Morpho-biochemical characterization of Psidium species

Several Psidium species are available with many important traits, lack of intensive characterization limits their use in guava improvement. Therefore, the present study was carried out to characterize five wild Psidium species (P. molle, P. chinensis, P. guineense, P. cattleianum var. cattleianum and P. cattleianum var. lucidum) and two P. guajava genotypes (cv. ‘Arka Poorna’ and ‘H 12-5’), based on morphological and biochemical traits. Among morphological traits, fruit weight was ranged from 5.22 g (P. cattleianum var. cattleianum) to 225.14 g (‘H 12-5’), however, among biochemical traits, highest TSS (12.06 ºBrix) and total sugars (9.98%) were recorded in cv. ‘Arka Poorna’, while, lowest recorded in P. cattleianum var. lucidum. Highest ascorbic acid was recorded in P. chinensis (205.33 mg/100 g), whereas, lowest recorded in P. guineense (60.83 mg/100 g). A positive correlation was observed among wild Psidium species but none had correlation with P. guajava genotypes for quantitative traits

Mycodecolorization Activity of Pleurotus Citrinopileatus for Chemically Different Textile Dye Under Varied Aromatic Amino Acids and Trace Elements

In the present study, ligninolytic enzymes laccase (benzenediol: oxygen reductase EC; 1.10.3.2) and Manganese Peroxidase (Mn(II): hydrogen-peroxide oxidoreductase EC; 1.11.1.13) activity and of White Rot Fungi (WRF) Pleurotus citrinopileatus were enhanced with the application of trace metal i.e. Copper and Manganese at 25 ppm and 50 ppm followed by aromatic amino acids (Phenylalanine, Tryptophan and Tyrosine) at 0.02 μM and 0.4 μM. Laccase and MnP activity were 213.42U and 202.28U respectively, observed at 300ppm of Methyl Red supplemented with Tyrosine (0.2μM) followed by treatment of Tryptophan (198.45U and 195.16U) and Phenylalanine (195.85U and 188.15U). Maximum Laccase and MnP activity (Tyrosine treated) were revealed maximum decolorization of Phenol Red and Methyl Red (84.14% and 78.20%) followed by Phenylalanine (80.92% and 73.80%) and Trypatophan (71.22% and 70.12%).  The negative correlation of  Laccase and MnP activity was observed with a higher concentration (>50ppm) of trace metal in the medium, while at 25ppm of copper supplemented medium increase three-fold of Laccase activity (585.56U) as tyrosine medium and similarly, Manganese (25ppm) inosculated medium revealed three-fold more MnP activity (478.95U).  A lower amount of Cu hoists Laccase and MnP activity which decolorized 300ppm of Methyl Red and Phenol Red with maximum percent (92.3% and 88.15%) followed by Mn. Thus, Laccase and MnP enzymes both play an important role in decolorization of dyes, and its activity was enhanced with the application of lower concentration of trace metals followed by aromatic amino acids

Morpho-biochemical characterization of Psidium species

Several Psidium species are available with many important traits, lack of intensive characterization limits their use in guava improvement. Therefore, the present study was carried out to characterize five wild Psidium species (P. molle, P. chinensis, P. guineense, P. cattleianum var. cattleianum and P. cattleianum var. lucidum) and two P. guajava genotypes (cv. ‘Arka Poorna’ and ‘H 12-5’), based on morphological and biochemical traits. Among morphological traits, fruit weight was ranged from 5.22 g (P. cattleianum var. cattleianum) to 225.14 g (‘H 12-5’), however, among biochemical traits, highest TSS (12.06 ºBrix) and total sugars (9.98%) were recorded in cv. ‘Arka Poorna’, while, lowest recorded in P. cattleianum var. lucidum. Highest ascorbic acid was recorded in P. chinensis (205.33 mg/100 g), whereas, lowest recorded in P. guineense (60.83 mg/100 g). A positive correlation was observed among wild Psidium species but none had correlation with P. guajava genotypes for quantitative traits

Learning Salient Features in Radar Micro-Doppler Signatures Using Attention Enhanced Alexnet

This work introduces an attention mechanism that can be integrated into any standard convolution neural network (CNN) to improve model sensitivity and prediction accuracy with minimal computational overhead. We introduce the attention mechanism in a lightweight network - Alexnet and evaluate its classification performance for human micro-Doppler signatures. We show that the Alexnet model trained with an attention module can implicitly learn to highlight the salient regions in the radar signatures whilst suppressing the irrelevant background regions and consistently improve the network predictions by more than 4% in most cases. We further provide network visualizations through class activation mapping, providing better insights into how the predictions are made

Sparsity-based autoencoders for denoising cluttered radar signatures

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%

On the Propagation of Shock Waves Produced by Explosion of a Spherical Charge in Deep Sea

The propagation of spherical shock waves, produced by explosion of a spherical charge in deep sea, have been studied using the, energy hypothesis of T.Y. Thomas. The energy release from the charge is supposed to be time dependent and the effects of earth's gravitation is taken into account. It is found that the shock compression varies very slowly with variation in the direction of shock propagation. The effects of earth's gravitation is to decrease the shock compression and its decay with shock radius. A comparison has also been made between the results of time dependent and instantaneous energy release

Using RF Transmissions from IoT Devices for Occupancy Detection and Activity Recognition

IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing)

SimHumalator: An Open Source End-to-End Radar Simulator For Human Activity Recognition

Radio-frequency based non-cooperative monitor ing of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and bio-medical applications for non-intrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever- increasing processing speeds of computers could drive forward the data- driven deep-learning focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi standard(IEEE 802.11g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks