31 research outputs found
Programmable CMOS Analog-to-Digital Converter Design and Testability
In this work, a programmable second order oversampling CMOS delta-sigma analog-to-digital converter (ADC) design in 0.5µm n-well CMOS processes is presented for integration in sensor nodes for wireless sensor networks. The digital cascaded integrator comb (CIC) decimation filter is designed to operate at three different oversampling ratios of 16, 32 and 64 to give three different resolutions of 9, 12 and 14 bits, respectively which impact the power consumption of the sensor nodes. Since the major part of power consumed in the CIC decimator is by the integrators, an alternate design is introduced by inserting coder circuits and reusing the same integrators for different resolutions and oversampling ratios to reduce power consumption. The measured peak signal-to-noise ratio (SNR) for the designed second order delta-sigma modulator is 75.6dB at an oversampling ratio of 64, 62.3dB at an oversampling ratio of 32 and 45.3dB at an oversampling ratio of 16. The implementation of a built-in current sensor (BICS) which takes into account the increased background current of defect-free circuits and the effects of process variation on ΔIDDQ testing of CMOS data converters is also presented. The BICS uses frequency as the output for fault detection in CUT. A fault is detected when the output frequency deviates more than ±10% from the reference frequency. The output frequencies of the BICS for various model parameters are simulated to check for the effect of process variation on the frequency deviation. A design for on-chip testability of CMOS ADC by linear ramp histogram technique using synchronous counter as register in code detection unit (CDU) is also presented. A brief overview of the histogram technique, the formulae used to calculate the ADC parameters, the design implemented in 0.5µm n-well CMOS process, the results and effectiveness of the design are described. Registers in this design are replaced by 6T-SRAM cells and a hardware optimized on-chip testability of CMOS ADC by linear ramp histogram technique using 6T-SRAM as register in CDU is presented. The on-chip linear ramp histogram technique can be seamlessly combined with ΔIDDQ technique for improved testability, increased fault coverage and reliable operation
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
Neural radiance fields (NeRF) have achieved impressive performances in view
synthesis by encoding neural representations of a scene. However, NeRFs require
hundreds of images per scene to synthesize photo-realistic novel views.
Training them on sparse input views leads to overfitting and incorrect scene
depth estimation resulting in artifacts in the rendered novel views. Sparse
input NeRFs were recently regularized by providing dense depth estimated from
pre-trained networks as supervision, to achieve improved performance over
sparse depth constraints. However, we find that such depth priors may be
inaccurate due to generalization issues. Instead, we hypothesize that the
visibility of pixels in different input views can be more reliably estimated to
provide dense supervision. In this regard, we compute a visibility prior
through the use of plane sweep volumes, which does not require any
pre-training. By regularizing the NeRF training with the visibility prior, we
successfully train the NeRF with few input views. We reformulate the NeRF to
also directly output the visibility of a 3D point from a given viewpoint to
reduce the training time with the visibility constraint. On multiple datasets,
our model outperforms the competing sparse input NeRF models including those
that use learned priors. The source code for our model can be found on our
project page:
https://nagabhushansn95.github.io/publications/2023/ViP-NeRF.html.Comment: SIGGRAPH 202
SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions
Neural Radiance Fields (NeRF) show impressive performance for the
photorealistic free-view rendering of scenes. However, NeRFs require dense
sampling of images in the given scene, and their performance degrades
significantly when only a sparse set of views are available. Researchers have
found that supervising the depth estimated by the NeRF helps train it
effectively with fewer views. The depth supervision is obtained either using
classical approaches or neural networks pre-trained on a large dataset. While
the former may provide only sparse supervision, the latter may suffer from
generalization issues. As opposed to the earlier approaches, we seek to learn
the depth supervision by designing augmented models and training them along
with the NeRF. We design augmented models that encourage simpler solutions by
exploring the role of positional encoding and view-dependent radiance in
training the few-shot NeRF. The depth estimated by these simpler models is used
to supervise the NeRF depth estimates. Since the augmented models can be
inaccurate in certain regions, we design a mechanism to choose only reliable
depth estimates for supervision. Finally, we add a consistency loss between the
coarse and fine multi-layer perceptrons of the NeRF to ensure better
utilization of hierarchical sampling. We achieve state-of-the-art
view-synthesis performance on two popular datasets by employing the above
regularizations. The source code for our model can be found on our project
page: https://nagabhushansn95.github.io/publications/2023/SimpleNeRF.htmlComment: SIGGRAPH Asia 202
Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images
The challenge of graphically rendering high frame-rate videos on low compute
devices can be addressed through periodic prediction of future frames to
enhance the user experience in virtual reality applications. This is studied
through the problem of temporal view synthesis (TVS), where the goal is to
predict the next frames of a video given the previous frames and the head poses
of the previous and the next frames. In this work, we consider the TVS of
dynamic scenes in which both the user and objects are moving. We design a
framework that decouples the motion into user and object motion to effectively
use the available user motion while predicting the next frames. We predict the
motion of objects by isolating and estimating the 3D object motion in the past
frames and then extrapolating it. We employ multi-plane images (MPI) as a 3D
representation of the scenes and model the object motion as the 3D displacement
between the corresponding points in the MPI representation. In order to handle
the sparsity in MPIs while estimating the motion, we incorporate partial
convolutions and masked correlation layers to estimate corresponding points.
