Notes on ensembles of IoT,network functions and clouds for service-oriented computing and applications

Many advances have been introduced recently for service-oriented computing and applications (SOCA). The Internet of Things (IoT) has been pervasive in various application domains. Fog/Edge computing models have shown techniques that move computational and analytics capabilities from centralized data centers where most enterprise business services have been located to the edge where most customer’s Things and their data and actions reside. Network functions between the edge and the cloud can be dynamically provisioned and managed through service APIs. Microservice architectures are increasingly used to simplify engineering, deployment and management of distributed services in not only cloud-based powerful machines but also in light-weighted devices. Therefore, a key question for the research in SOCA is how do we leverage existing techniques and develop new ones for coping with and supporting the changes of data and computation resources as well as customer interactions arising in the era of IoT and Fog/Edge computing. In this editorial paper, we attempt to address this question by focusing on the concept of ensembles for IoT, network functions and clouds.     

Magnetoelectric properties of Ni/PZT/Ni layered composite for low field applications

A composite material when placed under the external magnetic/electric fields exhibits voltage/induced magnetization is known as magnetoelectric (ME) composite. Such composite materials should have ferroelectric and ferro/ferri magnetic phases as constituents. The magnetoelectric output is exhibited as a product property. Magnetoelectric composites are being used for variety of applications including resonators, filters, phase shifters, optical isolators, actuators and magnetic field sensors. Metal/ferroelectric/metal magnetoelectric composite using Ni and PZT as constituent phases has been fabricated in 2-2 composite pattern to study its product property. The paper presents magnetoelectric studies of Ni/PZT/Ni composite using low dc magnetic field magnetoelectric set-up. Using this ME set-up ME output of Ni/PZT/Ni composite is studied as a function of dc magnetic field. The results were analyzed to identify the useful magnetic field (dc and ac) range in which Ni/PZT/Ni sensor can be utilized for applications.     

Local search with constraint propagation and conflict-based heuristics

Search algorithms for solving csp (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflict-based techniques to efficiently guide the search. This new technique benefits from both classical approaches: a priori pruning of the search space from filtering-based search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decision-repair. Experiments done on open-shop scheduling problems show that our approach competes well with the best highly specialized algorithms.     

Stable model reference adaptive control in the presence of bounded disturbances

The adaptive control of a linear time-invariant plant in the presence of bounded disturbances is considered. In addition to the usual assumptions made regarding the plant transfer function, it is also assumed that the high-frequency gain kpof the plant and an upper bound on the magnitude of the controller parameters are known. Under these conditions the adaptive controller suggested assures the boundedness of all signals in the overall system.     

The mechanism of adhesion and printability of plasma processed PET films

Of the several techniques available for the surface modification, plasma processing has proved to be very appropriate. The low temperature plasma is a soft radiation source and it affects the material only over a few hundred Å deep, the bulk properties remaining unaffected. Plasma surface treatment also offers the advantage of greater chemical flexibility. PET films are widely used for packaging and electrical insulation. The studies of adhesion and printability properties are important. In the present study PET films are treated in air plasma for different time of treatment. The improvement in adhesion is studied by measuring T-peel and Lap shear strength. In addition, printability of plasma treated PET films is studied by cross test method. It has been found that printability increases considerably for plasma treatment of short duration. Therefore it is interesting to study the surface composition and morphology by contact angle measurement, ESCA and AFM. Surface energy and surface roughness can be directly correlated to the improvement in above-mentioned surface related properties. It has been found that the surface oxidation occurs containing polar functional groups such as C-O, COO. A correlation of all such observations from different techniques gives a comprehensive picture of the structure and surface composition of plasma treated PET films.     

Comparison of Expedient and Optima Reinforcement Schemes for Learning Systems

Stochastic automata models have been successfully used in the past for modeling learning systems. An automaton model with a variable structure reacts to inputs from a random environment by changing the probabilities of its actions. These changes are carried out using a reinforcement scheme in such a manner that the automaton evolves to a final structure which is satisfactory in some sense. Several reinforcement schemes have been proposed in the literature for updating the structure of automata [1–4]. Most of these are expedient schemes which in the limit yield structures which are better than a device that chooses the actions with equal probabilities irrespective of the environment's response. A few schemes have also been suggested recently which in the limit lead to a continuous selection of a single optimal action as the output of the automaton, when it operates in a stationary environment and are called optimal schemes [5–7]. The question naturally arises as to which of the schemes are to be preferred in practical applications. In view of the anticipated extensive use of learning schemes in multilevel decision-making systems this question of optimality versus expediency takes on particular significance. Consequently, a comparison has to be made not merely of individual automata schemes but also of the effectiveness of such schemes in situations involving several automata (e.g. stochastic games, multilevel systems).     

Robust Visual Tracking via Structured Multi-Task Sparse Learning

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $\ell _{p,q}$ mixed norms $(\text{ specifically} p\in \{2,\infty \}$ and $q=1),$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $L_1$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259–2272, 2011) is a special case of our MTT formulation (denoted as the $L_{11}$ tracker) when $p=q=1.$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.