Abstract

Context:

Software Testing is a costly activity since the size of the test case set tends to increase as the construction of the software evolves. Test Case Prioritization (TCP) can reduce the effort and cost of software testing. TCP is an activity where a subset of the existing test cases is selected in order to maximize the possibility of finding defects. On the other hand, Markov Chains representing a reactive system, when solved, can present the occupation time of each of their states. The idea is to use such information and associate priority to those test cases that consist of states with the highest probabilities.

Objective:

     The objective of this paper is to conduct a survey to identify and understand key initiatives for using Markov Chains in TCP. Aspects such as approaches, developed techniques, programming languages, analytical and simulation results, and validation tests are investigated.

Methods:

     A Systematic Literature Review (SLR) was conducted considering studies published up to July 2021 from five different databases to answer the three research questions.

Results:

     From SLR, we identified 480 studies addressing Markov Chains in TCP that have been reviewed in order to extract relevant information on a set of research questions.

Conclusion:

     The final 12 studies analyzed use Markov Chains at some stage of test case prioritization in a distinct way, that is, we found that there is no strong relationship between any of the studies, not only on how the technique was used but also in the context of the application. Concerning the fields of application of this subject, 6 forms of approach were found: Controlled Markov Chain, Usage Model, Model-Based Test, Regression Test, Statistical Test, and Random Test. This demonstrates the versatility and robustness of the tool. A large part of the studies developed some prioritization tool, being its validation done in some cases analytically and in others numerically, such as: Measure of the software specification, Optimal Test Transition Probabilities, Adaptive Software Testing, Automatic Prioritization, Ant Colony Optimization, Model Driven approach, and Monte Carlo Random Testing.

Introduction

     It is a fact, nowadays, that the question of software reliability is extremely relevant, given the dependence society has on software systems. Quality of all such produced software systems must be ensured, and hence Verification & Validation (V&V) activities play a major role to be employed to achieve this goal [2]. Moreover, software testing is the most used V&V activity in practice [3], [4]. The goal of any software testing effort is to find defects1 in the software product, in this case very related to defects (faults) in the source code.

     Testing activity ensures the conformity of the software product with the requirements defined by the customer, through a systematic execution. However, in order to entirely achieve this goal, the ideal approach is to exhaustively test the software, which is impractical. In order to cope up with this impracticality, techniques are designed to use only a small subset of the input domain, but with a high probability of revealing the presence of defects, if they exist [3]. Therefore, it is essential to adopt a consolidated methodology that makes the test activity viable and effective. Some approaches have already been proposed [5], such as Test Case Selection (TCS), which selects a subset of the test cases for execution according to a specific objective; Test Suite Reduction (TSR), which reduces the size of the test suite; and Test Case Prioritization (TCP), which prioritizes test cases to be executed. The differential of TCP concerning TSR and TCS is that TCP uses the entire test suite. Every single test case will be considered in prioritization, thus reducing the risk of omitting some type of defect. As the main interest of this study is TCP, the paper will focus on its approaches.

Conclusions

     In this paper, we have reported the results of an SLR on TCP using Markov Chains. From the defined search string, a total of 468 studies obtained from five databases were returned (IEEEXplore, ACM, ScienceDirect, SpringerLink, and Scopus), of which only 12 persisted until the last selection step. For this study, 3 research questions were defined, whose objective is not only to clarify the technologies and methodologies involved in the application of Markov Chains and the prioritization of test cases but also to understand how research related to this field is being distributed and studied by the around the world.

     The main objective of this work was to investigate all the approaches and applications related to this topic, so that, based on the correlations between the studies found, we could trace possible future directions regarding this topic. Our motivation for this was the fact that Test Case Prioritization is a profitable field from the point of view of the entire software development cycle, as its application can be useful in many ways, such as saving time and budget since the goal is to optimize the process of software testing, which often consumes a good deal of effort on the part of the development team.

