Symmetric rearrangeable networks and algorithms

A class of symmetric rearrangeable nonblocking networks has been considered in this thesis. A particular focus of this thesis is on Benes networks built with 2 x 2 switching elements. Symmetric rearrangeable networks built with larger switching elements have also being considered. New applications of these networks are found in the areas of System on Chip (SoC) and Network on Chip (NoC). Deterministic routing algorithms used in NoC applications suffer low scalability and slow execution time. On the other hand, faster algorithms are blocking and thus limit throughput. This will be an acceptable trade-off for many applications where achieving ”wire speed” on the on-chip network would require extensive optimisation of the attached devices. In this thesis I designed an algorithm that has much lower blocking probabilities than other suboptimal algorithms but a much faster execution time than deterministic routing algorithms. The suboptimal method uses the looping algorithm in its outermost stages and then in the two distinct subnetworks deeper in the switch uses a fast but suboptimal path search method to find available paths. The worst case time complexity of this new routing method is O(NlogN) using a single processor, which matches the best known results reported in the literature. Disruption of the ongoing communications in this class of networks during rearrangements is an open issue. In this thesis I explored a modification of the topology of these networks which gives rise to what is termed as repackable networks. A repackable topology allows rearrangements of paths without intermittently losing connectivity by breaking the existing communication paths momentarily. The repackable network structure proposed in this thesis is efficient in its use of hardware when compared to other proposals in the literature. As most of the deterministic algorithms designed for Benes networks implement a permutation of all inputs to find the routing tags for the requested inputoutput pairs, I proposed a new algorithm that can work for partial permutations. If the network load is defined as ρ, the mean number of active inputs in a partial permutation is, m = ρN, where N is the network size. This new method is based on mapping the network stages into a set of sub-matrices and then determines the routing tags for each pair of requests by populating the cells of the sub-matrices without creating a blocking state. Overall the serial time complexity of this method is O(NlogN) and O(mlogN) where all N inputs are active and with m < N active inputs respectively. With minor modification to the serial algorithm this method can be made to work in the parallel domain. The time complexity of this routing algorithm in a parallel machine with N completely connected processors is O(log^2 N). With m active requests the time complexity goes down to (logmlogN), which is better than the O(log^2 m + logN), reported in the literature for 2^0.5((log^2 -4logN)^0.5-logN)<= ρ <= 1. I also designed multistage symmetric rearrangeable networks using larger switching elements and implement a new routing algorithm for these classes of networks. The network topology and routing algorithms presented in this thesis should allow large scale networks of modest cost, with low setup times and moderate blocking rates, to be constructed. Such switching networks will be required to meet the bandwidth requirements of future communication networks

Two-stream Multi-dimensional Convolutional Network for Real-time Violence Detection

The increasing number of surveillance cameras and security concerns have made automatic violent activity detection from surveillance footage an active area for research. Modern deep learning methods have achieved good accuracy in violence detection and proved to be successful because of their applicability in intelligent surveillance systems. However, the models are computationally expensive and large in size because of their inefficient methods for feature extraction. This work presents a novel architecture for violence detection called Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which uses RGB frames and optical flow to detect violence. Our proposed method extracts temporal and spatial information independently by 1D, 2D, and 3D convolutions. Despite combining multi-dimensional convolutional networks, our models are lightweight and efficient due to reduced channel capacity, yet they learn to extract meaningful spatial and temporal information. Additionally, combining RGB frames and optical flow yields 2.2% more accuracy than a single RGB stream. Regardless of having less complexity, our models obtained state-of-the-art accuracy of 89.7% on the largest violence detection benchmark dataset.Comment: 8 pages, 6 figure

