The use of gamification has increased in companies in recent years and is used among other things to accelerate learning, increase motivation and engagement. Gamification is defined as the use of game elements in a non-game context. This study aims to investigate whether the use of gamification raises the financial value of a company. The purpose of the study is to expand the knowledge of gamification so that it can be used more efficiently and more frequently in businesses. The research was conducted with an event study on companies that are listed on Nasdaq Stockholm. With the theory of the efficient market hypothesis as a foundation, investors' willingness to buy shares in a company as a direct measure of news publishing on a company's gamification use was examined. The result, which is based on 91 articles from Swedish news sources, illustrates that news about companies' use of gamification does not have a significant impact on their share price. Therefore, in line with the efficient market hypothesis, the news about gamification does not increase the value of the companies, which is the conclusion of this study. The result also shows that the choice of gamification technology or industry that the company is active in does not have an impact on the significance of the results. The study concludes that a correlation between gamification and a company's financial value may exist, although the results of this study indicate the contrary. / Användningen av gamification har ökat hos företag de senaste åren och används bland annat för att skynda på inlärning, höja motivation och öka engagemang. Gamification definieras som användandet av spelelement utanför en spelkontext. Denna studie syftar till att utforska om användandet av gamification höjer det finansiella värdet hos ett bolag. Anledningen till studien är att expandera kunskapen om gamification, för att det ska kunna användas effektivare och mer frekvent i företagande. Undersökningen genomfördes med en eventstudie på företag som är noterade på Stockholmsbörsen Nasdaq. Med teorin om den effektiva marknadshypotesen i grunden granskades investerares vilja att köpa aktier i ett bolag som en direkt åtgärd av nyheters publicering om ett bolags användande av gamification. Resultatet, som är baserat på 91 artiklar från svenska nyhetskällor, åskådliggör att nyheter om företags användande av gamification inte har någon signifikant påverkan på företaget aktiekurs. I linje med den effektiva marknadsanalysen, så har därför inte nyheterna om gamification ökat värdet på företagen, vilket också är denna studies slutsats. Resultatet visar även att val av gamficationteknik eller marknad som företaget är aktivt i inte har en påverkan på signifikansen av resultaten. Studien konkluderar att en korrelation mellan gamification och ett företags finansiella värde kan existera, även om resultaten från denna studie tyder på motsatsen.

A suitable environment for the growth of plants is the Greenhouse, that needs to be monitored by a continuous collection of data related to temperature, carbon dioxide concentration, humidity, illumination intensity using sensors, preferably in a wireless sensor network (WSN). Demand initiates various challenges for diversified applications of WSN in the field of IoT (Internet of Things). Network design in IoT based WSN faces challenges like limited energy capacity, hardware resources, and unreliable environment. Issues like cost and complexity can be limited by using sensors that are heterogeneous in nature. Since replacing or recharging of nodes in action is not possible, heterogeneity in terms of energy can overcome crucial issues like energy and lifetime. In this paper, an energy efficient routing process is discussed that considers three different sensor node categories namely normal, intermediate and advanced nodes. Also, the basic cluster head (CH) selection threshold value is modified considering important parameters like initial and residual energy with an optimum number of CHs in the network. When compared with routing algorithms like LEACH (Low Energy Adaptive Clustering Hierarchy) and SEP (Stable Election Protocol), the proposed model performs better for metrics like throughput, network stability and network lifetime for various scenarios.

This thesis presents an approach for modeling and generating efficient hardware architectures using constraint programming techniques, targeting field programmable gate arrays (FPGAs). The focus of this thesis is the derivation of optimal or near-optimal schedules for streaming applications from data flow graphs (DFGs). The resulting schedules are then used to facilitate the architecture generation process. Most streaming applications, like digital singal processing (DSP) algorithms, are repetitive in nature: the same computation is performed on different data items. This repetitive nature of streaming applications can be used to expose additional parallelism available across different iterations, by creating multiple instances of the same computation. The replication of the single computation, when applied to high level synthesis (HLS), improves the performance of the design but requires additional area. The amount of additional area required for a replicated graph can be reduced through the use of pipelined functional units and the addition of some extra clock cycles beyond the critical path of the DFG. This thesis discusses the use of a constraint programming (CP)-based scheduler to generate optimal schedules based on designer-provided replication level and critical path relaxation. The scheduler is an integrated part of the design tool, called CHARGER, which analyzes the resulting schedules to allocate memory for storing intermediate data, creates the infrastructure necessary to efficiently execute the application, and finally generates a synthesizable Verilog/VHDL code for the controller. The performance of the architectures derived using the CP-based scheduler is compared with the architectures generated using a force directed scheduling (FDS)-based scheduler for algorithms selected from embedded/multimedia applications. The results show that our CP-based scheduler outperforms the FDS-based scheduler, both in terms of area and efficiency of the generated architectures. The results show average area saving of 39% and average performance improvement of 41%.

The construction industry has increasingly moved in an environmentally conscious direction when energy-efficient and sustainable buildings are in focus. The motivation behind this development is largely about reducing emissions and relieving the environment, both through new construction, but also through making existing buildings more efficient. For that reason, it is relevant to highlight how energy efficiency of existing properties are done, and how it can be optimized from an environmentally conscious perspective. In order to get a clear picture of energy efficiency and its environmental impact, the study will highlight the differences between new production and existing buildings. The parts that will be discussed are economics, sustainability, and the environment. Through qualitative interviews, the largest real estate companies in Skåne will reveal what the work with energy efficiency of existing commercial buildings looks like. Therefore, the purpose of this study is to investigate how the large real estate companies in Skåne work with their existing commercial properties. The method in the study is a qualitative method where people at the property companies had to answer a selection of questions. The questions posed are relevant to modern society and highlight how real estate companies work with environmental factors and economic factors. The result shows that the study of energy efficiency of existing buildings is a crucial aspect of sustainable development. The study deals with the construction and property industry's emissions of greenhouse gases, and it is therefore of the utmost importance to review the existing properties that the property companies own and where there are opportunities for improvement. The improvements are what make existing buildings increase their environmental performance so that they can comply with the environmental requirements that both Sweden and the EU provide. In conclusion, an increased awareness of the benefits of energy efficiency should produce results, and this could be done throughout training where the individuals who work with properties get a greater insight into energy saving methods.

