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Risk of social colours in an agamid lizard: implications for the evolution of dynamic signals
No full textThe forces of sexual and natural selection are typically invoked to explain variation in colour patterns of animals. Although the benefits of conspicuous colours for social signalling are well documented, evidence for their ecological cost, especially for dynamic colours, remains limited. We examined the riskiness of colour patterns of Psammophilus dorsalis, a species in which males express distinct colour combinations during social interactions. We first measured the conspicuousness of these colour patterns on different substrates based on the visual systems of conspecifics and predators (bird, snake, canid) and then quantified actual predation risk on these patterns using wax/polymer lizard models in the wild. The black and red male state exhibited during courtship was the most conspicuous to all visual systems, while the yellow and orange male aggression state and the brown female colour were least conspicuous. Models bearing the courtship colour pattern experienced the highest predator attacks, irrespective of the substrate they were placed on. Thus, social colours of males are not only conspicuous but also risky. Using physiological colours to shift in and out of conspicuous states may be an effective evolutionary solution to balance social signalling benefits with predation costs
Seismic site classification and correlation between V-S and SPT-N for deep soil sites in Indo-Gangetic Basin
No full textThe Indo-Gangetic Basin (IGB) is occupied by the thick deposits of different types of loose to dense soil due to the active sedimentation. Till date, no detailed study has been carried out on seismic site characterization, and classification considering shear wave velocity >50 m depth. In this study, surface wave survey was used for determining the shear wave velocity (V-S) variation with depth at 276 locations in the IGB using low-frequency geophones by performing combined active (Multichannel analysis of surface waves, MASW) and passive (Ambient Noise) surveys. To study the spatial variability of V-S, based on the sediment deposition and geological variability, the whole IGB has been divided into Punjab-Haryana region (PHR), Uttar Pradesh region (UPR), and Bihar region (BR). Firstly, using the least square, orthogonal, and mixed effect approaches, a new correlation between the V-S and SPT-N has been developed for all the three regions separately. The V-S and SPT-N correlation has been developed considering both corrected and uncorrected SPT-N value. The residuals have been determined using the observed and the predicted V-S values from newly developed V-S and SFT-N correlation from each approach. For the same SPT-N value, correlation for UPR region is predicting high V-S value as compared to other two regions. Studying the variation of residuals with V-S, it is determined that the mixed effect is performing better for V-S <= 250 m/s and orthogonal for V-S > 250 m/s. At higher N-value, difference between V-S values for all the three regions are noticeable. Further, the newly developed relationships are compared with the existing V-S and SPT-N relationship. Additionally, the IGB is classified based on the average shear wave velocity in the top 30 m (V-S30) as per the National Earthquake Hazards Reduction Program (NEHRP) and compared with Eurocode 8 (EC8). The V-S30 is high in the southern part of the BR and UPR and low near to the active channel deposition and major part of the IGB is classified as seismic site class D. Most of the IGB sites have experienced severe damage either due to local site effects or liquefaction during past earthquakes. Therefore, the spatial variability of the average shear wave velocity at different depths, i.e., at top 5,10,1520, 30 50, 100, 150, 200, 250 and 300 m are also estimated and compared with the available basement depth map. (C) 2019 Published by Elsevier B.V
Synthesis of -Diketone DNA Derivatives for Affinity Modification of Proteins
No full textDiketone DNA derivatives have been proposed to modify the guanidine group of Arg in proteins. The -diketo group at the C2' atom of the sugar phosphate moiety has been introduced in DNA by acylation of oligonucleotide precursors, i.e., DNA fragments containing 2'-amino-2'-deoxyuridine, which have been synthesized by the chemical automatic synthesis. Water-soluble N-3-(dimethylamino)propyl]-N-ethylcarbodiimide (EDC) and 4,6-dioxoheptanoic acid have been used in the reaction. The ability of oligodeoxyribonucleotides containing the 2'--diketo group to react with guanidine, N-Boc-L-arginine, and N-Dns-L-arginine has been demonstrated. The introduction of this modification into one of the strands of the 15-base pair DNA duplex has been shown to lead to its destabilization. The conjugate formation of MutS and MutL proteins from the E.coli mismatch repair system with 17-base pair DNA duplexes containing the 2'-deoxy-2'-(4,6-dioxoheptylamido)uridine residue has been detected for the first time. To increase the selectivity of the DNA ligands containing the -diketo group in the reaction with the Arg residues of proteins, we have proposed to treat the reaction mixture with hydroxylamine. This treatment leads to the cleavage of Schiff bases, which are formed with the involvement of lysine residues
