An optimized stereo vision implementation for embedded systems: application to RGB and Infra-Red images

The aim of this paper is to demonstrate the applicability and the effectiveness of a computationally demanding stereo matching algorithm in different lowcost and low-complexity embedded devices, by focusing on the analysis of timing and image quality performances. Various optimizations have been implemented to allow its deployment on specific hardware architectures while decreasing memory and processing time requirements: (1) reduction of color channel information and resolution for input images, (2) low-level software optimizations such as parallel computation, replacement of function calls or loop unrolling, (3) reduction of redundant data structures and internal data representation. The feasibility of a stereovision system on a low cost platform is evaluated by using standard datasets and images taken from Infra-Red (IR) cameras. Analysis of the resulting disparity map accuracy with respect to a full-size dataset is performed as well as the testing of suboptimal solutions

Proportionality in Age Discrimination Cases - a Model Suitable for Socially Embedded Rights

This chapter discusses the use of proportionality in age discrimination cases before the Court of Justice of the European Union. It argues that the Court does not use this concept systematically - indeed it exposes some contradiction that make the case law seem arbitrary - and proposes a more fruitful use of the principle, which is in line with a modern conception of human rights. The chapter argues that the principle of proportionality stems from the time when human rights served the recently liberated burgeois elite in guarding their rights to property and liberty against the state. Today, states not only respect human rights (which is fully sufficient for this elite, who can rely on their inherited wealth to fend for themselves). They also protect and promote human rights, and these activities are a precondition for human rights to be practically relevant for the whole population. This also means that state activity, which is experienced as a limitation of rights to property and liberty by some, may constitute a measure to promote and protect human rights of others. In employment law - the only field where the EU ban on age discrimination is applied - this is a typical situation. If such a situation occurs, the principle of proportionality must be applied in a bifurcated way.It is not sufficient that the limitation of property rights is proportionate for the achievement of a public policy aim. If the aim of public policy is to enable the effective use of human rights, the limitation of the state action must be proportionate to the protection and promotion of those human rights. It is argued that the principle of proportionality is superior to less structures balancing acts (e.g. the Wednesbury principle), if it is applied both ways. Going over to the field of age discrimination, the chapter identifies a number of potentially colliding aims pursued in this field. Banning age discrimination may relate to genuine aims of anti-discrimination law if bias against older or very young workers is addressed. However, the EU ban of discrimination against all ages also serves to restructure employment law and policy to the age of flexibilisation, replacing the synchronisation principle that has been predominant for the welfare states of the 20th century. The former aim is related to human rights protection, while the latter aim is not (at least not always). This has consequences for applying the proportionality test. The chapter proposes different ways to argue the most difficult age discrimination cases, where anti-discrimination rationales and flexibilisation rationales clash

Influence of embedded length on strength of BFRP rods bonded parallel to the grain in low grade timber by pullout-bending tests

Bonded-in rod connections in timber possess many desirable attributes in terms of efficiency, manufacture, performance, aesthetics and cost. In recent years research has been conducted on such connections using fibre reinforced polymers (FRPs) as an alternative to steel. This research programme investigates the pull-out capacity of Basalt FRP rods bonded-in in low grade Irish Sitka Spruce. Embedded length is thought to be the most influential variable contributing to pull- out capacity of bonded-in rods after rod diameter. Previous work has established an optimum embedded length of 15 times the hole diameter. However, this work only considered the effects of axial stress on the bond using a pull-compression testing system which may have given an artificially high pull out capacity as bending effects were neglected. A hinge system was utilised that allows the effects of bending force to be taken in to consideration along with axial forces in a pull-out test. This paper describes an experimental programme where such pull-bending tests were carried out on samples constructed of 12mm diameter BFRP bars with a 2mm glueline thickness and embedded lengths between 80mm and 280mm bonded-in to low-grade timber with an epoxy resin. Nine repetitions of each were tested. A clear increase in pull-out strength was found with increasing embedded length.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Energy versus data integrity trade-offs in embedded high-density logic compatible dynamic memories

Current variation aware design methodologies, tuned for worst-case scenarios, are becoming increasingly pessimistic from the perspective of power and performance. A good example of such pessimism is setting the refresh rate of DRAMs according to the worst-case access statistics, thereby resulting in very frequent refresh cycles, which are responsible for the majority of the standby power consumption of these memories. However, such a high refresh rate may not be required, either due to extremely low probability of the actual occurrence of such a worst-case, or due to the inherent error resilient nature of many applications that can tolerate a certain number of potential failures. In this paper, we exploit and quantify the possibilities that exist in dynamic memory design by shifting to the so-called approximate computing paradigm in order to save power and enhance yield at no cost. The statistical characteristics of the retention time in dynamic memories were revealed by studying a fabricated 2kb CMOS compatible embedded DRAM (eDRAM) memory array based on gain-cells. Measurements show that up to 73% of the retention power can be saved by altering the refresh time and setting it such that a small number of failures is allowed. We show that these savings can be further increased by utilizing known circuit techniques, such as body biasing, which can help, not only in extending, but also in preferably shaping the retention time distribution. Our approach is one of the first attempts to access the data integrity and energy tradeoffs achieved in eDRAMs for utilizing them in error resilient applications and can prove helpful in the anticipated shift to approximate computing.