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Web applications, mobile applications and progressive web applications are the three main types that can be used in smartphones today. These have multiple pros and cons and the decision about which type to choose must be made before the start of the development. When developing a mobile application, the developer should take into account at least two operating systems: Android and iOS. This can be achieved by either building two native applications or by using a framework or development kit that allows the creation of the two version with only one source code. The purpose of this thesis is to build an application that answers the management needs of a nail salon in Switzerland such as appointment management and storage for client in-formation. As the app should be available in iOS and Android, it will be built using Flutter which is a development kit created by Google that uses Dart as its main language to build the application. The back-end part will be created using Firebase which can contain the database and numerous functionalities that will be discussed later during this thesis.
Non-uniform, event-driven sampling of signals can be advantageous for different applications. In this paper, we focus on event-based sampling strategy for electricity metering purposes. Specifically, we propose an improvement in the enhanced event-driven metering (EDM) technique introduced by Simonov et al. Our solution provides additional flexibility on the types of measurements to be sent, by including the option to reduce the sending of consecutive measurements. Numerical results are presented for 4 different open databases of electricity consumption and consistently show that, in relation to the other options, our proposed strategy leads to both: (i) reduction in the amount of measurements sent, and (ii) improvements on the signal reconstruction by decreasing its reconstruction error. These two aspects are extremely useful in a scenario of massive deployment of measurement devices.
The development of tools that accurately describe relationships between woodpeckers and their environment is important for natural resource management, since woodpecker cavities can be used by a diversity of animals. We can use molecular tools to identify patterns of within-cavity diversity, and remote sensing tools can assist in characterizing the environments associated with woodpeckers and the cavities they create. Our objectives were to: 1) use molecular methods to describe some aspects of the diversity found within cavities, 2) describe lidar-based tools that can be used to measure small structures such as cavities, and 3) explore how lidar technologies can be used to explore woodpecker-associated diversity from the cavity to the landscape. In Washington and Idaho (USA), we used molecular approaches, bird surveys, and a variety of remote sensing tools to examine relationships between woodpeckers and diversity in ponderosa pine (Pinus ponderosa) and aspen (Populus tremuloides) forests. We characterized the fungal communities in recently excavated cavities (n=31) of four different woodpecker species and compared those fungal communities with those from unexcavated trees (n=31). Fungal community composition differed between cavities and unexcavated trees, and finer-scale differences were seen between cavities of different excavators. At the cavity scale, we used a lidar (i.e. laser-based) smartphone device and app called the Spike by ikeGPS to measure known cavity entrance dimensions as small as 3cm by 3.5cm. Such information is important because the size of the cavity entrance influences cavity accessibility for different users. Additionally, cavity entrance sizes might influence microclimate characteristics, which could be particularly important for groups such as fungi. The Spike device was highly accurate in measuring cavity entrance dimensions within 30m of a target cavity and up to 15m above the ground (r=0.91). Using airborne lidar remote sensing, we modeled and mapped both woodpecker species richness and total bird species richness across an aspen-conifer gradient using the program RandomForest. We then examined the spatial congruence between woodpecker richness and overall bird species richness, and we found a high correlation (r=0.71) between woodpecker species richness and total avian species richness. Woodpeckers can be considered surrogates for diversity, and the integration of multiple tools and approaches to further understand the relationships between woodpeckers and their environment will improve conservation in the context of changing forest management practices and changing climates.
In this paper, we introduce a new fading model which is capable of characterizing both the shadowing of the dominant component and composite shadowing which may exist in wireless channels. More precisely, this new model assumes a κ-μ envelope where the dominant component is fluctuated by a Nakagami-m random variable (RV) which is preceded (or succeeded) by a secondary round of shadowing brought about by an inverse Nakagami-m RV. We conveniently refer to this as the double shadowed κ-μ fading model. In this context, novel closed-form and analytical expressions are developed for a range of channel related statistics, such as the probability density function, cumulative distribution function, and moments. All of the derived expressions have been validated through Monte-Carlo simulations and reduction to a number of well-known special cases. It is worth highlighting that the proposed fading model offers remarkable flexibility as it includes the κ-μ, η-μ, Rician shadowed, double shadowed Rician, κ-μ shadowed, κ-μ/inverse gamma and η-μ/inverse gamma distributions as special cases.
