Complexity Analysis Of Next-Generation VVC Encoding and Decoding

While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202

Effect of Cement Dust on Pulmonary Functions of Cement Workers

Background: Among cement dust, quartz particles are the most harmful and cause pulmonary fibrosis, which is pathologically among the severe and malignant pneumoconioses. Therefore, by measuring the dust and examining the status of lung functions among workers, we can assess the effects of inhaling cement dust. We aimed to assess the effect of cement dust on pulmonary functions among cement workers during 1999-2000 in Khash, Sistan and Baluchestan Province, Iran. Material and Methods: The total and inhalation dust of the working environment of different units in this industry was measured by individual sampling pump and silicon by weight method and after correcting the volumes, mg/m3 of dust was calculated.  Results: The total dust in different environments was 6.8-95 mg/m3 and the inhaled dust was 2.5-23 mg/m3. Due to the percentage of free silica associated with dust, these values are several times the standard values in the workplace.  The mean values of respiratory functions (FEV1, FVC, VC) in all cases were lower (P-value<0.005) than the mean values in the control group. The difference in the mean FEF25-75 values between the two groups was not significant (P-value>0.5). Although in the case group (all types of workers) the percentage of people with cough was more than the control group, the difference was not statistically significant (P<0.05). With respect to having sputum in the morning and during the day and night and the type of sputum (green and thick, thin, and no sputum), the case group experienced higher rates of sputum and respiratory symptoms. Conclusion: The working environments of cement factories, contrary to what is stated in the toxicology textbooks, requires more attention of health experts and industry managers. Examinations and periodic dust control measures and hiring an occupational health expert is necessary to maintain the health of workers in these environments

A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption

Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.1293

SVM based approach for complexity control of HEVC intra coding

The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.acceptedVersionPeer reviewe

PATTERN-BASED ERROR RECOVERY OF LOW RESOLUTION SUBBANDS IN JPEG2000

ABSTRACT Digital image transmission is widely used in consumer products, such as digital cameras and cellular phones, where low bit rate coding is required. In any low bit rate encoder, such as the JPEG2000 standard, data truncation (during the encoding process), and data loss (during transmission) will result in lost bit-planes, which will be normally replaced by zeros. In this paper a new algorithm has been proposed, which recovers the lost/truncated lower bit-planes of coefficients in the LL subband of a wavelet transform in a JPEG2000 stream using the data available in higher bitplanes of the same coefficient and its eight neighbors. Simulation results indicate that the proposed algorithm achieves 5.40-8.77 dB improvement with respect to zero filling data recovery method

Homing and mobilization of hematopoietic stem cells

Hematopoietic stem and progenitor cells (HSPCs) are non-stop travelers throughout body in both time and space. Understanding the mechanism of HSPCs homing and mobilization is important to enhance the efficacy at bone marrow transplantation and cellular therapy. Mobilized HSPCs has largely replaced than the use of bone marrow as a source of stem cells for both allogeneic and autologous stem cell transplantation. This review describes the specific factors which play a key role in homing and mobilization of HSPCs, includes SDF-1 and its receptor CXCR4, proteases (MMPs and CPM). Moreover, chemokines inducing rapid HPSCs mobilization would be discussed. In this article we showed that many factors such as adhesion molecules and SDF-1/CXCR4 have critical roles in homing hematopoietic stem cells and G.CSF, MMPs, adhesion molecules and ROS involvement in mobilization of stem cells. According to above, we can be rich the peripheral blood of HSPCS using of this factors and antagonist for this receptors on the osteoblastic cells or/and HSPCs to bone marrow transplant

Evaluating the association of biallelic OGDHL variants with significant phenotypic heterogeneity

BACKGROUND: Biallelic variants in OGDHL, encoding part of the α-ketoglutarate dehydrogenase complex, have been associated with highly heterogeneous neurological and neurodevelopmental disorders. However, the validity of this association remains to be confirmed. A second OGDHL patient cohort was recruited to carefully assess the gene-disease relationship. METHODS: Using an unbiased genotype-first approach, we screened large, multiethnic aggregated sequencing datasets worldwide for biallelic OGDHL variants. We used CRISPR/Cas9 to generate zebrafish knockouts of ogdhl, ogdh paralogs, and dhtkd1 to investigate functional relationships and impact during development. Functional complementation with patient variant transcripts was conducted to systematically assess protein functionality as a readout for pathogenicity. RESULTS: A cohort of 14 individuals from 12 unrelated families exhibited highly variable clinical phenotypes, with the majority of them presenting at least one additional variant, potentially accounting for a blended phenotype and complicating phenotypic understanding. We also uncovered extreme clinical heterogeneity and high allele frequencies, occasionally incompatible with a fully penetrant recessive disorder. Human cDNA of previously described and new variants were tested in an ogdhl zebrafish knockout model, adding functional evidence for variant reclassification. We disclosed evidence of hypomorphic alleles as well as a loss-of-function variant without deleterious effects in zebrafish variant testing also showing discordant familial segregation, challenging the relationship of OGDHL as a conventional Mendelian gene. Going further, we uncovered evidence for a complex compensatory relationship among OGDH, OGDHL, and DHTKD1 isoenzymes that are associated with neurodevelopmental disorders and exhibit complex transcriptional compensation patterns with partial functional redundancy. CONCLUSIONS: Based on the results of genetic, clinical, and functional studies, we formed three hypotheses in which to frame observations: biallelic OGDHL variants lead to a highly variable monogenic disorder, variants in OGDHL are following a complex pattern of inheritance, or they may not be causative at all. Our study further highlights the continuing challenges of assessing the validity of reported disease-gene associations and effects of variants identified in these genes. This is particularly more complicated in making genetic diagnoses based on identification of variants in genes presenting a highly heterogenous phenotype such as "OGDHL-related disorders"