Recently several fusion and switching based approaches have been presented to solve the problem of Visual Place Recognition. In spite of these systems demonstrating significant boost in VPR performance they each have their own set of limitations. The multi-process fusion systems usually involve employing brute force and running all available VPR techniques simultaneously while the switching method attempts to negate this practise by only selecting the best suited VPR technique for given query image. But switching does fail at times when no available suitable technique can be identified. An innovative solution would be an amalgamation of the two otherwise discrete approaches to combine their competitive advantages while negating their shortcomings. The proposed, Switch-Fuse system, is an interesting way to combine both the robustness of switching VPR techniques based on complementarity and the force of fusing the carefully selected techniques to significantly improve performance. Our system holds a structure superior to the basic fusion methods as instead of simply fusing all or any random techniques, it is structured to first select the best possible VPR techniques for fusion, according to the query image. The system combines two significant processes, switching and fusing VPR techniques, which together as a hybrid model substantially improve performance on all major VPR data sets illustrated using PR curves.

Scene classification is a well-established area of computer vision research that aims to classify a scene image into pre-defined categories such as playground, beach and airport. Recent work has focused on increasing the variety of pre-defined categories for classification, but so far failed to consider two major challenges: changes in scene appearance due to lighting and open set classification (the ability to classify unknown scene data as not belonging to the trained classes). Our first contribution, SceneVLAD, fuses scene classification and visual place recognition CNNs for appearance invariant scene classification that outperforms state-of-the-art scene classification by a mean F1 score of up to 0.1. Our second contribution, OpenSceneVLAD, extends the first to an open set classification scenario using intra-class splitting to achieve a mean increase in F1 scores of up to 0.06 compared to using state-of-the-art openmax layer. We achieve these results on three scene class datasets extracted from large scale outdoor visual localisation datasets, one of which we collected ourselves.

VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques archive state-of-the-art VPR performance but are computationally intensive and energy demanding. Binary neural networks (BNN) have been recently proposed to address VPR efficiently. Although a typical BNN is an order of magnitude more efficient than a CNN, its processing time and energy usage can be further improved. In a typical BNN, the first convolution is not completely binarized for the sake of accuracy. Consequently, the first layer is the slowest network stage, requiring a large share of the entire computational effort. This paper presents a class of BNNs for VPR that combines depthwise separable factorization and binarization to replace the first convolutional layer to improve computational and energy efficiency. Our best model achieves higher VPR performance while spending considerably less time and energy to process an image than a BNN using a non-binary convolution as a first stage.

Visual place recognition (VPR) - a fundamental task in computer vision and robotics - is the problem of identifying a place mainly based on visual information. View-point and appearance changes, such as due to weather and seasonal variations, make this task challenging. Currently, there is no universal VPR technique that can work in all types of environments, on a variety of robotic platforms, and under a wide range of viewpoint and appearance changes. Recent work has shown the potential of combining different VPR methods intelligently by evaluating complementarity for some specific VPR datasets to achieve better performance. This, however, requires ground truth information (correct matches) which is not available when a robot is deployed in a real-world scenario. Moreover, running multiple VPR techniques in parallel may be prohibitive for resource-constrained embedded platforms. To overcome these limitations, this paper presents a probabilistic complementarity-based switching VPR system, SwitchHit. Our proposed system consists of multiple VPR techniques, however, it does not simply run all techniques at once, rather predicts the probability of correct match for an incoming query image and dynamically switches to another complementary technique if the probability of correctly matching the query is below a certain threshold. This innovative use of multiple VPR techniques allow our system to be more efficient and robust than other combined VPR approaches employing brute force and running multiple VPR techniques at once. Thus making it more suitable for resource constrained embedded systems and achieving an overall superior performance from what any individual VPR method in the system could have by achieved running independently.

Improving result accuracy in approximate computing (AC)-based real-time applications without violating deadlines has recently become an active research domain. Execution time of AC real-time tasks can individually be separated into: execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve the result accuracy of the initial result within a given deadline. However, obtaining higher result accuracy at the cost of enhanced execution time may lead to deadline violation, along with higher energy usage. We present ACCURATE, a novel hybrid offline–online approximate real-time scheduling approach that first schedules AC-based tasks on multicore with an objective to maximize result accuracy and determines operational processing speeds for each task constrained by system-wide power limit, deadline, and task dependency. At runtime, by employing a way-sharing technique (WH_LLC) at the last level cache (LLC), ACCURATE improves performance, which is further leveraged, to enhance result accuracy by executing more from the optional part and to improve the energy efficiency of the cache by turning off a controlled number of cache ways. ACCURATE also exploits the slacks either to improve the result accuracy of the tasks or to enhance the energy efficiency of the underlying system, or both. ACCURATE achieves 85% QoS with 36% average reduction in cache leakage consumption with a 24% average gain in energy-delay product (EDP) for a 4-core-based chip multiprocessor (CMP) with 6.4% average improvement in performance.

Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.

Visual place recognition (VPR) is a robot’s ability to determine whether a place was visited before using visual data. While conventional handcrafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this article, we take a multistep approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the VPR problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed by seven times.

