A novel image compression algorithm for high resolution 3D reconstruction

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models

A novel 2D image compression algorithm based on two levels DWT and DCT transforms with enhanced minimize-matrix-size algorithm for high resolution structured light 3D surface reconstruction

Image compression techniques are widely used in 2D and 3D image and video sequences. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level Discrete Wavelet Transform (DWT) and a two level Discrete Cosine Transform (DCT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of 4 steps: 1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix respectively; 2) apply a second level DCT to the DC-Matrix to generate two arrays, namely nonzero-array and zero-array; 3) apply the Minimize-Matrix-Size (MMS) algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT; 4) apply arithmetic coding to the output of previous steps. A novel Fast-Match-Search (FMS) decompression algorithm is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined into one matrix followed by inverse two level DCT with two level DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D RMSE following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.</jats:p

The problematic backreaction of SUSY-breaking branes

In this paper we investigate the localisation of SUSY-breaking branes which, in the smeared approximation, support specific non-BPS vacua. We show, for a wide class of boundary conditions, that there is no flux vacuum when the branes are described by a genuine delta-function. Even more, we find that the smeared solution is the unique solution with a regular brane profile. Our setup consists of a non-BPS AdS_7 solution in massive IIA supergravity with smeared anti-D6-branes and fluxes T-dual to ISD fluxes in IIB supergravity.Comment: 27 pages, Latex2e, 5 figure

Simultaneous storage of medical images in the spatial and frequency domain: A comparative study

BACKGROUND: Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads. METHODS: The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example. RESULTS: It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain. CONCLUSION: The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient

Systemic Risk and the Ripple Effect in the Supply Chain

Supply chains are highly complex systems, and disruptions may ripple through these systems in unexpected ways, but they may also start in unexpected ways. We investigate the causes of ripple effect through the lens of systemic risk. We derive supply chain systemic risk from the finance discipline where sources of risk are found in systemic risk-taking, contagion, and amplification mechanisms. In a supply chain context, we identify three dimensions that influence systemic risk, the nature of a disruption, the structure, and dependency of the supply chain, and the decision-making. Within these three dimensions, there are several factors including correlation of risk, compounding effects, cyclical linkages, counterparty risk, herding behavior, and misaligned incentives. These factors are often invisible to decision makers, and they may operate in tandem to exacerbate ripple effect. We highlight these systemic risks, and we encourage further research to understand their nature and to mitigate their effect

Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

Inclusive J/ψ production at mid-rapidity in pp collisions at √s = 5.02 TeV

Inclusive J/ψ production is studied in minimum-bias proton-proton collisions at a centre-of-mass energy of s = 5.02 TeV by ALICE at the CERN LHC. The measurement is performed at mid-rapidity (|y| &lt; 0.9) in the dielectron decay channel down to zero transverse momentum pT, using a data sample corresponding to an integrated luminosity of Lint = 19.4 ± 0.4 nb−1. The measured pT-integrated inclusive J/ψ production cross sec- tion is dσ/dy = 5.64 ± 0.22(stat.) ± 0.33(syst.) ± 0.12(lumi.) μb. The pT-differential cross section d2σ/dpTdy is measured in the pT range 0–10 GeV/c and compared with state-of- the-art QCD calculations. The J/ψ 〈pT〉 and 〈pT2〉 are extracted and compared with results obtained at other collision energies. [Figure not available: see fulltext.]

Measurement of Λ (1520) production in pp collisions at √s=7TeV and p–Pb collisions at √sNN=5.02TeV

The production of the Λ (1520) baryonic resonance has been measured at midrapidity in inelastic pp collisions at s=7TeV and in p–Pb collisions at sNN=5.02TeV for non-single diffractive events and in multiplicity classes. The resonance is reconstructed through its hadronic decay channel Λ (1520) → pK - and the charge conjugate with the ALICE detector. The integrated yields and mean transverse momenta are calculated from the measured transverse momentum distributions in pp and p–Pb collisions. The mean transverse momenta follow mass ordering as previously observed for other hyperons in the same collision systems. A Blast-Wave function constrained by other light hadrons (π, K, KS0, p, Λ) describes the shape of the Λ (1520) transverse momentum distribution up to 3.5GeV/c in p–Pb collisions. In the framework of this model, this observation suggests that the Λ (1520) resonance participates in the same collective radial flow as other light hadrons. The ratio of the yield of Λ (1520) to the yield of the ground state particle Λ remains constant as a function of charged-particle multiplicity, suggesting that there is no net effect of the hadronic phase in p–Pb collisions on the Λ (1520) yield

Measurement of electrons from heavy-flavour hadron decays as a function of multiplicity in p-Pb collisions at √sNN = 5.02 TeV

The multiplicity dependence of electron production from heavy-flavour hadron decays as a function of transverse momentum was measured in p-Pb collisions at sNN = 5.02 TeV using the ALICE detector at the LHC. The measurement was performed in the centre-of-mass rapidity interval −1.07 &lt; ycms&lt; 0.14 and transverse momentum interval 2 &lt; pT&lt; 16 GeV/c. The multiplicity dependence of the production of electrons from heavy-flavour hadron decays was studied by comparing the pT spectra measured for different multiplicity classes with those measured in pp collisions (QpPb) and in peripheral p-Pb collisions (Qcp). The QpPb results obtained are consistent with unity within uncertainties in the measured pT interval and event classes. This indicates that heavy-flavour decay electron production is consistent with binary scaling and independent of the geometry of the collision system. Additionally, the results suggest that cold nuclear matter effects are negligible within uncertainties, in the production of heavy-flavour decay electrons at midrapidity in p-Pb collisions. [Figure not available: see fulltext.