arXiv subjects

Saurabh Singh

Publications and source records attributed to Saurabh Singh.

At least 73 records · Page 4Linked to original sources

arXiv2021

Methods of Error Estimation for Delay Power Spectra in $21\,\textrm{cm}$ Cosmology

Precise measurements of the 21 cm power spectrum are crucial for understanding the physical processes of hydrogen reionization. Currently, this probe is being pursued by low-frequency radio interferometer arrays. As these experiments come closer to making a first detection of the signal, error estimation will play an increasingly important role in setting robust measurements. Using the delay power spectrum approach, we have produced a critical examination of different ways that one can estimate error bars on the power spectrum. We do this through a synthesis of analytic work, simulations of toy models, and tests on small amounts of real data. We find that, although computed independently, the different error bar methodologies are in good agreement with each other in the noise-dominated regime of the power spectrum. For our preferred methodology, the predicted probability distribution function is consistent with the empirical noise power distributions from both simulated and real data. This diagnosis is mainly in support of the forthcoming HERA upper limit, and also is expected to be more generally applicable.

astro-ph.IM↗

arXiv2021

3D Scene Compression through Entropy Penalized Neural Representation Functions

Some forms of novel visual media enable the viewer to explore a 3D scene from arbitrary viewpoints, by interpolating between a discrete set of original views. Compared to 2D imagery, these types of applications require much larger amounts of storage space, which we seek to reduce. Existing approaches for compressing 3D scenes are based on a separation of compression and rendering: each of the original views is compressed using traditional 2D image formats; the receiver decompresses the views and then performs the rendering. We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints. The function is implemented as a neural network and jointly trained for reconstruction as well as compressibility, in an end-to-end manner, with the use of an entropy penalty on the parameters. Our method significantly outperforms a state-of-the-art conventional approach for scene compression, achieving simultaneously higher quality reconstructions and lower bitrates. Furthermore, we show that the performance at lower bitrates can be improved by jointly representing multiple scenes using a soft form of parameter sharing.

cs.CV↗

arXiv2021

Validation of the HERA Phase I Epoch of Reionization 21 cm Power Spectrum Software Pipeline

We describe the validation of the HERA Phase I software pipeline by a series of modular tests, building up to an end-to-end simulation. The philosophy of this approach is to validate the software and algorithms used in the Phase I upper limit analysis on wholly synthetic data satisfying the assumptions of that analysis, not addressing whether the actual data meet these assumptions. We discuss the organization of this validation approach, the specific modular tests performed, and the construction of the end-to-end simulations. We explicitly discuss the limitations in scope of the current simulation effort. With mock visibility data generated from a known analytic power spectrum and a wide range of realistic instrumental effects and foregrounds, we demonstrate that the current pipeline produces power spectrum estimates that are consistent with known analytic inputs to within thermal noise levels (at the 2 sigma level) for k > 0.2 h/Mpc for both bands and fields considered. Our input spectrum is intentionally amplified to enable a strong `detection' at k ~0.2 h/Mpc -- at the level of ~25 sigma -- with foregrounds dominating on larger scales, and thermal noise dominating at smaller scales. Our pipeline is able to detect this amplified input signal after suppressing foregrounds with a dynamic range (foreground to noise ratio) of > 10^7. Our validation test suite uncovered several sources of scale-independent signal loss throughout the pipeline, whose amplitude is well-characterized and accounted for in the final estimates. We conclude with a discussion of the steps required for the next round of data analysis.

astro-ph.IM↗

arXiv2021

A Floating Octave Bandwidth Cone-Disc Antenna for Detection of Cosmic Dawn

The critical component of radio astronomy radiometers built to detect redshifted 21-cm signals from Cosmic Dawn is the antenna element. We describe the design and performance of an octave bandwidth cone disc antenna built to detect this signal in the band 40 to 90 MHz. The Cosmic Dawn signal is predicted to be a wideband spectral feature orders of magnitude weaker than sky and ground radio brightness. Thus, the engineering challenge is to design an antenna at low frequencies that is able to provide with high fidelity the faint cosmological signal, along with foreground sky, to the receiver. The antenna characteristics must not compromise detection by imprinting any confusing spectral features on the celestial radiation, ground emission or receiver noise. An innovation in the present design is making the antenna electrically smaller than half wavelength and operating it on the surface of a sufficiently large water body. The homogeneous and high permittivity medium beneath the small cone-disc antenna results in an achromatic beam pattern, high radiation efficiency and minimum unwanted confusing spectral features. The antenna design was optimized in WIPL-D and FEKO. A prototype was constructed and deployed on a lake to validate its performance with field measurements. Index Terms: Antenna measurements, radio astronomy, reflector antennas.

astro-ph.IM↗

arXiv2021

SARAS 3 CD/EoR Radiometer: Design and Performance of the Receiver

SARAS is an ongoing experiment aiming to detect the redshifted global 21-cm signal expected from Cosmic Dawn (CD) and the Epoch of Reionization (EoR). Standard cosmological models predict the signal to be present in the redshift range $z \sim $6--35, corresponding to a frequency range 40--200~MHz, as a spectral distortion of amplitude 20--200~mK in the 3~K cosmic microwave background. Since the signal might span multiple octaves in frequency, and this frequency range is dominated by strong terrestrial Radio Frequency Interference (RFI) and astrophysical foregrounds of Galactic and Extragalactic origin that are several orders of magnitude greater in brightness temperature, design of a radiometer for measurement of this faint signal is a challenging task. It is critical that the instrumental systematics do not result in additive or multiplicative confusing spectral structures in the measured sky spectrum and thus preclude detection of the weak 21-cm signal. Here we present the system design of the SARAS~3 version of the receiver. New features in the evolved design include Dicke switching, double differencing and optical isolation for improved accuracy in calibration and rejection of additive and multiplicative systematics. We derive and present the measurement equations for the SARAS~3 receiver configuration and calibration scheme, and provide results of laboratory tests performed using various precision terminations that qualify the performance of the radiometer receiver for the science goal.

astro-ph.IM↗

arXiv2021

SARAS CD/EoR Radiometer: Design and performance of the Digital Correlation Spectrometer

In the currently accepted model for cosmic baryon evolution, Cosmic Dawn and the Epoch of Reionization are significant times when first light from the first luminous objects emerged, transformed and subsequently ionized the primordial gas. The 21 cm hyperfine transition of neutral hydrogen, redshifted from these cosmic times to a frequency range of 40 to 200 MHz, has been recognized as an important probe of the physics of CD/EoR. The global 21-cm signal is predicted to be a spectral distortion of a few 10's to a few 100's of mK, which is expected to be present in the cosmic radio background as a trace additive component. SARAS, Shaped Antenna measurement of the background RAdio Spectrum, is a spectral radiometer purpose designed to detect the weak 21-cm signal from CD/EoR. An important subsystem of the radiometer, the digital correlation spectrometer, is developed around a high speed digital signal processing platform called pSPEC. pSPEC is built around two quad 10 bit analog-to-digital converters and a Virtex 6 field programmable gate array, with provision for multiple Gigabit Ethernet and 4.5 Gbps fibre optic interfaces. Here we describe the system design of the digital spectrometer, the pSPEC board, and the adaptation of pSPEC to implement a high spectral resolution of about 61 kHz, high dynamic range correlation spectrometer covering the entire CD/EoR band. As the SARAS radiometer is required to be deployed in remote locations where terrestrial radio frequency interference is a minimum, the spectrometer is designed to be compact, portable and operating off internal batteries. The paper includes an evaluation of the spectrometer's susceptibility to radio frequency interference and capability to detect signals from CD/EoR.

astro-ph.IM↗

arXiv2021

Capacitor Type Thin-Film Heat Flow Switching Device

We developed a capacitor type heat flow switching device, in which electron thermal conductivity of the electrodes is actively controlled through the carrier concentration varied by an applied bias voltage. The devices consist of an amorphous p-type Si-Ge-Au alloy layer, an amorphous SiO$_2$ as the dielectric layer, and a n-type Si substrate. Both amorphous materials are characterized by very low lattice thermal conductivity, less than 1 Wm-1K-1. The Si-Ge-Au amorphous layer with 40 nm in thickness was deposited by means of molecular beam deposition technique on the 100 nm thick SiO$_2$ layer formed at the top surface of Si substrate. Bias voltage-dependent thermal conductivity and heat flow density of the fabricated device were evaluated by a time-domain thermoreflectance method at room temperature. Consequently, we observed a 55 percent increase in thermal conductivity.

