precision over the top 100 results.

cs.SI↗