Abstract

Document Sections

I. Introduction

II. Objective

III. Proposed Idea

IV. Related Works

V. Levels of User Authentication and Securing User Data

VI. Architecture

VII. Methods to Secure the Web Application

VIII. Implementation

IX. Results

X. Novelty

XI. Conclusion

Abstract:

     Website security is a major concern for large organizations as well as individual developers, the rarer the technology used the harder it becomes to come up with secure practices for developing a website. Vulnerabilities that are not fixed during development, and are deployed as such become easy targets for hackers. This could cause the company or the individual to lose a lot of money. It is not just the developers who are affected, end users who end up on vulnerable websites may get exposed to XSS attack which could compromise their system or an unsecured configuration of database system could lead to a potential data leak and hence the password of every registered user on the website is compromised, users who use the same password on multiple websites are affected the most The motivation for this paper comes from the fact that there is an overwhelming number of vulnerabilities in any application under development and every developer, experienced or not needs a starting point to patch these vulnerabilities that might have occurred in their application, this research provides the most common vulnerabilities which should be taken care of in any application and thus provide the much-needed starting point for developers. The objective of this paper is to design and develop a secure web application according to Open Web Application Security Project (OWASP) guidelines.

I. INTRODUCTION

A Web application (Web app) is an application program that is stored on a remote server and delivered over the Internet through a browser interface. A web application generally consists of three main parts –

 Front – End,

 Back – End and

 Database.

For this paper, the front-end has been made using HTML with ejs, for back-end NodeJS has been used and for the database MySQL has been used.The front end is used to display the main content of the webpage, it is usually the only thing that the client sees once the site is visited. If the website is static then only the front end is required.HTML is used to form the layout of the whole page, CSS is used to give styling to the page, and JavaScript is used to give logic to the page. Websites having only the frontend are prone to Cross-Site Scripting (XSS) injection attacks.

RESULTS
A. SQL – NodeJS is a relatively new backend language therefore automated vulnerability scanners don’t work well with most of the NodeJS applications. Manual testing of vulnerabilities is one of the ways to validate the security of the application.

1- Introduction

2- Related work

3- Flow-aware multi-topology adaptive routing

4- Implementation environment

5- Router components

6- Tests of the FAMTAR router

7- Conclusion

References

Abstract

Flow-Aware Multi-Topology Adaptive Routing (FAMTAR) is a new approach to multipath and adaptive routing in IP networks which enables automatic use of alternative paths when the primary one becomes congested. It provides more efficient network resource utilization and higher quality of transmission compared to standard IP routing. However, thus far it has only been evaluated through simulations. In this paper we share our experiences from building a real-time FAMTAR router and present results of its tests in a physical network. The results are in line with those obtained previously through simulations and they open the way to implementation of a production grade FAMTAR router.

Introduction

FAMTAR (Flow-Aware Multi-Topology Adaptive Routing) is a new approach to routing in IP networks, which provides multipath and adaptive capabilities. It is based on flows, notion currently incorporated in many architectures, including Software-Defined Networking (SDN). Unlike many SDN solutions, FAMTAR is a fully distributed mechanism and does not depend on a central entity. This is a key characteristic, as it significantly improves scalability and resilience. Our aim was to test the efficiency of FAMTAR in real network conditions and validate the results obtained earlier through network simulations. In order to do that, we needed to build a FAMTAR router. In this paper we present our experiences in building a FAMTAR router.

1- Motivation and significance

2- Software description

3- Illustrative example

4- Impact

5- Conclusion

References

Abstract

Ant Colony Optimisation (ACO) algorithms emulate the foraging behaviour of ants to solve optimisation problems. They have proven effective in both academic and industrial settings. ACO algorithms share many features among them. Isula encapsulates these commonalities and exposes them for reuse in the form of a Java library. In this paper, we use the travelling salesman problem and image segmentation to showcase the framework capabilities using three top-performing ACO algorithms implemented in Isula. This framework is an open-source project available at GitHub, where is currently the most popular ACO java repository.