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results

Aquatic Rover for Water Quality Testing and Helping in Pisciculture

We know that nowadays water pollution is very much common in Bangladesh. Two of the major sources are waste from industries and rubbish dumping. These cause much harm to farmers in their marine agriculture since it largely hampers the quality of water and affects fishes. This is a very serious issue and needs to be solved. Our focus is primarily in rural areas. We aim to solve these problems by building an underwater rover which would help to test the quality of water and detect water pollution. This would be done by measuring the pH level and the degree of Temperature which will determine the acidity or alkalinity of the water, and the availability of salts underwater. By sensing this we would get different readings of pollution in different areas by driving the rover all over the water body. Thus we can know which areas are good for what type of marine farming and can create awareness for the problem which would help reduce pollution in the future. Keywords: Mapping; Synchronizing; pH; Temperature

Symmetric rearrangeable networks and algorithms

A class of symmetric rearrangeable nonblocking networks has been considered in this thesis. A particular focus of this thesis is on Benes networks built with 2 x 2 switching elements. Symmetric rearrangeable networks built with larger switching elements have also being considered. New applications of these networks are found in the areas of System on Chip (SoC) and Network on Chip (NoC). Deterministic routing algorithms used in NoC applications suffer low scalability and slow execution time. On the other hand, faster algorithms are blocking and thus limit throughput. This will be an acceptable trade-off for many applications where achieving ”wire speed” on the on-chip network would require extensive optimisation of the attached devices. In this thesis I designed an algorithm that has much lower blocking probabilities than other suboptimal algorithms but a much faster execution time than deterministic routing algorithms. The suboptimal method uses the looping algorithm in its outermost stages and then in the two distinct subnetworks deeper in the switch uses a fast but suboptimal path search method to find available paths. The worst case time complexity of this new routing method is O(NlogN) using a single processor, which matches the best known results reported in the literature. Disruption of the ongoing communications in this class of networks during rearrangements is an open issue. In this thesis I explored a modification of the topology of these networks which gives rise to what is termed as repackable networks. A repackable topology allows rearrangements of paths without intermittently losing connectivity by breaking the existing communication paths momentarily. The repackable network structure proposed in this thesis is efficient in its use of hardware when compared to other proposals in the literature. As most of the deterministic algorithms designed for Benes networks implement a permutation of all inputs to find the routing tags for the requested inputoutput pairs, I proposed a new algorithm that can work for partial permutations. If the network load is defined as ρ, the mean number of active inputs in a partial permutation is, m = ρN, where N is the network size. This new method is based on mapping the network stages into a set of sub-matrices and then determines the routing tags for each pair of requests by populating the cells of the sub-matrices without creating a blocking state. Overall the serial time complexity of this method is O(NlogN) and O(mlogN) where all N inputs are active and with m < N active inputs respectively. With minor modification to the serial algorithm this method can be made to work in the parallel domain. The time complexity of this routing algorithm in a parallel machine with N completely connected processors is O(log^2 N). With m active requests the time complexity goes down to (logmlogN), which is better than the O(log^2 m + logN), reported in the literature for 2^0.5((log^2 -4logN)^0.5-logN)<= ρ <= 1. I also designed multistage symmetric rearrangeable networks using larger switching elements and implement a new routing algorithm for these classes of networks. The network topology and routing algorithms presented in this thesis should allow large scale networks of modest cost, with low setup times and moderate blocking rates, to be constructed. Such switching networks will be required to meet the bandwidth requirements of future communication networks

Geometrical contribution to neutrino mass matrix

The dynamics of fermions on curved spacetime requires a spin connection, which contains a part called contorsion, an auxiliary field without dynamics but fully expressible in terms of the axial current density of fermions. Its effect is the appearance of a quartic interaction of all fermions in the action, leading to a nonlinear Dirac equation involving all fermions present. Noting that left and right-chiral fermions may couple to contorsion by different strengths, we show that all fermions gain an effective mass when propagating through fermionic matter. This may have an observable effect on neutrino oscillations. In particular we find that different neutrino flavors can mix even if they have zero rest mass in vacuum, without requiring fields beyond the Standard Model