With the penetration of renewable energy sources based distributed generation (RES-DG) in the distribution network (DN), the operating time of protection relays becomes a major concern to avoid the mis-coordination and nuisance tripping of RES-DG. It is due to the bidirectional flow of current, variance in the fault current level, i.e. high fault current in case of Squirrel cage induction generator (SCIG) based RES-DG, and low fault current for an inverter-based RES-DGs. This paper proposes a novel User-defined dual setting direction overcurrent relay with hybrid time current-voltage (UDDOR-TCV) characteristics to intensify the proposed scheme’s flexibility without any communication assistance for radial DN. The proposed model is formulated in a constrained non-linear optimization fashion and solved with the MINLP solver of general algebraic modelling system(GAMS) software to determine the optimal relay settings. The propounded protection scheme is simulated on IEEE-33 bus radial distribution system and a local 40-bus system hosting the multiple SCIG and PV based RES-DGs at optimal locations. Detailed numerical studies are carried out to show the performance of the proposed scheme. 66.848% and 68.26% reduction in relay operation time with zero mis-coordination is achieved with SCIG-WTG case and PV-units case for the IEEE-33 bus system and a similar reduction in operation time is 68.937% and 66.563% for Local-40 bus system. Moreover, the relays numbers are reduced by 32.692% and 39.7% for IEEE-33 and Local-40 bus systems, respectively, due to the forward/reverse characteristics capability of the proposed UDDOR-TCV. The performance of the proposed scheme is also evaluated on IEEE-8 bus meshed DN. The results are compared with the existing protection schemes in recent literature.

People with vision impairment use Braille language for reading, writing, and communication. The basic structure of the Braille language consists of six dots arranged in three rows and two column cells, which are identified by visually impaired people using finger touch. However, it is difficult to memorize the pattern of dots that form the Braille characters. This research presents a novel approach for automatic Braille characters recognition. The designed approach works in two main stages. In first stage, image alignment & enhancement are performed using several image preprocessing techniques. In second stage, character recognition is performed with a proposed lightweight convolution neural network (CNN). As CNN shows promise for accurate recognition of optical characters. Therefore, we adopted several recently proposed state-of-the-art CNN networks for Braille characters’ recognition. To make the networks light and improve their recognition performance, we proposed a strategy by replacing few modules in the original CNNs with an inverted residual block (IRB) module with less computational cost. The novelty of this work lies in CNN model design and output performance. We executed the effectiveness of the designed setup through experiments on two different publicly available benchmark Braille datasets obtained from visually impaired people. On the English Braille and Chinese double-sided Braille image (DSBI) datasets, the proposed model shows a prediction accuracy of 95.2% and 98.3%, respectively. The reported test time of model is about 0.01s for English and 0.03s DSBI Braille images. In comparison to state-of-the-art, designed method is robust, effective, and capable to identify the Braille characters efficiently. In future, the functional performance of the proposed Braille recognition scheme will be tested through accessible user interfaces.

With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences. However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy. This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence. The proposed method also detects anomalous regions in anomalous video frames. We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames. Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed. These models efficiently utilise variable sized cell structure which creates variable sized sub-regions of scenes in video frames. Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features. The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature. The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time.

Weeds infestation causes damage to crops and limits the agricultural production. The traditional weeds controlling methods rely on agrochemicals which demand labour-intensive practices. Various methods are proposed for the pursuit of weeds detection using multispectral images. The machine vision-based weeds detection methods require the extraction of a large number of multispectral texture features which in turn increases the computational cost. Deep neural networks are used for pixel-based weeds classification, but a drawback of these deep neural network-based weeds detection methods is that they require a large size of images dataset for network training which is time-consuming and expensive to collect particularly for multispectral images. These methods also require a Graphics Processing Unit (GPU) based system because of having high computational cost. In this article, we propose a novel weeds detection model which addresses these issues, as it does not require any kind of supervised training using labelled images and multispectral texture features extraction. The proposed model can execute on a Central Processing Unit (CPU) based system as a result its computational cost reduces. The Predictive Coding/Biased Competition-Divisive Input Modulation (PC/BC-DIM) neural network is used to determine multispectral fused saliency map which is further used to predict salient crops and detect weeds. The proposed model has achieved 94.38% mean accuracy, 0.086 mean square error, and 0.291 root mean square error.

The human visual system uses saccadic and vergence eye movements to foveate visual targets. To mimic this aspect of the biological visual system the PC/BC-DIM neural network is used as an omni-directional basis function network for learning and performing sensory-sensory and sensory-motor transformations without using any hard-coded geometric information. A hierarchical PC/BC-DIM network is used to learn a head-centred representation of visual targets by dividing the whole problem into independent subtasks. The learned head-centred representation is then used to generate saccade and vergence motor commands. The performance of the proposed system is tested using the iCub humanoid robot simulator.

Invertebrates are abundant in horticulture and farming environments, and can be detrimental. Early pest detection for an integrated pest-management system with an integration of physical, biological, and prophylactic methods has huge potential for the better yield of crops. Computer vision techniques with multispectral images are used to detect and classify pests in dynamic environmental conditions, such as sunlight variations, partial occlusions, low contrast, etc. Various state-of-art, deep learning approaches have been proposed, but there are some major limitations to these methods. For example, labelled images are required to supervise the training of deep networks, which is tiresome work. Secondly, a huge in-situ database with variant environmental conditions is not available for deep learning, or is difficult to build for fretful bioaggressors. In this paper, we propose a machine-vision-based multispectral pest-detection algorithm, which does not require any kind of supervised network training. Multispectral images are used as input for the proposed pest-detection algorithm, and each image provides comprehensive information about different textural and morphological features, and visible information, i.e., size, shape, orientation, color, and wing patterns for each insect. Feature identification is performed by a SURF algorithm, and feature extraction is accomplished by least median of square regression (LMEDS). Feature fusion of RGB and NIR images onto the coordinates of Ultraviolet (UV) is performed after affine transformation. The mean identification errors of type I, II, and total mean error surpass the mean errors of the state-of-art methods. The type I, II, and total mean errors, with 6.672% UV weights, were emanated to 1.62, 40.27, and 3.26, respectively.