In this paper, we extensively investigate the way in which κ - μ fading channels can be impacted by shadowing. Following from this, a family of shadowed κ - μ fading models are introduced and classified according to whether the underlying κ - μ fading undergoes single or double shadowing. In total, we discuss three types of single shadowed κ - μ model (denoted Type I to Type III) and three types of double shadowed κ - μ model (denoted Type I to Type III). The taxonomy of the single shadowed Type I - III models is dependent upon whether the fading model assumes that the dominant component, the scattered waves, or both experience shadowing. Although the physical definition of the examined models make no predetermination of the statistics of the shadowing process, for illustrative purposes, two example cases are provided for each type of single shadowed model by assuming that the shadowing is influenced by either a Nakagami- m random variable (RV) or an inverse Nakagami- m RV. It is worth noting that these RVs have been shown to provide an adequate characterization of shadowing in numerous communication scenarios of practical interest. The categorization of the double shadowed Type I - III models is dependent upon whether a) the envelope experiences shadowing of the dominant component, which is preceded (or succeeded) by a secondary round of (multiplicative) shadowing, or b) the dominant and scattered contributions are fluctuated by two independent shadowing processes, or c) the scattered waves of the envelope are subject to shadowing, which is also preceded (or succeeded) by a secondary round of multiplicative shadowing. Similar to the single shadowed models, we provide two example cases for each type of double shadowed model by assuming that the shadowing phenomena are shaped by a Nakagami- m RV, an inverse Nakagami- m RV or their mixture. It is worth highlighting that the double shadowed κ - μ models offer remarkable flexibility as they include the κ - μ , η - μ , and the various types of single shadowed κ - μ distribution as special cases. This property renders them particularly useful for the effective characterization and modeling of the diverse composite fading conditions encountered in communication scenarios in many emerging wireless applications.
Abstract Remote and rural area connectivity is a true challenge that can be alleviated by allowing shared spectrum access in bands below 1 GHz. Spectrum sensing can provide benefits when used together with the database approach for realizing spectrum sharing. Energy detection (ED) is very suitable in cooperative sensing because of its low computational complexity and it does not need prior knowledge about the signal and noise. In this work, cooperative sensing using the window-based (WIBA) ED method is studied to maximize signal detection distance in a rural area scenario with a dedicated channel model. Based on the required individual user detection probabilities, cooperative signal detection distances in kilometers are explored using both OR and k-out-of-n -rules. The results are compared to that of the localization algorithm based on double-thresholding (LAD) method. Computer simulations using a rural area channel model show that the detection distance difference is tens of kilometers. Furthermore, it was found that the signal detection distance improvement can be even five-fold when using the cooperative sensing approach. Thus the proper use and design of cooperative sensing can help in rural area connectivity.
Abstract Connectivity in low-density rural and remote areas where distances are long is a big challenge because of high deployment costs and challenging radio channels with long delay profiles. Spectrum sharing can make spectrum available for 5G local network deployments to serve rural and remote areas. Spectrum sensing can be used to complement the traditional database approach in order to enable efficient and dynamic use of the radio spectrum. In rural and remote areas, long range coverage is required in order to enable flexible and cost-effective solutions. This calls for efficient and low-complex sensing methods who are able to operate in those challenging environments. In this paper we study spectrum sensing method called the window-based (WIBA) energy detector in a challenging rural area channel model for 5G networks. The results are compared to that of the localization algorithm based on double-thresholding (LAD) energy detector. Simulations using a rural area channel model with long delay profile indicated that the WIBA method is able to operate in a rural area channel, and it clearly outperforms the LAD method in terms of detection distance. The detection difference was even 15-fold for the WIBA method, depending on the transmit power and the signal bandwidth.
