Search

In this work we have explored the use of a Surface Plasmon resonance (SPR) phenomenon for the detection of interleukin-6 (IL-6), a pro-inflammatory cytokine. It plays an important role in the muscle tissues, having direct relation with muscle contraction and, thus, it is considered a biomarker for some types of muscular dystrophies. Here we show that SPR can be used as a real-time monitoring of the shift of the reflectance dip of a gold diffraction grating in front to the antibody adhesion to gold.

Internet-of-Things (IoT) systems have spread among different application domains, from home automation to industrial manufacturing processes. The rushed development by competing vendors to meet the market demand of IoT solutions, the lack of interoperability standards, and the overall lack of a defined set of best practices have resulted in a highly complex, heterogeneous, and frangible ecosystem. Several works have been pushing towards visual programming solutions to abstract the underlying complexity and help humans reason about it. As these solutions begin to meet widespread adoption, their building blocks usually do not consider reliability issues. Node-RED, being one of the most popular tools, also lacks such mechanisms, either built-in or via extensions. In this work we present SHEN (Self-Healing Extensions for Node-RED) which provides 17 nodes that collectively enable the implementation of self-healing strategies within this visual framework. We proceed to demonstrate the feasibility and effectiveness of the approach using real devices and fault injection techniques., Paper accepted for publication as part of the 3rd International Workshop on Software Engineering Research & Practices for the Internet of Things (SERP4IoT 2021), part of 43rd International Conference on Software Engineering (ICSE 2021)

Automated Program Repair (APR) is an area of research focused on the automatic generation of bug-fixing patches. Current APR approaches present some limitations, namely overfitted patches and low maintainability of the generated code. Several works are tackling this problem by attempting to come up with algorithms producing higher quality fixes. In this experience paper, we explore an alternative. We believe that by using existing low-costAPR techniques, fast enough to provide real-time feedback, and encouraging the developer to work together with the APR inside the IDE, will allow them to immediately discard proposed fixes deemed inappropriate or prone to reduce maintainability. Most developers are familiar with real-time syntactic code suggestions, usually provided as code completion mechanisms. What we propose are semantic code suggestions, such as code fixes, which are seldom automatic and rarely real-time. To test our hypothesis, we implemented a Visual Studio Codeextension (named pAPRika), which leverages unit tests as specifications and generates code variations to repair bugs in JavaScript. We conducted a preliminary empirical study with 16 participants in a crossover design. Our results provide evidence that, although incorporating APR in the IDE improves the speed of repairing faulty programs, some developers are too eager to accept patches, disregarding maintenance concerns.

Closed-loop glycemic control algorithms have demonstrated the ability to improve glucose regulation in patients with type 1 diabetes mellitus (T1DM), both in silico and clinical research trials. However, many of the proposed control strategies have been developed under time-invariant linear models, without considering the well-know intraday parametric variability. In this work, a recently published pulsatile Zone Model Predictive Control (pZMPC) is analyzed under a real observed pattern of intraday insulin sensitivity (S I ), according to the latest updates of the FDA-approved UVA/Padova simulator. To this end, nominal information of the postprandial insulin sensitivity is explicitly included in the control-oriented model to be taken into account in the predictions. The proposed control strategy is evaluated in silico under the FDA-approved UVA/Padova simulator, the performance analysis is done using statistical metrics and it is compared with the invariant version of the aforementioned controller. Results show a significant improvement in the analyzed metrics when the time-variant control-oriented model is considered in the predictions, which justifies the proposed increment in the controller complexity.

The continuous spreading of the Internet-of-Things across application domains, aided by the continuous growth on the number of devices and systems that are Internet-connected, created both a rise in the complexity of these systems and made noticeable a lack of human resources with the expertise to design, develop and maintain them. Recent works try to mitigate these issues by creating solutions that abstract the complexity of the systems, such as using visual programming languages. Node-RED, as one of the most common solutions for the visual development IoT systems, stills has several limitations, such as the lack of observability and inadequate debugging mechanisms. In this work, we address some of these limitations by enhancing Node-RED with new features that improve the user's system development, debugging, and understanding tasks. We proceed to empirically evaluate the impact of these enhancements, concluding that, overall, such enhancements reduce the development time and the number of failed attempts to deploy the system.

The Psyche Mission presents the first opportunity to explore the largest metal asteroid in the solar system, (16) Psyche, which is believed to be the exposed core of a larger planetesimal that was stripped of its rocky mantle through multiple collisions during early solar system formation. The mission was selected in January 2017 for a 2022 launch as part of NASA's Discovery Program and is uniquely enabled by the integration of a Solar Electric Propulsion (SEP) Chassis delivered by Maxar Space Solutions with JPL's core deep space avionics, flight software, and fault management architectures. One of the key design tasks is the development of a fault management system capable of being responsive to the unique elements of the combined JPL and Maxar spacecraft architecture. This new design leverages the strengths of each organization, with Maxar delivering its well-proven highvoltage power bus and low-thrust electric propulsion subsystem from its GEO communications satellite product line, and JPL delivering its deep space mission expertise and the hardware and software most critical to deep space mission design. The development of a robust low-thrust mission and the integration of design philosophies and hardware from two organizations is not without its challenges though. A key challenge in the development of the Psyche fault management architecture and design is in the integration of design philosophies and hardware from JPL and Maxar. At the architecture level, Maxar GEO communications satellites are developed under the premise of highly responsive ground in the loop for the resolution of anomalies, and the implementation takes a fail-operational approach to minimize down time for its customers. In contrast, a deep space mission must be able to maintain safety with long periods of ground communication outage. Additionally, with no time-critical events after launch, the Psyche spacecraft will generally failsafe in the presence of anomalous conditions; special consideration is being given to this approach, however, to minimize the loss of electric propulsion thrust time, which is critical to low-thrust missions. At the hardware level, the detailed definition of interfaces between JPL and Maxar hardware presents a unique challenge in the development and flowdown of fault management requirements, the development and implementation of fault monitors and responses, and the development and verification of fault containment boundaries. This paper describes the evolution of the Psyche fault management architecture and design from the concept study into the preliminary design phase, with a focus on the unique challenges associated with flying GEO communications satellite hardware in deep space, implementing a robust low-thrust mission, and the integration of design philosophies and hardware from JPL and Maxar. Details regarding how these challenges are addressed in the fault management design in order to maximize heritage, leverage the strengths of each organization, and minimize risk across the design are also discussed.

Physiological parameters of glucose-insulin models for type 1 diabetes mellitus patients are assumed as time-invariant for glucose regulation control design without considering typical intra-day variations in a diabetic patient. This work analyzes the performance of two control strategies. The first one is a zone model predictive controller, the second is a sliding mode controller. Both controllers were set assuming a nominal plant model identified with standard information, continuous glucose monitoring, exogenous insulin, and carbohydrate counting. The controller's design is evaluated under variations in the most important model parameters, up to 60% in insulin sensitivity, insulin time action and carbohydrate absorption time. Results show that given nominal conditions, the predictive controller has better performance avoiding hyper and hypoglycemic events. However, under parametric variations in the model, the predictive controller is not capable of keeping its performance. Thereby, in the opposite case, the sliding mode controller achieves to maintain results for nominal conditions. This aims to study the future development of a hybrid strategy where advantages of model predictive control and sliding modes control could be taken to improve the system response for meal intake and parameter variations.

Current proposals in glucose control using closedloop systems have focused on maintaining blood glucose in a safe range, this against disturbances as meal intake or exercise. Due of its clinical performance, Model predictive control has been positioned as one of the most used control algorithms in the artificial pancreas. However, the personalization of this control strategy is an unresolved issue and little addressed in the literature. In this work, an auto-tuning methodology for MPC controller on type 1 diabetes patients is presented. The tuning is done through the nelder-mead method to find the controller's parameters that maximizes the time inside the normoglycemia range (70 - 180 mg/dl). It is chosen as variables to tune the weighting coefficient of the output and input, the predictive horizon, and the minimum and maximum values of the target zone. The results obtained show that auto-tuning methodology allow an increase up to 54.43% of time in normoglycemia, with an average increase up to 17.21%.