Protein functionalised self assembled monolayer based biosensor for colon cancer detection
No full textWe report results of the studies relating to the fabrication of a surface plasmon resonance (SPR) based label-free immunosensor for real-time monitoring of endothelin-1 (ET-1), a colon cancer biomarker. A gold disk modified with a self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid (11-MUA) was functionalised via covalent immobilization of monoclonal anti-ET-1 antibodies using EDC-NHS (1-(3-(dimethylamine)-propyl)-3ethylcarbodiimide hydrochloride, N-hydroxy succinimide) chemistry. This immunosensing platform (ethanolamine/anti-ET-1/11-MUA/Au) was characterized via atomic force microscopy (AFM), contact angle (CA) and Fourier transform infrared (FT-1R) spectroscopic techniques. The fabricated SPR electrode was further used to detect ET-1 in the broad concentration range 2-100 pg mL(-1), with a detection limit of 0.30 pg mL(-1) and remarkable sensitivity of 2.18 m degrees pg(-1)mL. The adsorption mechanism was studied using monophasic model and the values of association (k(a)) and dissociation (k(d)) constants for anti-ET-1 and ET-1 binding were calculated to be 4.4 +/- 0.4 x 10(5) M-1 s(-1) and 2.04 +/- 0.0003 x 10(-3) s(-1), respectively. The results obtained via analysis of serum samples of colorectal cancer patients were found to be in good agreement with those obtained from enzyme-linked immunosorbent assay (ELISA) technique. Further, electrochemical studies were performed to prove the efficacy of the fabricated platform as a point of care device for the detection of ET-1
Effect of Dy3+ substitution on structural, magnetic and dielectric properties of BiFeO3-PbTiO3 multiferroics
No full textMultiferroic 0.6Bi((1-x))Dy(x)FeO(3)-0.4PbTiO(3) (x = 0, 0.10) nanoparticles were synthesized via sol-gel route. The effect of rare earth Dy3+ ion substitution on structural, magnetic and dielectric properties of BiFeO3-PbTiO3 system has been studied. Rietveld refinement studies of X-ray diffraction profiles reveal that system exhibit cubic (Pm (3) over barm) and tetragonal (P4mm) crystal structure for x = 0 and single cubic (Pm3m) phase for x = 0.10. Experimental results show that Dy3+ doping suppresses the modulated spin cycloid of BiFeO3 results in net remnant magnetization. Dielectric studies show that the conductivity of the sample decreases with Dy3+ ion substitution in the composition, which indicates that Dy3+ doping suppresses the oxygen vacancies
Modulation Filter Learning Using Deep Variational Networks for Robust Speech Recognition
No full textThe performance of a typical speech recognition system is degraded in the presence of extrinsic sources like noise and due to the recording artifacts like reverberation. The principle of modulation filtering attempts to remove the spectro-temporal modulations of the speech signal that are more susceptible to noise while preserving the key modulations for speech recognition. While traditional approaches use modulation filters that are hand-crafted, we propose a novel method for modulation filter learning using deep variational models in this paper. Specifically, we pose the filter learning problem in a deep unsupervised generative modeling framework where the convolutional filters in the variational autoencoder capture the important speech modulations. The two-dimensional modulation filters, learned using the deep variational networks in the joint spectro-temporal domain, are used to process the spectrogram features for speech recognition task. Several speech recognition experiments are performed on a set of tasks consisting of additive noise with channel artifacts (Aurora-4), reverberation (REVERB Challenge), and additive noise with reverberation (CHiME-3). In these experiments, the proposed modulation filter learning framework shows significant improvements over the baseline features as well as various other noise robust front-ends (average relative improvements of 7.5% and 20% over the baseline features on the Aurora-4 and CHiME-3 databases respectively). Furthermore, the proposed method is also shown to be of considerable benefit for semi-supervised automatic speech recognition applications. For example, on Aurora-4 database we observe an average relative improvement of 25% over the baseline system using 30% labeled training data
Depth compression via planar segmentation
No full textAugmented Reality applications are set to revolutionize the smartphone industry due to the integration of RGB-D sensors into mobile devices. Given the large number of smartphone users, efficient storage and transmission of RGB-D data is of paramount interest to the research community. While there exist Video Coding Standards such as HEVC and H.264/AVC for compression of RGB/texture component, the coding of depth data is still an area of active research. This paper presents a method for coding depth videos, captured from mobile RGB-D sensors, by planar segmentation. The segmentation algorithm is based on Markov Random Field assumptions on depth data and solved using Graph Cuts. While all prior works based on this approach remain restricted to images only and under noise-free conditions, this paper presents an efficient solution to planar segmentation in noisy depth videos. Also presented is a unique method to encode depth based on its segmented planar representation. Experiments on depth captured from a noisy sensor (Microsoft Kinect) shows superior Rate-Distortion performance over the 3D extension of HEVC codec