physics.app-ph↗

arXiv2021

Selective Enhancement in Phonon Scattering leads to High Thermoelectric Figure of Merit in ZnO -- Graphene Oxide Core-shell Nanohybrids

ZnO is a promising candidate as an environment friendly thermoelectric (TE) material. However, the poor TE figure of merit (zT) needs to be addressed to achieve significant TE efficiency for commercial applications. Here we demonstrate that selective enhancement in phonon scattering leads to increase in zT of RGO encapsulated Al-doped ZnO core shell nanohybrids, synthesized via a facile and scalable method. The incorporation of 1 at% Al with 1.5 wt% RGO into ZnO (AGZO) has been found to show significant enhancement in zT (=0.52 at 1100 K) which is an order of magnitude larger compared to that of bare undoped ZnO. Photoluminescence and X-ray photoelectron spectroscopy measurements confirm that RGO encapsulation significantly quenches surface oxygen vacancies in ZnO along with nucleation of new interstitial Zn donor states. Tunneling spectroscopy reveals that the band gap of ~ 3.4 eV for bare ZnO reduces effectively to ~ 0.5 eV upon RGO encapsulation, facilitating charge transport. The electrical conductivity enhancement also benefits from the more than 95% densification achieved, using the spark plasma sintering method, which aids reduction of GO into RGO. The same Al doping and RGO capping synergistically brings about drastic reduction of thermal conductivity, through enhanced phonon-phonon and point defect-phonon scatterings. These opposing effects on electrical and thermal conductivities enhances the power factors as well as the zT value. Overall, a practically viable route for synthesis of oxide - RGO TE material which could find its practical applications for the high-temperature TE power generation.

cond-mat.mtrl-sci↗

arXiv2020

DAYENU: A Simple Filter of Smooth Foregrounds for Intensity Mapping Power Spectra

We introduce DAYENU, a linear, spectral filter for HI intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modeling of the underlying data. Beyond 21 cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21 cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered DFT estimators. Since these modes have the largest SNRs, DAYENU significantly increases the sensitivity of 21 cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEANing (DAYENUREST). We refer readers to the Code section at the end of this paper for links to examples and code.

astro-ph.CO↗

arXiv2020

Nonlinear Transform Coding

We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate--distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate--distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate--distortion trade-off of nonlinear transforms, introducing a simplified one.

cs.IT↗

arXiv2020

A Review on Cyber Crimes on the Internet of Things

Internet of Things (IoT) devices are rapidly becoming universal. The success of IoT cannot be ignored in the scenario today, along with its attacks and threats on IoT devices and facilities are also increasing day by day. Cyber attacks become a part of IoT and affecting the life and society of users, so steps must be taken to defend cyber seriously. Cybercrimes threaten the infrastructure of governments and businesses globally and can damage the users in innumerable ways. With the global cybercrime damages predicted to cost up to 6 trillion dollars annually on the global economy by cyber crime. Estimated of 328 Million Dollar annual losses with the cyber attacks in Australia itself. Various steps are taken to slow down these attacks but unfortunately not able to achieve success properly. Therefor secure IoT is the need of this time and understanding of attacks and threats in IoT structure should be studied. The reasons for cyber-attacks can be Countries having week cyber securities, Cybercriminals use new technologies to attack, Cybercrime is possible with services and other business schemes. MSP (Managed Service Providers) face different difficulties in fighting with Cyber-crime. They have to ensure that security of the customer as well as their security in terms of their servers, devices, and systems. Hence, they must use effective, fast, and easily usable antivirus and antimalware tools.

cs.CY↗

arXiv2020

Thermoelectric Properties of GaN with Carrier Concentration Modulation: An Experimental and Theoretical Investigation