Real-time monitoring of hematophagous diptera (such as mosquitoes) populations in the field is a crucial challenge to foresee vaccination campaigns and to restrain potential diseases spreading. However, current methods heavily rely on costly DNA extraction which is destructive, costly, time consuming and requires experts. The contributions of this work are: 1) the usage of a new type of imaging, named Wing Interference Patterns (WIPs), which is non-destructive and easier to produce during in the field experiments; 2) a deep learning architecture which is optimized for very low computation cost, memory usage and a short inference time; 3) the use of a dataset of more than 50 medically important species of hematophagous diptera with more than 3000 images of WIPs. With these contributions, we demonstrate that WIPs are an excellent medium to automatically recognize a large amount of hematophagous diptera species with very high accuracy and low computational cost convolutional neural network.

Software has a longstanding association with a state of crisis considering its success rate. The explosion of Internet-connected devices – Internet-of-Things – adds to the complexity of software systems. The particular characteristics of these systems, such as its large-scale and heterogeneity, pose increasingly new challenges. Model-based approaches have been widely used as a mechanism to abstract low-level programming details and processes. By using such approaches, and leveraging concepts as reactive design, visual notations, and live programming, we believe to be able to reduce the complexity of creating, operate/monitor and evolve such systems. The main objective of this Ph.D. is to delve into the software engineering practices for developing IoT systems and systems of systems, exploiting models as a suitable abstraction, expecting to reduce the complexity of managing most of the software development lifecycle that targets IoT systems and to develop the prototype that will aid on the validation of such approach.

Developing learning systems is challenging in many ways: often there is the need to optimise the learning algorithm structure and parameters, and it is necessary to decide which is the best data representation to use, i.e., we usually have to design features and select the most representative and useful ones. In this work we focus on the later and investigate whether or not it is possible to obtain good performances with compressed versions of the original data, possibly reducing the learning time. The process of compressing the data, i.e., reducing its dimensionality, is typically conducted by someone who has domain knowledge and expertise, and engineers features in a trial-and-error endless cycle. Our goal is to achieve such compressed versions automatically; for that, we use an Evolutionary Algorithm to generate the structure of AutoEncoders. Instead of targeting the reconstruction of the images, we focus on the reconstruction of the mean signal of each class, and therefore the goal is to acquire the most representative characteristics of each class. Results on the MNIST dataset show that the proposed approach can not only reduce the original dataset dimensionality, but the performance of the classifiers over the compressed representation is superior to the performance on the original uncompressed images.

The automatic annotation of Mandarin monosyllabic audio word tokens remains an important yet challenging issue in phonetics research. In this work, we address this annotation task via a novel subcategories-classification framework that not only performs word identification via the joint classifications of vowel and tone subcategories, but also performs gender discrimination of the speaker, which stands in contrast to previously proposed methods for Mandarin speech that focused only on tone-, vowel-, or gender- classification. We also propose a novel hierarchical classification algorithm to boost overall classification performance. Extensive experimental results show that our approach yielded superior performance in both cases of adequate and very limited training data. When trained using data from only one female and one male speaker, our approach also yielded the best classification accuracy in all subcategories of the token annotation problem, achieving an Fl-score of 0.742 as opposed to 0.705 as achieved by the second competing approach.

Systems are error-prone. Big systems have lots of errors. The Internet-of-Things poses us one of the biggest and widespread systems, where errors directly impact people's lives. Testing and validating is how one deals with errors; but testing and validating a planetary-scale, heterogeneous, and evergrowing ecosystem has its own challenges and idiosyncrasies. As of today, the solutions available for testing these systems are insufficient and fragmentary. In this paper we provide an overview on test approaches, tools and methodologies for the Internet-of-Things, its software and its devices. Our conclusion is that we are still lagging behind on the best practices and lessons learned from the Software Engineering community in the past decades.

