Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

A multi-hierarchical motorway ramp signalling strategy

Ramp signalling is an access control for motorways, in which a traffic signal is placed at on-ramps to regulate the rate of vehicles entering the motorway and thus to preserve the motorway capacity. In general, ramp signalling algorithms fall into two categories: local control and coordinated control by their effective scope. Coordinated ramp signalling strategies make use of measurements from the entire motorway network to operate individual ramp signals for the optimal performances at the network level. This study proposes a multi-hierarchical strategy for coordinated ramp signalling. The strategy is structured in two layers. At the higher layer with a longer update interval, coordination group is assembled and disassembled based on the location of high-risk breakdown flow. At the lower layer with a shorter update interval, individual ramps are hired to serve the coordination and are also released based on the prevailing congestion level on the ramp. This strategy is modelled and applied to the northbound Pacific Motorway micro-simulation platform (AIMSUN). The simulation results show an effective congestion mitigation of the proposed strategy.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Classifying natural aerial scenery for autonomous aircraft emergency landing

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

Non-linear model predictive control for UAVs with slung/swung load

Suspended loads on UAVs can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present software and flight system architecture to test controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions.

Plasma-enabled, catalyst-free growth of carbon nanotubes on mechanically-written Si features with arbitrary shape

Simple, rapid, catalyst-free synthesis of complex patterns of long, vertically aligned multiwalled carbon nanotubes, strictly confined within mechanically-written features on a Si(1 0 0) surface is reported. It is shown that dense arrays of the nanotubes can nucleate and fully fill the features when the low-temperature microwave plasma is in a direct contact with the surface. This eliminates additional nanofabrication steps and inevitable contact losses in applications associated with carbon nanotube patterns. Using metal catalyst has long been considered essential for the nucleation and growth of surface-supported carbon nanotubes (CNTs) [1] and [2]. Only very recently, the possibility of CNT growth using non-metallic (e.g., oxide [3] and SiC [4]) catalysts or artificially created carbon-enriched surface layers [5] has been demonstrated. However, successful integration of carbon nanostructures into Si-based nanodevice platforms requires catalyst-free growth, as the catalyst nanoparticles introduce contact losses, and their catalytic activity is very difficult to control during the growth [6]. Furthermore, in many applications in microfluidics, biological and molecular filters, electronic, sensor, and energy conversion nanodevices, the CNTs need to be arranged in specific complex patterns [7] and [8]. These patterns need to contain the basic features (e.g., lines and dots) written using simple procedures and fully filled with dense arrays of high-quality, straight, yet separated nanotubes. In this paper, we report on a completely metal or oxide catalyst-free plasma-based approach for the direct and rapid growth of dense arrays of long vertically-aligned multi-walled carbon nanotubes arranged into complex patterns made of various combinations of basic features on a Si(1 0 0) surface written using simple mechanical techniques. The process was conducted in a plasma environment [9] and [10] produced by a microwave discharge which typically generates the low-temperature plasmas at the discharge power below 1 kW [11]. Our process starts from mechanical writing (scribing) a pattern of arbitrary features on pre-treated Si(1 0 0) wafers. Before and after the mechanical feature writing, the Si(1 0 0) substrates were cleaned in an aqueous solution of hydrofluoric acid for 2 min to remove any possible contaminations (such as oil traces which could decompose to free carbon at elevated temperatures) from the substrate surface. A piece of another silicon wafer cleaned in the same way as the substrate, or a diamond scriber were used to produce the growth patterns by a simple arbitrary mechanical writing, i.e., by making linear scratches or dot punctures on the Si wafer surface. The results were the same in both cases, i.e., when scratching the surface by Si or a diamond scriber. The procedure for preparation of the substrates did not involve any possibility of external metallic contaminations on the substrate surface. After the preparation, the substrates were loaded into an ASTeX model 5200 chemical vapour deposition (CVD) reactor, which was very carefully conditioned to remove any residue contamination. The samples were heated to at least 800 °C to remove any oxide that could have formed during the sample loading [12]. After loading the substrates into the reactor chamber, N2 gas was supplied into the chamber at the pressure of 7 Torr to ignite and sustain the discharge at the total power of 200 W. Then, a mixture of CH4 and 60% of N2 gases were supplied at 20 Torr, and the discharge power was increased to 700 W (power density of approximately 1.49 W/cm3). During the process, the microwave plasma was in a direct contact with the substrate. During the plasma exposure, no external heating source was used, and the substrate temperature (∼850 °C) was maintained merely due to the plasma heating. The features were exposed to a microwave plasma for 3–5 min. A photograph of the reactor and the plasma discharge is shown in Fig. 1a and b.

Effective control of nanostructured phases in rapid, room-temperature synthesis of nanocrystalline Si in high-density plasmas

An innovative and effective approach based on low-pressure, low-frequency, thermally nonequilibrium, high-density inductively coupled plasmas is proposed to synthesize device-quality nanocrystalline silicon (nc-Si) thin films at room temperature and with very competitive growth rates. The crystallinity and microstructure properties (including crystal structure, crystal volume fraction, surface morphology, etc.) of this nanostructured phase of Si can be effectively tailored in broad ranges for different device applications by simply varying the inductive rf power density from 25.0 to 41.7 mW/cm3. In particular, at a moderate rf power density of 41.7 mW/cm3, the nc-Si films feature a very high growth rate of 2.37 nm/s, a high crystalline fraction of 86%, a vertically aligned columnar structure with the preferential (111) growth orientation and embedded Si quantum dots, as well as a clean, smooth and defect-free interface. We also propose the formation mechanism of nc-Si thin films which relates the high electron density and other unique properties of the inductively coupled plasmas and the formation of the nanocrystalline phase on the Si surface.