The predicted object motion is then integrated with the given user or camera
motion to generate the next frame. Using a disocclusion infilling module, we
synthesize the regions uncovered due to the camera and object motion. We
develop a new synthetic dataset for TVS of dynamic scenes consisting of 800
videos at full HD resolution. We show through experiments on our dataset and
the MPI Sintel dataset that our model outperforms all the competing methods in
the literature.Comment: To appear in ISMAR 2022; Project website:
https://nagabhushansn95.github.io/publications/2022/DeCOMPnet.htm
Low Light Video Enhancement by Learning on Static Videos with Cross-Frame Attention
The design of deep learning methods for low light video enhancement remains a
challenging problem owing to the difficulty in capturing low light and ground
truth video pairs. This is particularly hard in the context of dynamic scenes
or moving cameras where a long exposure ground truth cannot be captured. We
approach this problem by training a model on static videos such that the model
can generalize to dynamic videos. Existing methods adopting this approach
operate frame by frame and do not exploit the relationships among neighbouring
frames. We overcome this limitation through a selfcross dilated attention
module that can effectively learn to use information from neighbouring frames
even when dynamics between the frames are different during training and test
times. We validate our approach through experiments on multiple datasets and
show that our method outperforms other state-of-the-art video enhancement
algorithms when trained only on static videos
State Amplification Subject To Masking Constraints
This paper considers a state dependent broadcast channel with one
transmitter, Alice, and two receivers, Bob and Eve. The problem is to
effectively convey ("amplify") the channel state sequence to Bob while
"masking" it from Eve. The extent to which the state sequence cannot be masked
from Eve is referred to as leakage. This can be viewed as a secrecy problem,
where we desire that the channel state itself be minimally leaked to Eve while
being communicated to Bob. The paper is aimed at characterizing the trade-off
region between amplification and leakage rates for such a system. An achievable
coding scheme is presented, wherein the transmitter transmits a partial state
information over the channel to facilitate the amplification process. For the
case when Bob observes a stronger signal than Eve, the achievable coding scheme
is enhanced with secure refinement. Outer bounds on the trade-off region are
also derived, and used in characterizing some special case results. In
particular, the optimal amplification-leakage rate difference, called as
differential amplification capacity, is characterized for the reversely
degraded discrete memoryless channel, the degraded binary, and the degraded
Gaussian channels. In addition, for the degraded Gaussian model, the extremal
corner points of the trade-off region are characterized, and the gap between
the outer bound and achievable rate-regions is shown to be less than half a bit
for a wide set of channel parameters.Comment: Revised versio
Test Time Adaptation for Blind Image Quality Assessment
While the design of blind image quality assessment (IQA) algorithms has
improved significantly, the distribution shift between the training and testing
scenarios often leads to a poor performance of these methods at inference time.
This motivates the study of test time adaptation (TTA) techniques to improve
their performance at inference time. Existing auxiliary tasks and loss
functions used for TTA may not be relevant for quality-aware adaptation of the
pre-trained model. In this work, we introduce two novel quality-relevant
auxiliary tasks at the batch and sample levels to enable TTA for blind IQA. In
particular, we introduce a group contrastive loss at the batch level and a
relative rank loss at the sample level to make the model quality aware and
adapt to the target data. Our experiments reveal that even using a small batch
of images from the test distribution helps achieve significant improvement in
performance by updating the batch normalization statistics of the source model.Comment: Accepted to ICCV 202