The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical features e.g. the colour and texture of cabbage and lettuce. Current state-of-the-art classification techniques such as those based on Deep Convolutional Neural Networks (DCNNs) are highly prone to errors resulting from the inter-class similarities and intra-class variations of fruit and vegetable images. The deep features of highly variable classes can invade the features of neighbouring similar classes in a learned feature space of the DCNN, resulting in confused classification hyper-planes. To overcome these limitations of current classification techniques we have proposed a class distribution-aware adaptive margins approach with cluster embedding for classification of fruit and vegetables. We have tested the proposed technique for cluster-based feature embedding and classification effectiveness. It is observed that introduction of adaptive classification margins proportional to the class distribution can achieve significant improvements in clustering and classification effectiveness. The proposed technique is tested for both clustering and classification, and promising results have been obtained.

Image classification of fruit and vegetables at supermarket self-checkouts is a complex problem. Significant variations in the size, shape and colour of objects are involved, along with potentially large variations in the environmental conditions, must be accommodated to implement such a robust and effective system. Convolution Neural Networks (CNNs) have shown promising results for object classifications. However, the scarcity of training datasets due to the diversity of varieties and applications of fruit and vegetable classification is a significant limitation to the CNN implementation for this task. To overcome this, we propose the use of transfer learning and ensemble technique. Specifically, a transfer learning based weighted average weight optimisation ensemble technique is applied to the weights of GoogleNet and MobileNet by transfer learning the pre-trained CNNs using a custom dataset. Two hyperparameter optimisation techniques have been applied in a sequential way to identify the effective weights progressively. The optimised weights are used as an input to a normalised exponential softmax layer to estimate the final probability distribution for classification. A comparative evaluation among standalone GoogleNet, MobileNet and different levels of ensemble has been presented, which supports the adoption of this technique as a solution in a real-world supermarket environment.

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. This paper proposes the use of a transfer learning technique with deep convolutional neural networks to learn how to visually identify the grasping configurations for a parallel plate gripper that will be used to grasp various household objects. The Red-Green-Blue-Depth (RGB-D) data from the Cornell Grasp Dataset is used to train the network model using an end-to-end learning method. With this method, we achieve a grasping configuration prediction accuracy of 93.91%.

Colour 2D barcodes, such as the MMCC barcode, have been developed recently to improve the data capacity of monochrome barcodes. However, the use of colour imposes greater challenges in decoding the symbols correctly as different lighting conditions on the barcode vary the values of the colours significantly. Hence, it is desirable to have an adaptive classification of the data cells so as to adapt to various lighting conditions. In this paper, we propose a classification method that is able to adapt to different lighting conditions during the classification of the MMCC data cells in a mobile environment. To highlight the effectiveness of this method, it is compared against the classification of the data cells performed in two different colour space representations, namely RGB and YCbCr.

Using p-Cycles to protect against single span failures in Wavelength-Division Multiplexing (WDM) networks has been widely studied. p-Cycle retains not only the speed of ring-like restoration, but also achieves the capacity efficiency over mesh networks. However, in selecting an optimal set of p-cycles to achieve the minimum spare capacity and fast computational time is an NP-hard problem. To address this issue, we propose a heuristic approach to iteratively select and refine a set of p-cycles, which contains two algorithms: the Heuristic p-Cycle Selection (HPS) algorithm, and the Refine Selected Cycles (RSC) algorithm. Our simulation results show that the proposed approach is within 3.5% redundancy difference from the optimal solution with very fast computation time even for large networks.

This paper introduces a novel 3-Dimensional (3D) vertically integrated adaptive computing system. This 3D-SoftChip is a combination of state-of-the-art processing and interconnection technology. It comprises the vertical integration of two chips (a Configurable Array Processor and an Intelligent Configurable Switch) through indium bump 3D interconnections. The Configurable Array Processor (CAP) is an array of heterogeneous processing elements (PEs) while the Intelligent Configurable Switch (ICS) comprises a switch block, 32-bit dedicated RISC processor for control, on-chip program/data memory, data frame buffer along with a Direct Memory Access (DMA) controller. This paper introduces the 3D-Softchip architecture for real-time communication and multimedia signal processing as a next generation computing system and describes the HW/SW codesign and verification methodology using SystemC.

A novel smart pixel opto-VLSI architecture to implement a complete 2-D wavelet transform of real-time captured images is presented. The smart pixel architecture enables the realisation of a highly parallel, compact, low power device capable of real-time capture, compression, decompression and display of images suitable for mobile multimedia communication applications.