Speaker identification using multi-modal i-vector approach for varying length speech in voice interactive systems
No full textThe development in the interface of smart devices has lead to voice interactive systems. An additional step in this direction is to enable the devices to recognize the speaker. But this is a challenging task because the interaction involves short duration speech utterances. The traditional Gaussian mixture models (GMM) based systems have achieved satisfactory results for speaker recognition only when the speech lengths are sufficiently long. The current state-of-the-art method utilizes i-vector based approach using a GMM based universal background model (GMM-UBM). It prepares an i-vector speaker model from a speaker's enrollment data and uses it to recognize any new test speech. In this work, we propose a multi-model i-vector system for short speech lengths. We use an open database THUYG-20 for the analysis and development of short speech speaker verification and identification system. By using an optimum set of mel-frequency cepstrum coefficients (MFCC) based features we are able to achieve an equal error rate (EER) of 3.21% as compared to the previous benchmark score of EER 4.01% on the THUYG-20 database. Experiments are conducted for speech lengths as short as 0.25 s and the results are presented. The proposed method shows improvement as compared to the current i-vector based approach for shorter speech lengths. We are able to achieve improvement of around 28% even for 0.25 s speech samples. We also prepared and tested the proposed approach on our own database with 2500 speech recordings in English language consisting of actual short speech commands used in any voice interactive system
Robust and High-Dynamic-Performance Control of Induction Motor Drive Using Transient Vector Estimator
No full textThe two most commonly used induction motor control methods are the scalar (V/f) and vector (fieldoriented) control techniques. V/f control has good steady-state response and robustness against the variation in machine parameters, but the resulting torque dynamics are poorer. Vector control achieves better dynamic performance by decoupling the flux- and torque-producing components of the stator current. Vector-controlled techniques are heavily dependent on the machine parameters and are sensitive to the variation in machine parameters. This paper proposes a novel control technique, which integrates the robustness features of scalar control and good dynamic performance of vector control. Estimation of the transient vector is responsible for improving dynamics in the proposed control. The proposed control technique uses an optimal controller with an output feedback law. The operation of the proposed control is validated experimentally under both steady-state and transient conditions. Finally, the proposed control is compared with both the V/f and vector control strategies in terms of dynamic performance and parameter sensitivity
Neuromorphic vision: From sensors to event-based algorithms
No full textRegardless of the marvels brought by the conventional frame-based cameras, they have significant drawbacks due to their redundancy in data and temporal latency. This causes problem in applications where low-latency transmission and high-speed processing are mandatory. Proceeding along this line of thought, the neurobiological principles of the biological retina have been adapted to accomplish data sparsity and high dynamic range at the pixel level. These bio-inspired neuromorphic vision sensors alleviate the more serious bottleneck of data redundancy by responding to changes in illumination rather than to illumination itself. This paper reviews in brief one such representative of neuromorphic sensors, the activity-driven event-based vision sensor, which mimics human eyes. Spatio-temporal encoding of event data permits incorporation of time correlation in addition to spatial correlation in vision processing, which enables more robustness. Henceforth, the conventional vision algorithms have to be reformulated to adapt to this new generation vision sensor data. It involves design of algorithms for sparse, asynchronous, and accurately timed information. Theories and new researches have begun emerging recently in the domain of event-based vision. The necessity to compile the vision research carried out in this sensor domain has turned out to be considerably more essential. Towards this, this paper reviews the state-of-the-art event-based vision algorithms by categorizing them into three major vision applications, object detection/recognition, object tracking, localization and mapping. This article is categorized under: Technologies > Machine Learnin