Abstract

Approximate Computing techniques are finding a central role in modern applications, by optimizing architectures to relax some computation but with a constrained inaccuracy. In many applications, the FFT algorithm is invariably applied and there is a need for approximate low energy hardware solutions to the FFT. The paper thus proposes an approximate, fixed-point, in-place, shared memory architecture for FFT. It is well observed that the energy at FFT I/Os is not strictly contiguous, hence the proposed FFT exploits this window to tune the error. The proposed FFT and its associated butterfly unit is constructed to efficiently incorporate approximate Toom–Cook multiplication. As said, a supporting function in error correction based on the sparsity patterns, is a feature of this design. The design synthesized at 32 n m shows on average, a 48.6% and 52.8% improvement in consumption of area and energy, respectively …

Abstract

Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose EI and UCB based BO algorithms and demonstrate their ability to outperform vanilla BO and the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.

Abstract

With the Integrated Circuits becoming pervasive in all key industries and applications, PUF’s has gained immense popularity for securing the IC’s by providing unique identification code to each chip. Designing a highly efficient PUF with optimal values of uniqueness and reliability is a significant challenge. Uniqueness depends on process variations during chip fabrication, and reliability depends on the chip’s ability to resist changes to supply voltage and temperature variations. Multiple PUF designs that employ reliability enhancement circuits and security algorithms achieve these design characteristics. Nonetheless, these techniques are design overheads. This paper presents a novel Physical Unclonable Function (PUF) based on the ProHys switch. It deals with hysteresis and proteresis mode of operation, which are complementary to each other. The Prohys PUF befittingly satisfies both uniqueness and reliability

Abstract

In the channel code detection problem, given a sequence of noise-affected codewords generated by an unknown code, the aim is to identify the correct channel code from the given set of potential codes. This problem has many applications in military spectrum surveillance and in cognitive radios. In this paper, we consider the situation when the set of potential channel codes consists of two linear block codes and propose a deep learning based classification approach for the corresponding code detection problem. In this work, we propose data processing strategies suitable for linear block codes that reduce the amount of data required to train the deep neural network classifier. This however comes at the cost of having a lower probability of detection. For the proposed data processing strategies, we analytically obtained the optimal probability of correct detection that one can hope to achieve using any neural …

Abstract

One of the key challenges for a novice engineer in a product company is to comprehend the product sufficiently and quickly. It can take anywhere from six months to several years for them to attain mastery but they need to start delivering results much before. SaaS (Software-as-a-Service) products have sophisticated system architecture which adds to the time and effort of understanding them. On the other hand, time available to new hires for product understanding continues to be short and getting shorter, given the pressure to deliver more in less time. Constructivist theory views learning as a personal process in which the learner constructs new knowledge for themselves. Building and refining a mental model is the key way in which they learn, similar to how the brain operates. This paper presents an approach to improve system comprehension process by using a system model that a) acts as a transitional object to aid and refine the mental model of the learner, and b) captures the current understanding of the dynamics of the software system in a way that can be reasoned with and simulated. We have adapted discrete systems modeling techniques and used a transition system as a lightweight modeling language. Such a model can be used by novice engineers during their product ramp-up phase to build a model of the software system that captures their knowledge of the system and aid their mental model. The paper also presents a learning approach in which the learners create and refine these models iteratively using the available and newly uncovered knowledge about the software system. We hypothesize that by leveraging this modeling

Abstract

Software practitioners use various requirement elicitation methods to produce a well-defined product. These methods impact the software product’s eventual traits and target a particular audience segment. Virtual Reality(VR) products are no different from this influence. With the notable rise in product offerings across various domains, VR has become an essential technology for the future. Nevertheless, the type of methods practiced for requirement elicitation still has not been thoroughly studied. This paper presents a mapping study on requirement elicitation methods practiced by VR practitioners in academia and industry. We consolidated our observations based on their popularity in the practitioner community. Further, we present our insights on the necessary and sufficient conditions to conduct VR requirement elicitation using the identified methods to benefit the VR practitioner community.

Abstract

Modeling Uncertainty Quantification of NDVI of Agricultural Fields through Bayesian Linear Regression in Time Series Prediction

Abstract

In Himalayas, the high-elevational mountains have warmed more rapidly in recent decades than other areas of the globe. Himalayan timberline is climate dependent and sensitive to changes, thus can provide biological proof of global warming. The present analysis changes in timberline elevations over four decades and corresponding climatic parameters (temperature and precipitation) in the Western Himalayan region (Himachal Pradesh). Landsat-2 Multispectral Scanner (MSS) and Landsat-8 were used to evaluate the long-term (1976–2015) timberline dynamics. The climate data APHRODITE was used to calculate the annual mean temperature and annual precipitation along the spatially distinct timberline locations. The mean elevation of timberline position has shifted vertically 145 m with a rate of ~37 m per decade (@ 3.7 m per year) over the past four decades, however majority of timberline remained

Abstract

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for

Abstract

Systems of differential equations with polynomial right-hand sides are very common in applications. In particular, when restricted to the positive orthant, they appear naturally (according to the law of mass-action kinetics) in ecology, population dynamics, as models of biochemical interaction networks, and models of the spread of infectious diseases. On the other hand, their mathematical analysis is very challenging in general; in particular, it is very difficult to answer questions about the long-term dynamics of the variables (species) in the model, such as questions about persistence and extinction. Even if we restrict our attention to mass-action systems, these questions still remain challenging. On the other hand, if a polynomial dynamical system has a weakly reversible single linkage class (WR1 ) realization, then its long-term dynamics is known to be remarkably robust: all the variables are persistent (i.e., no species goes extinct), irrespective of the values of the parameters in the model. Here we describe an algorithm for finding WR1 realizations of polynomial dynamical systems, whenever such realizations exist.