42 research outputs found
Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images
Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures.Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations.Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images.Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.Keywords: Ripplet, Directional discrete cosine transform (DDCT), Peak signal to noise ratio, MSE (mean square error), SC (structural content), MD (maximum difference), NCC (normalized cross correlatio
Do Language Models Know When They're Hallucinating References?
Current state-of-the-art language models (LMs) are notorious for generating
text with "hallucinations," a primary example being book and paper references
that lack any solid basis in their training data. However, we find that many of
these fabrications can be identified using the same LM, using only black-box
queries without consulting any external resources. Consistency checks done with
direct queries about whether the generated reference title is real (inspired by
Kadavath et al. 2022, Lin et al. 2022, Manakul et al. 2023) are compared to
consistency checks with indirect queries which ask for ancillary details such
as the authors of the work. These consistency checks are found to be partially
reliable indicators of whether or not the reference is a hallucination. In
particular, we find that LMs in the GPT-series will hallucinate differing
authors of hallucinated references when queried in independent sessions, while
it will consistently identify authors of real references. This suggests that
the hallucination may be more a result of generation techniques than the
underlying representation
Channel Estimation of FBMC Aux, FBMC and OFDM, based on BER and PAPR for 5G Systems
320-324In this generation of high-speed internet many research organizations are still busy finding the best candidate for the
upcoming next generation of cellular mobile communication. These days mobile communication is divided into two parts,
one is calling service and second is internet connectivity. Though many waveforms have been suggested for the fifth
generation of mobile communication but out of which getting a compared output results on the basis of channel estimation is
required. Through this research paper, FBMC Aux, FBMC, and OFDM are compared in terms of BER and PAPR to find our
best candidate for future 5th generation mobile and data communication. A comparison of the time index is also done to
verify the merits and demerits of the FBMC and FBMC Aux in this paper
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton