ZIGBEE SIGNALS ANALYZER

Широкое распространение беспроводных технологий требует развития средств контроля за устройствами и сетями передачи данных и, в частности, за беспроводными персональными сетями стандарта ZigBee. Известные способы поиска и приема сигналов ZigBee, требующие осуществления предварительной оценки частотного рассогласования, обладают высокой вычислительной сложностью. Некогерентный способ приема сигналов ZigBee не требует больших вычислительных ресурсов, но не обеспечивает удовлетворительную помехоустойчивость. Целью работы являлась разработка комбинированного алгоритма обнаружения и приема сигналов ZigBee. На основе разработанного алгоритма построен анализатор, позволяющий идентифицировать персональные сети, их передающее и приемное устройства. Новизна: для приёма сигналов при неизвестной частотной расстройке используется сочетание когерентной обработки на коротких временных интервалах с их последующим некогерентным накоплением. Предложенный алгоритм способен эффективно работать в неблагоприятных условиях приема и обладает относительно невысокой вычислительной сложностью. Результат: использование представленного решения позволяет выполнять обнаружение и прием сигналов ZigBee радиодоступных источников, идентифицировать персональную сеть, передающее и приемное устройства в этой сети. Практическая значимость: предложенный алгоритм может использоваться для построения анализатора сигналов ZigBee на программно-определяемом радиоприемном устройстве с полосой одновременной обработки сигналов от 2 МГц. Реализованный в универсальных цифровых радиоприемных устройствах семейства АРГАМАК алгоритм применяется в системах поиска и локализации несанкционированных источников радиоизлучений в контролируемых объектах The widespread adoption of wireless technologies requires the development of controls over devices and data networks and in particular over ZigBee wireless personal networks. Known methods of searching for and receiving ZigBee signals, which require a preliminary assessment of frequency offset, have a high computational complexity. The non-coherent method of receiving ZigBee signals does not require large computing resources but does not provide satisfactory noise immunity. The purpose of the work was to develop a combined algorithm for detecting and receiving ZigBee signals. Based on the developed algorithm, we built an analyzer that allows you to identify personal networks, their transmitting and receiving devices. Novelty: to receive signals with an unknown frequency offset, we used a combination of coherent processing at short time intervals with their subsequent non coherent accumulation. The proposed algorithm is able to work effectively in unfavorable reception conditions and has a relatively low computational complexity. Result: the use of the presented solution allows you to detect and receive ZigBee signals from radio-accessible sources, identify a personal network, a transmitting and receiving device in this network Practical relevance: the proposed method can be used to build a ZigBee signal analyzer on an SDR with a band of simultaneous signal processing from 2 MHz. The ZigBee network analyzer, implemented on the basis of a digital radio receiver of the ARGAMAK family, serves as the basis for the device for searching and localizing unauthorized radio sources in controlled objects

Augmented Lagrangian–Based First-Order Methods for Convex-Constrained Programs with Weakly Convex Objective

First-order methods (FOMs) have been widely used for solving large-scale problems. A majority of existing works focus on problems without constraint or with simple constraints. Several recent works have studied FOMs for problems with complicated functional constraints. In this paper, we design a novel augmented Lagrangian (AL)–based FOM for solving problems with nonconvex objective and convex constraint functions. The new method follows the framework of the proximal point (PP) method. On approximately solving PP subproblems, it mixes the usage of the inexact AL method (iALM) and the quadratic penalty method, whereas the latter is always fed with estimated multipliers by the iALM. The proposed method achieves the best-known complexity result to produce a near Karush–Kuhn–Tucker (KKT) point. Theoretically, the hybrid method has a lower iteration-complexity requirement than its counterpart that only uses iALM to solve PP subproblems; numerically, it can perform significantly better than a pure-penalty-based method. Numerical experiments are conducted on nonconvex linearly constrained quadratic programs. The numerical results demonstrate the efficiency of the proposed methods over existing ones.

An Active Set Trust-Region Method for Bound-Constrained Optimization

This paper discusses an active set trust-region algorithm for bound-constrained optimization problems. A sufficient descent condition is used as a computational measure to identify whether the function value is reduced or not. To get our complexity result, a critical measure is used which is computationally better than the other known critical measures. Under the positive definiteness of approximated Hessian matrices restricted to the subspace of non-active variables, it will be shown that unlimited zigzagging cannot occur. It is shown that our algorithm is competitive in comparison with the state-of-the-art solvers for solving an ill-conditioned bound-constrained least-squares problem.

On BMRN*-colouring of planar digraphs

International audience In a recent work, Bensmail, Blanc, Cohen, Havet and Rocha, motivated by applications for TDMA scheduling problems, have introduced the notion of BMRN*-colouring of digraphs, which is a type of arc-colouring with particular colouring constraints. In particular, they gave a special focus to planar digraphs. They notably proved that every planar digraph can be 8-BMRN*-coloured, while there exist planar digraphs for which 7 colours are needed in a BMRN*-colouring. They also proved that the problem of deciding whether a planar digraph can be 3-BMRN*-coloured is NP-hard. In this work, we pursue these investigations on planar digraphs, in particular by answering some of the questions left open by the authors in that seminal work. We exhibit planar digraphs needing 8 colours to be BMRN*-coloured, thus showing that the upper bound of Bensmail, Blanc, Cohen, Havet and Rocha cannot be decreased in general. We also generalize their complexity result by showing that the problem of deciding whether a planar digraph can be k-BMRN*-coloured is NP-hard for every k ∈ {3,...,6}. Finally, we investigate the connection between the girth of a planar digraphs and the least number of colours in its BMRN*-colourings.

