Yes / This article presents a miniaturized triple-band antenna for Internet of Things (IoT) applications. The miniaturization is achieved by using a split square ring resonator and half ring resonator. The antenna is fabricated on an FR4 substrate with dimensions of (33 × 22 × 1.6) mm3. The proposed antenna resonates at the frequencies 2.4 GHz, 3.7 GHz, and 5.8 GHz for WLAN and WiMax applications. The obtained −10 dB bandwidth for the three bands of the proposed antenna are 300 MHz, 360 MHz, and 900 MHz, respectively. The measured reflection coefficient values of the proposed antenna corresponding to each resonant frequency are equal to −14.772 dB, −20.971 dB, and −28.1755 dB, respectively. The measured gain values are 1.43 dBi, 0.89 dBi, and 1 dBi, respectively, at each resonant frequency. There is a good agreement between the measured and simulated results, and both show an omnidirectional radiation pattern at each of the antenna resonant frequencies that is suitable for IoT portable devices.

Some individuals and older adults have trouble remembering when to take their medication. A medication dispenser machine is one suggested solution to this problem. The purpose of this thesis is to design a prototype of a wireless machine for storing and releasing patients’ medication dosages at a certain time. The user can insert the medication schedule through a platform connected to the machine via wireless technology. In order to achieve the purpose, a literature study was conducted to gather and analyze information about wireless communication technologies to determine the most appropriate wireless technology for the thesis end device. The Arduino MKR NB 1500 is the core component of the prototype of the wireless system. Several types of tests were conducted on the system to evaluate its performance, as well as to determine NB-IoT coverage in real-world scenarios. The NB-IoT technology was proven to be the more suitable technology for the wireless pill dispensing machine and for other IoT applications. Moreover, the system performed satisfactorily in solving the defined problem. Despite this, the system could be improved in terms of design, mechanisms, and hygiene system of medicines. / Vissa människor eller äldre vuxna har svårighet att komma ihåg att ta sin medicin och hantera dem själva. En medicindispenser maskin är den föreslagna lösningen för detta problem. Syftet med detta examensarbete är att designa en prototyp av en trådlös maskin för att lagra och frigöra patienternas läkemedelsdoser vid en viss tidpunkt. Användaren kan infoga medicinschemat via en plattform ansluten till maskinen med hjälp av en lämplig trådlös teknik. För att uppnå syftet genomfördes en litteraturstudie för att samla in och analysera information om trådlös kommunikationsteknik för att bestämma den mest lämpliga trådlösa tekniken för arbetets slut enhet. Arduino MKR NB 1500 är kärnkomponenten i prototypen av det trådlösa systemet. Flera typer av tester utfördes på systemet för att utvärdera dess prestanda, samt för att fastställa NB-IoT-täckning i verkliga scenarier. NB-IoT tekniken har visat sig vara den mer lämpliga tekniken för den trådlösa medicindispenser maskinen och för andra IoT-applikationer. Dessutom fungerade systemet tillfredsställande för att lösa det definierade problemet. Trots detta skulle systemet kunna förbättras när det gäller design, mekanismer och hygiensystem för läkemedel.

The velocity dimension of Big Data refers to the need to rapidly process data that arrives continuously as streams of messages or events. Distributed Stream Processing Systems (DSPS) refer to distributed programming and runtime platforms that allow users to define a composition of dataflow logic that are executed on distributed resources over streams of incoming messages. A DSPS uses commodity clusters and Cloud Virtual Machines (VMs) for its execution. In order to meet the required performance for these applications, the DSPS needs to schedule these dataßows efficiently over the resources. Despite their growing use, resource scheduling for DSPSÕs tends to be done in an ad hoc manner, favoring empirical and reactive approaches, rather than a model-driven and analytical approach. Such empirical strategies may arrive at an approximate schedule for the dataflow that needs further tuning to meet the quality of service. We propose a model-based scheduling approach that makes use of performance profiles and benchmarks developed for tasks in the dataßow to plan both the resource allocation and the resource mapping that together form the schedule planning process. We propose the Model Based Allocation (MBA) and the Slot Aware Mapping (SAM) approaches that efectively utilize knowledge of the performance model of logic tasks to provide an efficient and predictable scheduling behavior. We implemented and validate these algorithms using the popular open source Apache Storm DSPS for several micro and application dataflows. The results show that our model-driven approach is able to reduce the amount of required resources (VMs) by 30% − 50% relative to existing techniques. Also we see that our strategies o↵er a predictable behavior that ensures that the expected and actual rates supported and resources used match closely. This can enable deterministic schedule planning even under dynamic conditions. Besides this static scheduling, we also examine the ability to dynamically consolidate tasks onto fewer VMs when the load on the dataßow decreases or the VMs get fragmented. We propose reliable task migration models for Apache Storm dataßows that are able to rapidly move the task assignment in the cluster, and resume the dataflow execution without any message loss.

With the advancement of neural networks and deep learning, the complexity and size of models have increased exponentially. On the other hand, advancements of internet of things (IoT) and sensor technology have opened for many embedded machine learning applications and projects. In many of these applications, the hardware has some constraints in terms of computational and memory resources. The always increasing popularity of these applications, require shrinking and compressing neural networks in order to satisfy the requirements. The frameworks and algorithms governing the compression of a neural network are commonly referred to as pruning algorithms. In this project several pruning frameworks are applied to different neural network architectures to better understand their effect on the performance as well as the size of the model. Through experimental evaluations and analysis, this thesis provides insights into the benefits and trade-offs of pruning algorithms in terms of size and performance, shedding light on their practicality and suitability for embedded machine learning. The findings contribute to the development of more efficient and optimized neural networks for resource constrained hardware, in real-world IoT applications such as wearable technology. / Med framstegen inom neurala nätverk och djupinlärning har modellernas komplexitet och storlek ökat exponentiellt. Samtidigt har framsteg inom Internet of Things (IoT) och sensorteknik öppnat upp för många inbyggda maskininlärningsapplikationer och projekt. I många av dessa applikationer finns det begränsningar i hårdvaran avseende beräknings- och minnesresurser. Den ständigt ökande populariteten hos dessa applikationer kräver att neurala nätverk minskas och komprimeras för att uppfylla kraven. Ramverken och algoritmerna som styr komprimeringen av ett neuralt nätverk kallas vanligtvis för beskärningsalgoritmer. I detta projekt tillämpas flera beskärningsramverk på olika neurala nätverksarkitekturer för att bättre förstå deras effekt på prestanda och modellens storlek. Genom experimentella utvärderingar och analys ger denna avhandling insikter om fördelarna och avvägningarna med beskärningsalgoritmer vad gäller storlek och prestanda, och belyser deras praktiska användbarhet och lämplighet för inbyggd maskininlärning. Resultaten bidrar till utvecklingen av mer effektiva och optimerade neurala nätverk för resursbegränsad hårdvara i verkliga IoT-applikationer, såsom bärbar teknik.