Nachiketa Tiwari

Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.

Knowledge of internal ballistic pressure and projectile velocity is required to ensure gun safety, reliability, calculation of its range, and evaluation of ammunition. However, existing methods for determining the same are either overly complex, costly or have limitations in their applicability. In this work, the authors address this need by proposing a reliable, simple, cheap, and non-destructive method for determining in-bore pressure, projectile velocity, and acceleration profile using strain measurements. Data from strain gauges were used to detect the projectile’s arrival time at different locations in the barrel and computed projectile velocity and acceleration as a function of projectile position. Finally, acceleration data was used to calculate the pressure behind the projectile. Results were verified using alternative experimental and simulation techniques on two different barrels with different ammunition. It was found that the proposed method works well and thus can be reliably used in similar applications elsewhere.

Purpose: In this work, we have determined the long-term average speech spectra (LTASS) and dynamic ranges (DR) of 17 Indian languages. This work is important because LTASS and DR are language-dependent functions used to fit hearing aids, calculate the Speech Intelligibility Index, and recognize speech automatically. Currently, LTASS and DR functions for English are used to fit hearing aids in India. Our work may help improve the performance of hearing aids in the Indian context. Method: Speech samples from native talkers were used as stimuli in this study. Each speech sample was initially cleaned for extraneous sounds and excessively long pauses. Next, LTASS and DR functions for each language were calculated for different frequency bands. Similar analysis was also performed for English for reference purposes. Two-way analysis of variance was also conducted to understand the effects of important parameters on LTASS and DR. Finally, a one-sample t test was conducted to assess the significance of important statistical attributes of our data. Results: We showed that LTASS and DR for Indian languages are 5–10 dB and 11 dB less than those for English. These differences may be due to lesser use rate of high-frequency dominant phonemes and preponderance of vowel-ending words in Indian languages. We also showed that LTASS and DR do not differ significantly across Indian languages. Hence, we propose a common LTASS and DR for Indian languages. Conclusions: We showed that differences in LTASS and DR for Indian languages vis-à-vis English are large and significant. Such differences may be attributed to phonetic and linguistic characteristics of Indian languages.