VLSI Architectures for the Steerable-Discrete-Cosine-Transform (SDCT)

Since frame resolution of modern video streams is rapidly growing, the need for more complex and efficient video compression methods arises. H.265/HEVC represents the state of the art in video coding standard. Its architecture is however not completely standardized, as many parts are only described at software level to allow the designer to implement new compression techniques. This paper presents an innovative hardware architecture for the Steerable Discrete Cosine Transform (SDCT), which has been recently embedded into the HEVC standard, providing better compression ratios. Such technique exploits directional DCT using basis having different orientation angles, leading to a sparser representation which translates to an improved coding efficiency. The final design is able to work at a frequency of 188 MHZ, reaching a throughput of 3.00 GSample/s. In particular, this architecture supports 8k UltraHigh Definition (UHD) (7680 × 4320) with a frame rate of 60 Hz, which is one of the best resolutions supported by HEVC

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Not AvailableSalinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall’s tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.Not Availabl

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Not AvailableThough the non-coding RNAs (ncRNAs) do not encode for proteins, they act as functional RNAs and regulate gene expression besides their involvement in disease-causing mechanisms and epigenetic mechanisms. Thus, discriminating ncRNAs from coding RNAs (cRNAs) is important in transcriptome studies. Several machine learning-based classifiers, including deep learning classifiers, have been employed for discriminating cRNAsfrom ncRNAs. However, the performance comparison of such classifiers in plant species is yet to be ascertained. Thus, in the present study, the performance of the classifiers such as Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) were evaluated for classifying cRNAs and ncRNAsby using the datasets of plant species including crops such as rice, wheat, maize, cotton, sunflower, barley, banana, grape, papaya. Further, the performance of classifiers was assessed by following the cross-validation process as well as by considering an independent test data set of 3,997 cRNAs and 4,110 ncRNAs. The results revealed that Random Forest classifier exhibited highest performance accuracy (99.803%) among the machine learning classifiers, followed by DNN (99.519%), SVM (97.364%) and ANN (99.260%). The present study is expected to help computational and experimental biologists for easy discrimination between coding and non-coding RNAs.Not Availabl

A full duplex radio over fiber architecture employing 12 Gbps 16 × 16 optical MIMO for next generation communication networks

In this paper, a full duplex Millimeter Wave (mm-wave) enabled Radio-over-Fiber (RoF) architecture is proposed for Distributed Antenna Systems (DAS). This architecture is capable of achieving transmission of 16 16 optical Multiple Input Multiple Output (MIMO) spatial streams at 12 Gbps per spatial stream by employing Wavelength Division Multiplexing (WDM) and exploiting its other degrees of freedom such as polarization states and modes of wavelengths. A single laser source based multi-wavelength comb and wavelength reuse techniques along with Plastic Optical Fiber (POF) are employed to make the proposed architecture cost efficient. Optical heterodyne detection is performed at the Radio Access Unit (RAU) to generate mm-wave carrier frequency at 60 GHz. Channel equalization is achieved for Pulse Amplitude Modulation (PAM-4) data signal by employing Least Mean Square (LMS) equalizer to mitigate the optical fiber channel effects. Our proposed system supports 16x12 Gbps for Downlink (DL) and Uplink (UL) transmissions. To evaluate the performance of the proposed system, we compare the receiver sensitivities at FEC limit of 3.8x1e-3 of Bit Error Ratio (BER) of Back to Back (B2B) system, employing no fiber effects, with its counterparts. We show that acceptable power penalties for the fiber lengths of 200 meter and 400 meter are achieved for both LP01 and LP11 modes in DL and UL directions