The present work investigates the less explored thermoelectric properties of n-type GaN semiconductors by combined both the experimental and computational tools. Seebeck coefficients of epitaxial thin films of GaN were experimentally measured in the wide temperature range from 77 K to 650 K in steps of ~10 K covering both low and high-temperature regimes as a function of carrier concentration 2 x1016, 2 x 1017, 4 x 1017 and 8 x 1017 cm-3. The measured Seebeck coefficient at room temperature was found to be highest, -374 microV/K, at the lowest concentration of 4 x 1016 cm-3 and decreases in magnitude monotonically (-327.6 microV/K, -295 microV/K, -246 microV/K for 2 x 1017, 4 x 1017, 8 x 1017 cm-3, respectively) as the carrier concentration of samples increases. Seebeck coefficient remains negative in the entire temperature range under study indicate that electrons are dominant carriers. To understand the temperature-dependent behavior, we have also carried out the electronic structure, and transport coefficients calculations by using Tran-Blaha modified Becke-Johnson (TB-mBJ) potential, and semiclassical Boltzmann transport theory implemented in WIEN2k and BoltzTraP code, respectively. The experimentally observed carrier concentrations were used in the calculations. The estimated results obtained under constant relaxation time approximations provide a very good agreement between theoretical and experimental data of Seebeck coefficients in the temperature range of 260 to 625 K.

cond-mat.mtrl-sci↗

arXiv2020

An Approximate Carry Estimating Simultaneous Adder with Rectification

Approximate computing has in recent times found significant applications towards lowering power, area, and time requirements for arithmetic operations. Several works done in recent years have furthered approximate computing along these directions. In this work, we propose a new approximate adder that employs a carry prediction method. This allows parallel propagation of the carry allowing faster calculations. In addition to the basic adder design, we also propose a rectification logic which would enable higher accuracy for larger computations. Experimental results show that our adder produces results 91.2% faster than the conventional ripple-carry adder. In terms of accuracy, the addition of rectification logic to the basic design produces results that are more accurate than state-of-the-art adders like SARA and BCSA by 74%.

cs.AR↗

arXiv2020

Wide range temperature-dependent (80-630 K) study of Hall effect and the Seebeck coefficient of \b{eta}-Ga2O3 single crystals

Investigation of Seebeck coefficient in ultra-wide bandgap materials presents a challenge in measurement, nevertheless, it is essential for understanding fundamental transport mechanisms involved in electrical and thermal conduction. \b{eta}-Ga2O3 is a strategic material for high power optoelectronic applications. Present work reports Seebeck coefficient measurement for single crystal Sn doped \b{eta}-Ga2O3 in a wide temperature range (80-630 K). The non-monotonic trend with large magnitude and negative sign in the entire temperature range shows electrons are dominant carriers. The structural and Raman characterization confirms the single-phase and presence of low, mid, and high-frequency phonon modes, respectively. Temperature dependent (90-350 K) Hall effect measurement was carried out as supplementary study. Hall mobility showed T1.12 for T less than 135 K and T-0.70 for T more than 220 K. Activation energies from Seebeck coefficient and conductivity analysis revealed presence of inter band conduction due to impurity defects. The room temperature Seebeck coefficient, power factor and thermal conductivity were found as 68.57 microV/K, 0.15 microW/K2cm and 14.2 W/mK, respectively. The value of the figure-of-merit for \b{eta}-Ga2O3 was found to be aprox. 0.01 (300 K).

cond-mat.mtrl-sci↗

arXiv2020

End-to-end Learning of Compressible Features

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf feature generators and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression, while general purpose lossy compression methods based on energy compaction (e.g. PCA followed by quantization and entropy coding) are sub-optimal, as they are not tuned to task specific objective. We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that our method produces features that are an order of magnitude more compressible, while having a regularization effect that leads to a consistent improvement in accuracy.

cs.CV↗

arXiv2020

Channel-wise Autoregressive Entropy Models for Learned Image Compression

In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation. Forward adaptation makes use of side information and can be efficiently integrated into a deep neural network. In contrast, backward adaptation typically makes predictions based on the causal context of each symbol, which requires serial processing that prevents efficient GPU / TPU utilization. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance than existing context-adaptive models while minimizing serial processing. Empirically, we see an average rate savings of 6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a context-adaptive baseline model. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%.

eess.IV↗

arXiv2020

Deep Implicit Volume Compression

We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly compress the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algorithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively reducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.

eess.IV↗

arXiv2020

PatchVAE: Learning Local Latent Codes for Recognition

Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs). However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervised learning for recognition. Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data. Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level. Our key contribution is a bottleneck formulation that encourages mid-level style representations in the VAE framework. Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs.

cs.CV↗