In this paper we study the queuing system that describes the operations of data services in cellular networks, e.g., UMTS, LTE/LTE-A, and most likely the forthcoming 5G standard. The main characteristic of all these systems is that after service access, resources remain allocated to the end user for some time before release, so that if the same user requests access to service again, before a system timeout, the same resources are still available. For the resulting queuing model, we express the blocking probability in closed form, and we also provide recursive expressions in the number of connections that can be handled by the base station. Closed form expressions are also derived for other useful performance metrics, i.e., throughput and network service time. Analytical results are validated against results of a detailed simulation model, and compared to traditional queueing models results, such as the Erlang B formula iteratively applied to the resources that are not blocked by potentially returning users. Our analysis complements the performance evaluation of the other key mechanism used to access data services in cellular networks, namely the random access, which precedes the resource allocation and utilization phase studied in this paper. TRUE pub

In this paper we deal with the problem of making a set of Fog Infrastructure Providers (FIPs) increase their profits when allocating their resources to process the data generated by IoT applications that need to meet specific QoS targets in face of time-varying workloads. We show that if FIPs cooperate among them, by mutually sharing their workloads and resources, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among FIPs. Furthermore, we propose a mathematical optimization model for profit maximization to allocate IoT applications to a set of FIPs, in order to reduce costs and, at the same time, to meet the corresponding QoS targets. Based on this, we propose an algorithm, based on cooperative game theory, that enables each FIP to decide with whom to cooperate in order to increase its profits. The effectiveness of the proposed algorithm is demonstrated through an experimental evaluation considering various workload intensities. The results we obtain from these experiments show the ability of our algorithm to form coalitions of FIPs that are stable and profitable in all the scenarios we consider.

Type 1 diabetes mellitus is a worldwide chronic and autoimmune disease affecting almost 25 million individual around the world. Particularly, in Colombia, it has an annual rate of growth of 3%–5%. Current research in the treatment of this illness is aimed at the implementation of the so-called Artificial Pancreas, a device that allows the regulation of blood glucose levels automatically. Recently, several model predictive control strategies has been developed to accomplish that. In this work, a performance comparison between a standard model predictive control (MPC) and a novel impulsive zone MPC scheme is made based on a novel long-term glucose insulin model. The proposed control strategies ensure the stability under moderate-to-severe disturbances. A long-term scenario — including meals — are simulated and tested in 20 virtual patients. The results appear to be satisfactory as long as every hyperglycemia and hypoglycemia episodes are suitably controlled. The performance comparison also shows that the standard MPC is 70% of the time in the target zone, while the iZMPC is 92.5%.

Advertisement (ad) delivery is expected to play a crucial role in future vehicular networks. In this article, we examine how ads can be broadcasted by roadside units (RSUs) as well as vehicles and then displayed to interested users. We describe the ad dissemination process by means of an optimization model that aims to maximize the number of ads that users display within the target area and validity period of the ad. We then solve the optimization problem, obtaining the optimal scheduling strategy that RSUs and vehicles should adopt for ad broadcasting. Our study highlights the important role that vehicle-to-vehicle (V2V) communications will have in ad delivery. Also, it shows how coexisting vehicular and cellular networks can effectively complement each other, with vehicular networks being a very efficient means for pervasive ad dissemination.

An experience common to smartphone users is the difficulty in accessing services in crowded scenarios, such as a rock concert or a football match. In these cases, to (partially) mitigate frustration, users generically claim that network congestion is occurring, and try again and again to access the network with their smartphones: the result is that user frustration and network congestion reinforce each other! This paper investigates the root causes of poor performance of cellular networks in crowded environments and shows that the commonly adopted random access procedure can prevent full utilization of wireless resources. We develop a simple yet accurate analytical model to analyze why attempting random access to wireless resources can become a problem even when access congestion avoidance is enforced, e.g., with the Access Class Barring technique. The model we propose suggests that cluster-based network access, leveraging device-to-device communications, significantly alleviates access problems. Moreover, it sheds light on scalability laws that govern network utilization and quality of experience, in terms of cell capacity, number of access channels, and cluster size.