Motivation and significance

Ant Colony Optimisation (ACO) algorithms, proposed by Dorigo et al. [1], solve optimisation problems by emulating the behaviour of ants in nature. When ants traverse a territory searching for food, they mark their path with pheromone. After they have located food, they make several trips from the food source to the nest, increasing the intensity of their path’s pheromone trail. In case several ants have located the same food source and are also transporting food to the nest, the pheromone trail of the ant with the shortest path is more intense. The shortest path, in the same amount of time, has more two-way trips than longer paths that require more time for their traversal. Fellow ants are sensitive to pheromone and tend to select the path with the most intense pheromone trail. This behaviour also increases the pheromone intensity of the shortest path. Over time, the whole colony converges towards the optimal solution.

1- Introduction

2- GROLL Overview

3- GROLL Design

4- GROLL Routing Engine

5- Related Work

6- Conclusions

References

Abstract

Routing in low-power lossy networks (LLNs) remains an important research problem for Internet of Things (IoT), despite the recent standardization in RFC 6550 of RPL as an IPv6 Routing Protocol [1]. Recent deployment experiences [2] have emphasized the challenges faced by a complex design in real world deployments. One of the key challenges is versatile routing protocol design that can handle efficiently and seamlessly various combinations of routing primitives, namely unicast, multicast, and convergecast. Although RPL supports these traffic types, due to its design principle oriented around data collection, its unicast and multicast functionalities are limited. In this article, we present the design and system implementation of the first Geographic Routing framework for Low Power and Lossy Networks (GROLL). GROLL’s routing engine effectively integrates routing for unicast, multicast, and convergecast and takes into account Complex Network Topologies (CNT), i.e., network holes and cuts. GROLL detects CNTs and abstracts the boundary information of detected CNTs with significantly reduced memory size. The proposed unified routing framework was implemented on real hardware and it was evaluated extensively on a testbed of 42 TelosB motes.

Introduction

RPL was adopted by IETF as the standard IoT routing protocol for low power and lossy networks [1]. Recent deployment experiences [2] have emphasized the complexities faced by RPL routing in real world, and pointed out some inefficiencies, especially in terms of routing path stretch. RPL is designed to support unicast, multicast and convergecast, but its design makes limiting assumptions: the predominant traffic is from nodes to the base station, followed, on rare occasions by traffic from the base station to the nodes and the traffic among nodes, while supported, is extremely rare. These assumptions may not hold well in some deployments, such as wireless sensor deployment for disaster response. On the other hand, geographic routing is well suited for LLNs because it is simple and highly scalable; and more importantly location information of devices, sensors, and machines are often readily available in IoT applications. Furthermore, different routing primitives such as 1-1 (unicast), 1-n (multicast), and n-1 (convergecast) can be easily supported based on geographic routing. Unfortunately, to the best of our knowledge, there is no unified location-based routing framework that integrates location-based routing protocols developed for different routing primitives. However, the needs for an unified routing framework has ever increased recently. In recent IoT applications [3], nodes do not just report data to the base station. For example, peer-to-peer or multicast data communications have become essential routing traffic patterns for in-network processing, data aggregation, and feedback control [3].

1- Introduction

2- Related work

3- System model

4- The probabilistic IDS

5- Experimental evaluation

6- Discussion

7- Conclusions

References

Abstract

The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than 90% accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer.