A New Predictor-corrector Infeasible Interior-point Algorithm for Linear Optimization in aWide Neighborhood

In this paper, we propose a Mizuno-Todd-Ye type predictor-corrector infeasible interior-point method for linear optimization based on a wide neighborhood of the central path. According to Ai-Zhang’s original idea, we use two directions of distinct and orthogonal corresponding to the negative and positive parts of the right side vector of the centering equation of the central path. In the predictor stage, the step size along the corresponded infeasible directions to the negative part is chosen. In the corrector stage by modifying the positive directions system a full-Newton step is removed. We show that, in addition to the predictor step, our method reduces the duality gap in the corrector step and this can be a prominent feature of our method. We prove that the iteration complexity of the new algorithm is 𝒪(n log ɛ−1), which coincides with the best known complexity result for infeasible interior-point methods, where ɛ > 0 is the required precision. Due to the positive direction new system, we improve the theoretical complexity bound for this kind of infeasible interior-point method [1] by a factor of n . Numerical results are also provided to demonstrate the performance of the proposed algorithm.

Bad influence: social networks, elite brokerage, and the construction of alliances

If all states want to survive, why do some of them enter unpropitious alliances? International Relations (IR) theory’s conventional answer is that imperfect information and systemic complexity result in miscalculation. This explanation begs the question: any alliance that fails is a miscalculated one, so the puzzle is not whether but why such mistakes are made. This article imports from recent scholarship on network theory and interpersonal trust to offer an alternative explanation. Alliances are not entities ethereally formed out of strategic imperatives, but products of interactions within transnational social networks of political, military, and business elites in the prospective allies. Such interactions enable alliances because people who are connected to each other through mutual association or previous exchanges develop mutual trust and gain subjective certainty about each other’s intentions and capabilities, which points at a previously ignored mechanism in alliance behavior: brokerage. In a case study that combines theory-based archival research and social network analysis, this article uses historical evidence on the Turco-German alliance to empirically demonstrate the brokerage role Colmar von der Goltz, the head of the German military mission to the Ottoman Empire, played in the two countries’ relations at the turn of the century and their eventual alliance in the First World War. The analysis points at a potential means of bridging IR, history, and sociology while expanding our understanding of alliance behavior and providing policy-relevant insights on geo-economic competition and the weaponization of interdependence at a time of growing strategic rivalry on the world stage.

Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems

Using a discrete dynamical system model for a networked social system, we consider the problem of learning a class of local interaction functions in such networks. Our focus is on learning local functions which are based on pairwise disjoint coalitions formed from the neighborhood of each node. Our work considers both active query and PAC learning models. We establish bounds on the number of queries needed to learn the local functions under both models. We also establish a complexity result regarding efficient consistent learners for such functions. Our experimental results on synthetic and real social networks demonstrate how the number of queries depends on the structure of the underlying network and number of coalitions.

Endorsement and visual complexity in food advertising on Instagram

Purpose The purpose of this paper is to investigate the differences in consumer pleasure, arousal and purchase intention when consumers encounter food advertising on Instagram using different endorsers and visual complexity levels. Design/methodology/approach An experimental design was conducted involving 180 undergraduate students from several universities in Surabaya, Indonesia. The participants had actively used Instagram for at least one year. Findings Food ads endorsed by a celebrity generate more pleasure and arousal than those endorsed by food experts. Food advertising using high levels of visual complexity cues generates more pleasure and arousal than less complex advertising. However, less complex food ads using food experts create greater pleasure than those endorsed by celebrities. Consumer pleasure and arousal were significant mediators of the impact of endorser type and visual complexity on consumer purchase intentions. Practical implications As celebrities and higher levels of visual complexity result in more favorable responses to Instagram ads, food marketers need to consider increasing visual complexity when using celebrities in advertising by adding more objects, using more colors, objects, or textures and incorporating asymmetric elements in the advertisements. Originality/value This is one of the few studies comparing the effectiveness of celebrity and expert endorsers in Instagram advertising. Also, this research extends the existing knowledge about visual complexity in the context of social media advertising.

A Highly Flexible Approach on the Steady-State Analysis of Innovative Micro Gas Turbine Cycles

Steady-state simulation is an important method to investigate thermodynamic processes. This is especially true for innovative micro gas turbine (MGT)-based cycles as the complexity of such systems grows. Therefore, steady-state simulation tools are required that ensure large flexibility and computation robustness. As the increased system complexity result often in more extensive parameter studies also a fast computation speed is required. While a number of steady-state simulation tools for MGT-based systems are described and applied in literature, the solving process of such tools is rarely explained. However, this solving process is crucial to achieve a robust and fast computation within a physically meaningful range. Therefore, a new solver routine for a steady-state simulation tool developed at the DLR Institute of Combustion Technology is presented in detail in this paper. The solver routine is based on Broyden's method. It considers boundaries during the solving process to maintain a physically and technically meaningful solution process. Supplementary methods are implemented and described which improve the computation robustness and speed. Furthermore, some features of the resulting steady-state simulation tool are presented. Exemplary applications of a hybrid power plant (HyPP), an inverted Brayton cycle (IBC), and an aircraft auxiliary power unit (APU) show the capabilities of the presented solver routine and the steady-state simulation tool. It is shown that the new solver routine is superior to the standard Simulink algebraic solver in terms of system evaluation and robustness for the given applications.

On the Conditional Logic of Simulation Models

We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature. Our main results include a series of axiomatizations, allowing comparison between this framework and existing frameworks (normality-ordering models, causal structural equation models), and a complexity result establishing NP-completeness of the satisfiability problem. Perhaps surprisingly, some of the basic logical principles common to all existing approaches are invalidated in our causal simulation approach. We suggest that this additional flexibility is important in modeling some intuitive examples.