Field-programmable gate arrays (FPGAs) offer the flexibility of general-purpose processors along with the performance efficiency of dedicated hardware that essentially renders it as a platform of choice for modern-day robotic systems for achieving real-time performance. Such robotic systems when deployed in harsh environments often get plagued by faults due to extreme conditions. Consequently, the real-time applications running on FPGA become susceptible to errors which call for a reliability-aware task scheduling approach, the focus of this article. We attempt to address this challenge using a hybrid offline-online approach. Given a set of periodic real-time tasks that require to be executed, the offline component generates a feasible preemptive schedule with specific preemption points. At runtime, these preemption events are utilized for fault detection. Upon detecting any faulty execution at such distinct points, the reliability-aware scheduling approach, RASA, orchestrates the recovery mechanism to remediate the scenario without jeopardizing the predefined schedule. Effectiveness of the proposed strategy has been verified through simulation-based experiments and we observed that the RASA is able to achieve 72% of task acceptance rate even under 70% of system workloads with high fault occurrence rates.

This paper proposes an energy efficient real-time scheduling strategy called EnSuRe, which (i) executes real-time tasks on low power consuming primary processors to enhance the system accuracy by maintaining the deadline and (ii) provides reliability against a fixed number of transient faults by selectively executing backup tasks on high power consuming backup processor. Simulation results reveal that EnSuRe consumes nearly 25% less energy, compared to existing techniques, while satisfying the fault tolerance requirements. EnSuRe is also able to achieve 75% system accuracy with 50% system utilisation. Further, the obtained simulation outcomes are validated on benchmark tasks via a fault injection framework on Xilinx ZYNQ APSoC heterogeneous dual core platform.

Abstract Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench ) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates.

This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criteria to assess the ‘memorability’ of an image for visual place recognition. A dataset namely ‘ESSEX3IN1’ is created, composed of highly confusing images from indoor, outdoor and natural scenes for analysis. When used in conjunction with state-of-the-art visual place recognition methods, the proposed framework provides significant performance boost to these techniques, as evidenced by results on ESSEX3IN1 and other public datasets.

As one of the most important components in the embedded systems, the SRAM are sensitive to radiation effects. When the embedded systems working in the extreme radiation environments, the bit flips could occur frequently and decrease the reliability of the systems significantly. In this paper, the self-scrubbing RAM scheme is proposed for light wight embedded systems in the extreme radiation environments. In the scheme, both scrubbing and ECC are used to mitigate the large number of the errors in the RAMs. The separately scrubber is designed to scrub the RAM separately. Therefore it is can be able to operating the scrubbing, when the CPUs are busy. In addition, the scrubber is a portable modules and the hardware costs do not grow with the size of the available RAM. The results of the real world radiation experiments show that it can correct most errors in the RAM under neutron radiation where the errors rates in unhardened RAMs is approximately 1.2bit/(KB·h). The results of the 6 hours radiation experiments show that the error rates of in the conventional ECC RAM is approximately 4.3×10-4bit/(KB·h), while the self-scrubbing RAMs is less than 8.7×10-5bit/(KB·h).

Robots can be used to deal with hazardous materials like nuclear waste. Unfortunately, electronic components are also susceptible to radiation effects. Current proposals to tackle this issue solve only parts of the problems for the specific scenarios and the specific types of radiation. At the same time, current computational devices should provide run-time capabilities to monitor and adapt to different situations. In this paper, we target a possible solution presenting a framework which provides the flexibility to employ fault-tolerant techniques on distributed systems. As proof of concept, we target a fault-tolerant technique to extend the operating time of systems in harsh environments. Results show a very low overhead, of few microseconds, to execute a majority voter with replicated tasks.

This article presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods.

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

A potential threat to embedded systems is the execution of unknown or malicious software capable of triggering harmful system behavior, aimed at theft of sensitive data or causing damage to the system. Commercial off-the-shelf embedded devices, such as embedded medical equipment, are more vulnerable as these type of products cannot be amended conventionally or have limited resources to implement protection mechanisms. In this paper, we present a self-organizing map (SOM)-based approach to enhance embedded system security by detecting abnormal program behavior. The proposed method extracts features derived from processor's program counter and cycles per instruction, and then utilises the features to identify abnormal behavior using the SOM. Results achieved in our experiment show that the proposed method can identify unknown program behaviors not included in the training set with over 98.4% accuracy.

We present a novel methodology for the fusion of multiple (two or more) images using the multivariate extension of empirical mode decomposition (MEMD). Empirical mode decomposition (EMD) is a data-driven method which decomposes input data into its intrinsic oscillatory modes, known as intrinsic mode functions (IMFs), without making a priori assumptions regarding the data. We show that the multivariate and multidimensional extensions of EMD are suitable for image fusion purposes. We further demonstrate that while multidimensional extensions, by design, may seem more appropriate for tasks related to image processing, the proposed multivariate extension outperforms these in image fusion applications owing to its mode-alignment property for IMFs. Case studies involving multi-focus image fusion and pan-sharpening of multi-spectral images are presented to demonstrate the effectiveness of the proposed method.

Speeded-Up Robust Features is a feature extraction algorithm designed for real-time execution, although this is rarely achievable on low-power hardware such as that in mobile robots. One way to reduce the computation is to discard some of the scale-space octaves, and previous research has simply discarded the higher octaves. This paper shows that this approach is not always the most sensible and presents an algorithm for choosing which octaves to discard based on the properties of the imagery. Results obtained with this best octaves algorithm show that it is able to achieve a significant reduction in computation without compromising matching performance.