Malleability attacks on multi-party key agreement protocols

Multi-party key agreement protocols indirectly assume that each principal equally contributes to the final form of the key. In this paper we consider three malleability attacks on multi-party key agreement protocols. The first attack, called strong key control allows a dishonest principal (or a group of principals) to fix the key to a pre-set value. The second attack is weak key control in which the key is still random, but the set from which the key is drawn is much smaller than expected. The third attack is named selective key control in which a dishonest principal (or a group of dishonest principals) is able to remove a contribution of honest principals to the group key. The paper discusses the above three attacks on several key agreement protocols, including DH (Diffie-Hellman), BD (Burmester-Desmedt) and JV (Just-Vaudenay). We show that dishonest principals in all three protocols can weakly control the key, and the only protocol which does not allow for strong key control is the DH protocol. The BD and JV protocols permit to modify the group key by any pair of neighboring principals. This modification remains undetected by honest principals.

Enhanced stochastic mobility prediction with multi-output Gaussian processes

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

Self-organized ZnO nanodot arrays : effective control using SiNx interlayers and low-temperature plasmas

An advanced inductively coupled plasma (ICP)-assisted rf magnetron sputtering deposition method is developed to synthesize regular arrays of pear-shaped ZnO nanodots on a thin SiNx buffer layer pre-deposited onto a silicon substrate. It is shown that the growth of ZnO nanodots obey the cubic root-law behavior. It is also shown that the synthesized ZnO nanodots are highly-uniform, controllable by the experimental parameters, and also feature good structural and photoluminescent properties. These results suggest that this custom-designed ICP-based technique is very effective and highly-promising for the synthesis of property- and size-controllable highly-uniform ZnO nanodots suitable for next-generation light emitting diodes, energy storage, UV nanolasers, and other applications.

Slaving and release in co-infection control

Background Animal and human infection with multiple parasite species is the norm rather than the exception, and empirical studies and animal models have provided evidence for a diverse range of interactions among parasites. We demonstrate how an optimal control strategy should be tailored to the pathogen community and tempered by species-level knowledge of drug sensitivity with use of a simple epidemiological model of gastro-intestinal nematodes. Methods We construct a fully mechanistic model of macroparasite co-infection and use it to explore a range of control scenarios involving chemotherapy as well as improvements to sanitation. Results Scenarios are presented whereby control not only releases a more resistant parasite from antagonistic interactions, but risks increasing co-infection rates, exacerbating the burden of disease. In contrast, synergisms between species result in their becoming epidemiologically slaved within hosts, presenting a novel opportunity for controlling drug resistant parasites by targeting co-circulating species. Conclusions Understanding the effects on control of multi-parasite species interactions, and vice versa, is of increasing urgency in the advent of integrated mass intervention programmes.

Health education and the control of intestinal worm infections in China : a new vision

Background: The transmission of soil-transmitted helminths (STHs) is associated with poverty, poor hygiene behaviour, lack of clean water and inadequate waste disposal and sanitation. Periodic administration of benzimidazole drugs is the mainstay for global STH control but it does not prevent re-infection, and is unlikely to interrupt transmission as a stand-alone intervention. Findings: We reported recently on the development and successful testing in Hunan province, PR China, of a health education package to prevent STH infections in Han Chinese primary school students. We have recently commenced a new trial of the package in the ethnically diverse Xishuangbanna autonomous prefecture in Yunnan province and the approach is also being tested in West Africa, with further expansion into the Philippines in 2015. Conclusions: The work in China illustrates well the direct impact that health education can have in improving knowledge and awareness, and in changing hygiene behaviour. Further, it can provide insight into the public health outcomes of a multi-component integrated control program, where health education prevents re-infection and periodic drug treatment reduces prevalence and morbidity.

Space vector modulated cascade multi-level inverter for PMSG wind generation systems

Modulation and control of a cascade multilevel inverter, which has a high potential in future wind generation applications, are presented. The inverter is a combination of a high power, three level “bulk inverter” and a low power “conditioning inverter”. To minimize switching losses, the bulk inverter operates at a low frequency producing square wave outputs while high frequency conditioning inverter is used to suppress harmonic content produced by the bulk inverter output. This paper proposes an improved Space Vector Modulation (SVM) algorithm and a neutral point potential balancing technique for the inverter. Furthermore, a maximum power tracking controller for the Permanent Magnet Synchronous Generator (PMSG) is described in detail. The proposed SVM technique eliminates most of the computational burdens on the digital controller and renders a greater controllability under varying DC-link voltage conditions. The DC-link capacitor voltage balancing of both bulk and conditioning inverters is carried out using Redundant State Selection (RSS) method and is explained in detail. Experimental results are presented to verify the proposed modulation and control techniques.