This paper considers the problem of anomaly detection in an outdoor environment where surveillance cameras are usually installed to monitor activities of general public. A novel solution is proposed which combines audio and visual data to automatically detect abnormal activities. The proposed anomaly detection algorithm makes use of both visual and audio features to automatically detect anomalous activities in scenes. Visual features such as optical flow technique combined with particle swam optimization and social force model are used, whereas, acoustic features such as, energy, zero crossing rate, volume, spectral-centroid, spectral spread, spectral roll-off, spectral flux, cross correlation and the mel-frequency cepstral coefficients (MFCCs) are used. An anomaly inference is developed which is based on both visual and audio features. The performance of the proposed algorithm is evaluated by testing it on the publicly available UMN datasets combined with the audio recordings. The proposed algorithm is compared with state-of-the-art techniques and is shown to achieve improved performance in terms of accuracy.

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.

Nowadays, the attainment of flexibility in different loads for different electricity customers is an interesting topic. The assessment of flexibility is important because of the ultimate benefits to handle challenges for both electricity customers and utilities. At first, different loads that have significant contribution in energy consumption and may be potential candidates for the extraction of demand flexibility are discussed in this paper. Secondly, different descriptions of demand side flexibility and its assessment manipulation on individual and aggregate loads are explored. After investigation of different techniques some innovative definitions of flexibility indices are surveyed for numerical flexibility assessment. The information about the flexibility potential can lead to initiate different Demand Side Management (DSM) techniques that is helpful to solve electrical power systems key issues. Keeping in view this aspect, different DSM techniques for Demand Response (DR) programs are also reviewed in this paper.

The electrohydrodynamic deformation of an emulsion droplet with a clean and particle-covered interface was explored. Here, the electrohydrodynamic deformation was numerically and experimentally demonstrated under the stimuli of moderate and strong electric fields. The numerical method involves the coupling of the Navier–Stokes equation with the level set equation of interface tracking and the governing equations of so-called leaky dielectric theory. The simulation model developed for a clean interface droplet was then extended to a capsule model for densely particle-covered droplets. The experiments were conducted using various combinations of immiscible oils and particle suspensions while the electric field strength ~10⁵ V/m was generated using a high voltage supply. The experimental images obtained by the camera were post-processed using an in-house image processing code developed on the plat-form of MATLAB software. The results show that particle-free droplets can undergo prolate (deformation in the applied electric field direction) or oblate deformation (deformation that is perpendicular to the direction of the applied electric field) of the droplet interface, whereas the low-conductivity particles can be manipulated at the emulsion interface to form a ‘belt’, ‘helmet’ or ‘cup’ morphologies. A densely particle-covered droplet may not restore to its initial spherical shape due to ‘particle jamming’ at the interface, resulting in the formation of unique droplet shapes. Densely particle-covered droplets behave like droplets covered with a thin particle sheet, a capsule. The deformation of such droplets is explored using a simulation model under a range of electric capillary numbers (i.e., the ratio of the electric stresses to the capillary stresses acting at the droplet interface). The results obtained are then compared with the theory and experimental findings. It was shown that the proposed simulation model can serve as a tool to predict the deformation/distortion of both the particle-free and the densely particle-covered droplets within the small deformation limit. We believe that this study could provide new findings for the fabrication of complex-shaped species and colloidosomes.

Bangladesh, being a developing country, needs an uninterrupted electricity supply to sustain and expand economic growth. The government’s strategic vision of 2021 and the international commitment under the Paris Agreement has meant to attract new capital investments for renewable electricity generation by diversifying energy blends, ranging from natural gas to more reliable coal technologies and renewable energy. To understand the practical implementation of such policies, this paper explores the key factors of the renewable energy (RE) sector of Bangladesh. This research has adopted the strengths, weaknesses, opportunities, and threats (SWOT) analysis method to examine the RE market and to understand the determinants of foreign direct investment (FDI) to attract new investments. For the analysis purposes, data were collected from extant literature and semi-structured interviews from the RE experts in Bangladesh. This study bears significance as it empirically reflects the government’s vision and strategy on RE development and analyzes its challenges and recommends accordingly. The analysis of the study reveals that the regulatory framework, tax haven/exemption, higher tariff, and presence of government guarantee are the major strengths to draw foreign investment. On the contrary, land acquisition, lack of coordination and collaboration among government authorities, administrative procedures, corruption, and access to local finance turns out to be the key weaknesses to consider while investing in this sector. In terms of the external factors, increasing energy demand, increasing global awareness of climate change, and decreasing cost of RE setup equipment act as potent opportunities; while the dominance of fossil fuel and discontinuity of energy policies should be taken as threats that can hinder the flow of investment in this sector. Hence, in order to attract sustainable FDI in the RE sector, several key areas need to be strengthened in the short, medium, and long-term. These are: (i) regulations on non-discriminatory treatment to foreign investors; (ii) control of corruption; (iii) protection of intellectual property rights; and (iv) coordination and collaboration between ministries.