Introduction

Two of the main prohibiting factors for the adoption of the Electric Vehicles (EVs) across Europe are the driving range (i.e. the distance the vehicle can cover before it needs to recharge), and the lack of supporting charging infrastructure. One solution to these pivotal factors would be the implementation of stochastic optimisation techniques for the charging procedure of EVs [1]. However, this research area has specific limitations regarding the optimal placement of charging stations in a city, queue stability issues, especially when few charging stations must facilitate a large number of requests, among others. Furthermore, the deployment of charging infrastructure requires changes to the existing civil infrastructure, which are costly and take a long time to implement. To overcome these prohibiting factors, it is important that novel and cost-effective approaches to help in the adoption of EVs are proposed. A novel solution initially proposed in Refs. [2,3] to increase the driving range of EVs is the use of city buses as energy sources on the move. The EVs can make efficient use of Mobile Energy Disseminators (MED), which operate as mobile charging stations, and Static Charging Stations (SCS) [2]. The role of a MED can be taken over by city buses that follow a predefined route across the city. Inner-city busses repeatedly move along a predefined route. An EV can establish a communication with a MED and approach it at a specific location along the predefined route to complete the charging process. The proposed method exploits Inter-Vehicle Communication (IVC) in order to eco-route EVs. This innovative approach drives the investigation towards integrated solutions that allow EVs to charge while moving through the city, without the need for a significant change in the existing road infrastructure.

1- Introduction

2- Image processing pipeline

3- Fast meteor detection

4- Classification via deep learning

5- CNN as a meteor detector

6- Leading edge position refinement

7- MULTI-SITE aggregation of tracks

8- Atmospheric trajectory estimation

References

Abstract

The processing pipeline for both video meteor detection and track analysis has evolved to embrace several new algorithms, which have improved the efficiency and performance of various steps in the meteor image processing chain. With the advent of larger pixel count digital sensors, the image processing techniques have needed to keep up with the computational load by not only employing higher end processors, but developing faster thresholding, clustering, and tracking algorithms for detection. In addition, machine learning methods employing both recurrent and convolutional deep neural networks have helped remove the human-in-loop false alarm mitigation step inherent in many meteor collection processing streams. The application of a matched filtering algorithm has helped to refine the measurement positional accuracy of propagating meteor tracks for post-detection analysis. The use of improved multi-site track aggregation has dramatically reduced the occurrence of mis-associating unrelated tracks during the combination into a single trajectory. When coupled with an improved minimization metric in the multi-parameter fitting method for trajectory estimation, this yields better meteor orbital solutions. Finally, proposed concepts in using a convolutional neural network as a meteor detector and performing trajectory fitting with an empirically based propagation model, show promise for more robust meteor image processing and analysis in the near future.

Introduction

The meteor imaging community has begun migrating towards multimegapixel, progressive-scan digital sensors, and away from the traditional analog cameras with less than half-million pixels per frame. While this has been beneficial from the lowered noise, image quality, higher bit depth, spatial coverage, and angular resolution perspectives, the significantly larger image sizes have stressed processing loads. For example, full high-definition (HD) 1080p imagery possesses what amounts to approximately six times the pixel count of NTSC or PAL video. The computational issue has been partially offset by both Moore’s law as well as employing larger capacity CPUs, but this can be limiting in multicamera systems without resorting to high cost PC systems or custom GPU implementations. In addition, the advent of innovative meteor collection systems for tracking meteors for fragmentation studies and spectroscopy that require real-time responsiveness of these short-lived events, has resulted in a re-examination of the entire image processing chain to increase computational efficiency, while also maintaining detection and analysis robustness. Finally, with the exploding growth of low-cost meteor camera deployments, meteor collection networks have tried to automate as many steps in the processing pipeline as possible, specifically those steps which were traditionally addressed by having a human-in-the-loop (HIL) perform the process. Thus, several algorithms have been undergoing new development and are being incorporated into various meteor imaging systems and their processing pipelines. This paper addresses a broad base of those enhancements and will highlight the significant improvements in algorithms, providing references for those algorithms described in greater detail in the published literature.

