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Ecosystem governance becomes gradually more relevant for a set of companies or actors characterized by symbiotic relations evolved on the top of a technological platform, i.e. a software ecosystem. In this study, we focus on the governance of a hardware-dependent software ecosystem. More specifically, we evaluate the governance model applied by Axis, a network video and surveillance camera produce
This thesis investigates the topic of joint positioning and radio channel estimation and prediction. Both positioning and radio channel estimation have a long history of research with many publications but the combination of the two has so far at large been left unexplored. The reason for studying this topic is twofold: improvement of positioning and improvement of radio channel prediction. Positi
Time synchronisation is crucial for distributed systems, and particularly for Wireless Sensor Networks (WSNs), where each node is executing concurrent operations to achieve a real-time objective. However, synchronisation is quite difficult to achieve in WSNs, due to the unpredictable deployment conditions and to physical effects like thermal stress, that cause drifts in the local node clocks. As a
The goal of this paper is to investigate the changing nature of India’s and Bangalore’s economy. This study will assess India’s change in the allotment of resources between different sectors within the state and country. To investigate the shifts, this paper will disaggregate the economy into three broad sectors, primary secondary and tertiary. It is expected that the much of the success of India
In this paper, we consider robust stability analysis of large-scale sparsely interconnected uncertain systems. By modeling the interconnections among the subsystems with integral quadratic constraints, we show that robust stability analysis of such systems can be performed by solving a set of sparse linear matrix inequalities. We also show that a sparse formulation of the analysis problem is equiv
This paper demonstrates how second-order time-delayed models adequate for PID controller synthesis can be identified from significantly shorter relay experiments, than used in previous publications to obtain first-order time-delayed models. Apart from having good noise robustness properties, the proposed method explicitly addresses non-stationary initial states of the dynamics to be identified, an
Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for micro
In this paper we study structure from motion problems for 1D radial cameras. Under this model the projection of a 3D point is a line in the image plane going through the principal point, which makes the model invariant to radial distortion and changes in focal length. It can therefore effectively be applied to uncalibrated image collections without the need for explicit estimation of camera intrin
Low rank inducing penalties have been proven to successfully uncover fundamental structures considered in computer vision and machine learning; however, such methods generally lead to non-convex optimization problems. Since the resulting objective is non-convex one often resorts to using standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), or other subgradient me
Traditional phase scan method for cavity phase and amplitude setting is offline and hard to track the variations of environment and operation points. An alternative beam loading based calibration method is investigated in this paper, which might become useful online/real time calibration method.
The higher efficiency and higher availability (fault-tolerant oriented) of RF & Cavity system (with beam loading) to operate at, the more dynamic details needs to be identified, so as to have the abilities (a) to work at nonlinearities, (b) to work close to limitation, and (c) to change operation point quickly and correctly. Dynamic detail identifications rely heavily on high precision measuri
This paper present a 6DoF-positioning method and shape estimation method for cars from monocular images.We pre-learn principal components, using Principal Component Analysis (PCA), from the shape of cars anduse a learnt encoder-decoder structure in order to position the cars and create binary masks of each camera instance. The proposed method is tailored towards usefulness for autonomous driving a
Factorization methods are frequently used for structure from motion problems (SfM). In the presence of noise they are able to jointly estimate camera matrices and scene points in overdetermined settings, without the need for accurate initial solutions. While the early formulations were restricted to affine models, recent approaches have been show to work with pinhole cameras by minimizing object s
With the purpose of examining biased updates in variance-reduced stochastic gradient methods, we introduce SVAG, a SAG/SAGA-like method with adjustable bias. SVAG is analyzed in a cocoercive root-finding setting, a setting which yields the same results as in the usual smooth convex optimization setting for the ordinary proximal-gradient method. We show that the same is not true for SVAG when biase
Internal positivity offers a computationally cheap certificate for external (inputoutput) positivity of a linear time-invariant system. However, the drawback with this certificate lies in its realization dependency. First, computing such a realization requires finding a polyhedral cone with a potentially high number of extremal generators that lifts the dimension of the state-space representation,
In this paper we study two major challenges in few-shot bioacoustic event detection: variable event lengths and false-positives. We use prototypical networks where the embedding function is trained using a multi-label sound event detection model instead of using episodic training as the proxy task on the provided training dataset. This is motivated by polyphonic sound events being present in the b
Millimeter-wave (mmWave) is one key enabler for high data rates in (beyond-)5G wireless communication networks. The use of directive beams and the shorter transmission range in mmWave communications make it easily obstructed or blocked by humans in dynamic real-world scenarios. Connectivity diversity, a solution to link disruption and blockage, could be achieved by deploying distributed multi-poin
This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XR/VR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadl
