Linear Prediction based Data Detection of Convolutional Coded DQPSK in SIMO-OFDM

Data detection of convolutional coded differential quaternary phase shift keyed (DQPSK) signals using a predictive Viterbi algorithm (VA) based receiver, is presented for single input, multiple output - orthogonal frequency division multiplexed (OFDM) systems. The receiver has both error correcting capability and also the ability to perform channel estimation (prediction). The predictive VA operates on a supertrellis with just $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P-1}$ states instead of $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P}$ states, where the complexity reduction is achieved by using the concept of isometry (here $S_{\mathrm{E}}$ denotes the number of states in the encoder trellis and $P$ denotes the prediction order). Though the linear prediction based data detection in turbo coded OFDM and the bit interleaved coded (BIC) OFDM systems perform better than the proposed approach in terms of bit error rate (BER) for a given signal to noise ratio (SNR), the decoding delay of the proposed approach is significantly lower than that of the BIC and the turbo coded OFDM systems.Comment: The Viterbi algorithm is used to detect convolutionally coded DQPSK in SIMO-OFDM, Wireless Personal Communications, Springer. Online first, 201

Jurassic frogs and the evolution of amphibian endemism in the Western Ghats

The diversity of frogs and toads (Anurans) in tropical evergreen forests has recently gained importance with reports of several new species1. We describe here a fossorial frog taxon related to the African Heleophrynidae and Seychellian Sooglossidae from the Western Ghats of India. This frog possesses a suite of unique ancient characters indicating that it is a transitional form between Archaeobatrachians and Neobatrachians. Molecular clock analysis based on the nucleotide diversity in mitochondrial 12S and 16S genes dates this frog as a Gondwana relic, which evolved 150–195 Mya during the mid-Jurassic period.With this taxon, the evolution of endemism in the Western Ghats and other Gondwana break up landmasses is now dated much before the Cretaceous–Tertiary boundary. We propose that sea level surges in the late Jurassic2 isolated tablelands creating insular amphibian fauna. Reduction in area may have promoted stochastic extinctions and resulted in amphibian endemism. Our study reinforces the conservation significance of the Western Ghats as major global hotspot of biodiversity. The habitat of this endemic amphibian lineage is currently endangered due to various upcoming dam projects, which is a cause of serious conservation concern

Effect of nano based seed treatment insecticides on seed quality in Pigeonpea

A laboratory experiment was conducted to know the effect seed treatment with nano insecticides on seed quality of pigeonpea (Cajanus cajan (L.) Millsp.) cv. TS3R. This study was conducted to evaluate the effect of macro and nano insecticides on seed germination and vigour of Pigeonpea. Different recommended seed treatment insecticides viz, malathion, fenvalerate, emamectine benzoate, thiodicarb, sweet flag and neem seed kernel powder insecticides were synthesized to nano form using high energy planetary ball mill. The Pigeonpea seed were treated with different nano insecticides i.e., 10-90 per cent reduction in actual dosage. Among the different treatments studied, seed treated with nano malathion 50 per cent lesser than normal dosage, fenvalerate 60 per cent lesser, thiodicarb 10 per cent lesser, emamectine benzoate 30 per cent lesser, sweetflag 70 per cent lesser, neem seed kernel powder 40 per cent lesser than actual recommended dosage gave significantly higher seed germination (98.0, 98.67, 98.67, 97.0, 99.0 and 98.67 percent) ,less number of abnormal seedlings (1.0, 0.33, 1.0, 1.0, 1.0 and 0.33 per cent) , shoot length (10.13, 9.00, 11.47, 9.50, 10.90 and 10.87 cm), root length (12.56, 12.93, 12.83, 12.60 11.50 and 13.00 cm), seedling dry weight (85.73, 87.40, 88.47, 87.70, 88.60 and 88.27 g) and seedling vigour index (2223, 2164, 2397, 2143, 2217 and 2354) as compared to untreated seeds and macro insecticides. Therefore, it is very clear that nano based insecticides has a significant (0.1 %) impact on the seed quality improvement

Autonomous Vascular Networks Synchronize GABA Neuron Migration in the Embryonic Forebrain

GABA neurons, born in remote germinative zones in the ventral forebrain (telencephalon), migrate tangentially in two spatially distinct streams to adopt their specific positions in the developing cortex. The cell types and molecular cues that regulate this divided migratory route remains to be elucidated. Here we show that embryonic vascular networks are strategically positioned to fulfill the task of providing support as well as critical guidance cues that regulate the divided migratory routes of GABA neurons in the telencephalon. Interestingly, endothelial cells of the telencephalon are not homogeneous in their gene expression profiles. Endothelial cells of the periventricular vascular network have molecular identities distinct from those of the pial network. Our data suggest that periventricular endothelial cells have intrinsic programs that can significantly mold neuronal development and uncovers new insights into concepts and mechanisms of CNS angiogenesis from both developmental and disease perspectives

Visual In-Context Learning for Few-Shot Eczema Segmentation

Automated diagnosis of eczema from digital camera images is crucial for developing applications that allow patients to self-monitor their recovery. An important component of this is the segmentation of eczema region from such images. Current methods for eczema segmentation rely on deep neural networks such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While effective, these methods require high volume of annotated data, which can be difficult to obtain. Here, we investigate the capabilities of visual in-context learning that can perform few-shot eczema segmentation with just a handful of examples and without any need for retraining models. Specifically, we propose a strategy for applying in-context learning for eczema segmentation with a generalist vision model called SegGPT. When benchmarked on a dataset of annotated eczema images, we show that SegGPT with just 2 representative example images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net trained on 428 images (mIoU: 32.60). We also discover that using more number of examples for SegGPT may in fact be harmful to its performance. Our result highlights the importance of visual in-context learning in developing faster and better solutions to skin imaging tasks. Our result also paves the way for developing inclusive solutions that can cater to minorities in the demographics who are typically heavily under-represented in the training data