In recent times data-driven approaches to parametrically optimize and explore building geometry has been proven to be a powerful tool that can replace computationally expensive and time-consuming simulations for energy prediction in the early design process. In this research, we explore the use of surrogate models, i.e. efficient statistical approximations of expensive physics-based building simulation models, to lower the computational burden of large-scale building geometry analysis. We try different approaches and techniques to train a machine learning model using multiple datasets to analyze the impact of geometry and envelope features on the energy efficiency of buildings. These contributions are presented in the form of two conference papers and one journal paper (being prepared for submission) that iteratively build up the underlying methodology. The first conference paper contains preliminary experiments using 4 manually generated building geometries for office buildings. Data were generated by simulating various building samples in EnergyPlus for different geometries. We used the generated data to train a machine learning model using support vector regression. We trained two separate models for predicting heating and cooling loads. The lesson learned from this first experiment was that the prediction of the models was not great due to insufficient geometric features explaining the variability in geometry and the lack of sufficient data for varied geometries. The second conference paper developed a novel dataset of 38,000 building energy models for varied geometry using 2D images of real-world residences. We developed a workflow in the Grasshopper/Rhino environment which can convert 2D images of a floor plan into a vector format then into a building energy model ready to be simulated in EnergyPlus. The workflow can also extract up to 20 geometric features from the model, to be used as features in the machine learning process. We used these features and the simulation results to train a neural network-based surrogate model. A sensitivity analysis was performed to understand the impact and importance of each feature to the energy use of the building. From the results of the experiment, we found that off-the-shelf neural network-based surrogates provided with engineered features can very well emulate the desired simulation outputs. We also repeated the experiment for 6 different climatic zones across Canada to understand the impact of geometric features across various climates; these findings are presented in an appendix. iv In the journal paper, we explored two different methodologies to train surrogate models: monolithic and component-based. We explored the component-based modeling technique as it allows the model to be more versatile if we need to add more components to it, ultimately increasing the usability of the model. We conducted further experiments by adding complexity to the geometry surrogate model. We introduced 10 envelope features as an input to the surrogate along with the 20 geometric features. We trained 6 different surrogate models using different datasets by varying geometric and envelope features. From the results of the experiment, we found that the monolithic model performs the best but the component-based surrogate also falls into an acceptable range of accuracy. From the overall results across the three papers, we see that simple neural network-based surrogate models perform really well to emulate simulation outcomes over a wide variety of geometries and envelope features / Graduate

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Recent advances in computer vision tasks are mainly due to the success of large deep neural networks. The current state-of-the-art models have high computational costs during inference and suffer from a high memory footprint. Therefore, deploying these large networks on edge devices remains a serious concern. Furthermore, training these over-parameterized networks is computationally expensive and requires a longer training time. Thus, there is a demand to develop techniques that can efficiently reduce training costs and also be able to deploy neural networks on mobile and embedded devices. This dissertation presents practices like designing a lightweight network architecture and increasing network resource utilization. These solutions improve the efficiency of large networks during training and inference. We first propose an efficient micro-architecture (slim modules) to construct a light-weight Slim-CNN to predicting face attributes. Slim modules uses depthwise separable convolutions with pointwise convolutions, making them computationally efficient for embedded applications. Next, we investigate the problem of obtaining a compact pruned model from an untrained original network in a single-stage process. We introduce our RAPID framework that distills knowledge to a pruned student model from a teacher model under online settings. Next, we analyze the phenomena of inactive channels in a trained neural network. We take a deep dive into the gradient updates of these channels and discover that these channels have no weight update after a few early epochs. Thus, we present our channel regeneration technique that reinitializes batch normalization gamma values of all inactive channels. The gradient updates of these channels improve after the regeneration step, resulting in an increase in the contribution of these channels to the network performance. Finally, we introduce a method to improve computational efficiency in pre-trained vision transformers by reducing redundancy in visual data. Our method selects image windows or regions with high objectness measures, as these regions may contain an object of any class. Across all works in this dissertation, we extensively evaluate our proposed methods and demonstrate that our techniques improve the computational efficiency of deep neural networks during training and inference.

The low-energy characteristics of Wireless Sensor Networks (WSNs) pose a great design challenge for MAC protocol design. The cluster-based scheme is a promising solution. Recent studies have proposed different cluster-based MAC protocols. We propose an intra-cluster communication bit-map-assisted (BMA) MAC protocol. BMA is intended for event-driven applications. The scheduling of BMA can change dynamically according to the unpredictable variations of sensor networks. In terms of energy efficiency, BMA reduces energy consumption due to idle listening and collisions. In this study, we develop two different analytic energy models for BMA, conventional TDMA and energy efficient TDMA (E-TDMA) when used as intra-cluster MAC schemes. Simulation experiments are constructed to validate the analytic models. Both analytic and simulation results show that in terms of energy efficiency, BMA performance heavily depends on the sensor node traffic offer load, the number of sensor nodes within a cluster, the data packet size and, in some cases, the number of sessions per round. BMA is superior for the cases of low and medium traffic loads, relatively few sensor nodes per cluster, and relatively large data packet sizes. In addition, BMA outperforms the TDMA-based MAC schemes in terms of average packet latency.

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