Abstract Non-uniform, event-driven sampling of signals can be advantageous for different applications. In this paper, we focus on event-based sampling strategy for electricity metering purposes. Specifically, we propose an improvement in the enhanced event-driven metering (EDM) technique introduced by Simonov et al. Our solution provides additional flexibility on the types of measurements to be sent, by including the option to reduce the sending of consecutive measurements. Numerical results are presented for 4 different open databases of electricity consumption and consistently show that, in relation to the other options, our proposed strategy leads to both: (i) reduction in the amount of measurements sent, and (ii) improvements on the signal reconstruction by decreasing its reconstruction error. These two aspects are extremely useful in a scenario of massive deployment of measurement devices.
Abstract This paper focuses on the detection of utilization patterns in electricity residential consumption, which are closely related to the occupant characteristics (e.g. number, age, occupancy, and social class). Our goal is to identify groups of appliances that are often used together via their statistically relatedness. This relation might be obvious (as in TV and Home Theater), or not. The results can be used, for example, to guide a recommendations letter from the energy supplier to the final user, suggesting specific change of habits in order to improve the residence’s energy efficiency. We propose here a methodology for identifying patterns from a large sets of system status, which is a computationally hard task defined in R n with n being the number of appliances involved. The approach consist in the following steps: (i) the Principal Component Analysis method is employed to reduce the set dimensionality to R 3 with explained variance from 68% to 90% to guarantee minimum information loses, (ii) the k-means method to clustering appliances into different groups and (iii) the elbow method was used to define the best number of clusters for each house with explained variance of at least 93% and reaching more than 99% for the best k. Numerical tests using the UK-DALE dataset are presented to show the effectiveness of the proposed solution. The main contribution of this work is a method with low computational cost that requires no other information than a large set of reliable system status (binary vectors) to reveal utilization patterns in the residence.
The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.
Abstract A considerable amount of very fine particles can be found, e.g., stored in tailing ponds, and they can include valuable or hazardous minerals that have the potential to be recovered. Selective flocculation, i.e., the formation of larger aggregates from specific minerals, offers a promising approach to improve the recovery of ultrafine particles. This study focuses on the use of a new bio-based flocculation agent made of silylated cellulose nanofibers containing a thiol-functional moiety (SiCNF). Flocculation was performed in separated systems of ultrafine mineral dispersions of pyrite, chalcopyrite, and quartz in aqueous alkaline medium. The flocculation performance of SiCNF was addressed in terms of the turbidity reduction of mineral dispersions and the floc size, and the results were compared with the performance of a commercial anionic polyacrylamide. SiCNF exhibited a turbidity removal efficiency of approximately 90%–99% at a concentration of 4000–8000 ppm with chalcopyrite and pyrite, whereas the turbidity removal of quartz suspension was significantly lower (a maximum of approximately 30%). The sulfide particles formed flocs with a size of several hundreds of micrometers. The quartz in turn did not form any visible flocs, and the dispersion still had a milky appearance after dosing 12,000 ppm of the flocculant. These results open a promising path for the investigation of SiCNF as a selective flocculation agent for sulfide minerals.
In most fisheries, larger fish experience substantially higher mortality than smaller fish. Body length, life-history and behavioral traits often correlate, such that fisheries-induced changes in size or life-history can also alter behavioural traits. However, empirical evidence regarding how size-selective harvesting alters the evolution of behavioural traits in exploited stocks is scarce. We used experimental lines of zebrafish (Danio rerio) that were exposed to positive, negative or random size-selective harvest over five generations. Our aim was to investigate whether simulated fishing changed the mean personality of the surviving females five generations after initial harvesting halted. We found that mean boldness, activity, and sociability were significantly altered relative to a randomly harvested control line. Harvestinduced changes in individual-level personality were only detected in the negatively sizeselected line. By contrast, we did not detect harvest-induced evolution of personality in the positively size-selected line. We conclude that size-selective harvesting alters individual fish personality in a social fish.