Glossary

Spatial Knowledge Representation and Inference

Expert Systems for Domain-Specific Problems

Acquisition of Spatial Knowledge

Intelligent Decision Support System-An Integration

Conclusion

Spatial Knowledge Representation and Inference

Geographical knowledge may be structured or unstructured. We may organize our knowledge in a highly structured form so that problems can be solved by systematic and procedural form. Mathematical models, statistical methods, and heuristic procedures are knowledge in procedural form. This type of knowledge follows a rigid framework for the representation and analysis of structures and processes in space and time. Procedural knowledge is effective in system specification, calibration, analysis, scenario generation, and forecasting of well-specified and structured problems. Through research, we have accumulated, over the years, a wealth of procedural knowledge which can be effectively utilized for geographical analysis in spatial information systems. A majority of knowledge, however, is loosely structured. Subjective experience, valuation, intuition, and loosely structured expertise often cannot be appropriately captured by rigid procedures. They are declarative in nature and can only be represented by more flexible frameworks. Making inferences from such knowledge structures cannot be done procedurally. Problem-solving by if–then arguments is a typical example of using declarative knowledge for decision-making. This type of knowledge is effective in solving unstructured or semistructured problems. It is suitable for inference with concepts, ideas, and values. Similar to the use of procedural knowledge, declarative knowledge can be utilized in spatial decision-making, especially with spatial information systems. Declarative knowledge is, however, ineffective to solve highly structured problems. Consequently, procedural and declarative knowledge have to be used in synchrony throughout a decision-making process. Once a spatial structure or process is understood and can be specified in a formal and structured manner, we can always capture it by a mathematical model or procedure. The representation of loosely structured knowledge is, nevertheless, not as straightforward. Declarative knowledge representation and inference have thus become a main concern in building spatial reasoning systems with artificial intelligence. To be able to understand and to reason, an intelligent machine needs prior knowledge about the problem domain. To understand sentences used to describe geographical phenomena, for example, natural language understanding systems have to be equipped with prior knowledge about topics of those phenomena. To be able to see and interpret scenes, spatial vision systems need to have in store prior information about objects to be seen. This also applies to the deep learning paradigm intensively studied in recent years. Therefore, any intelligent system should possess a knowledge or training database containing facts and concepts related to a problem domain and their relationships. There should also be an inference mechanism which can process symbols in the knowledge base and derive implicit knowledge from explicitly expressed knowledge. Knowledge representation formalism consists of a structure to express domain knowledge, a knowledge representation language, and an inference mechanism. Conventionally, its duty is to select an appropriate symbolic structure to represent knowledge in the most explicit and formal manner, and an appropriate mechanism for reasoning.

1- Introduction

2- Methods to achieve frequency boost up by instantaneous discharging

3- The basic characterization of TENG-based direct wireless transmission

4- The static force sensing based on resonant frequency shift

5- Wireless toy car control

6- Wireless 3D VR drone control

7- Conclusion

8- Experimental section

References

Abstract

Wireless Sensor Networks (WSNs) bring the basis that the development of the Internet of Things (IoTs) relies on. However, external power requirement restricts the widespread applications of WSNs. Triboelectric nanogenerator (TENG) has been a promising candidate for energy harvesting and self-powered sensing, but most of the reported TENG-based WSNs need to store energy in a capacitor first and then power the wireless modules without a continuous sensing capability simultaneously. In this work, a battery-free short-range self-powered wireless sensor network (SS-WSN) is proposed by using TENG-based direct sensory transmission (TDST). By leveraging a mechanical switch or diode-switch combination, enhanced output and boosted frequency of TENG signal have been achieved by instantaneous discharging in a short period, initiating the potential for direct signal transmission without additional wireless modules and external power suppliers. Different from others’ works, the stable resonant frequency is used to realize the force-sensing function with high sensitivity (434.7 Hz.N−1), in which case a wireless real-time electronic scale is demonstrated. Additionally, by varying the connections of the textile TENGs or the capacitance of an external capacitor for distinguishable resonant frequencies generation, the multiple-freedom-degree 2D toy car control, and 3D VR drone control are demonstrated with a single coil.

Introduction

Nowadays, Wireless Sensor Networks (WSNs) have attracted lots of attention from researchers in a wide variety of areas due to their ubiquitous nature and their tremendous development in the Internet of Things (IoT) [1–5]. IoT, which enables the wireless connection and controlling of devices through the internet, has been used in diversified applications for remote monitoring and sensing, such as environmental monitoring [6,7], smart home [8,9], and healthcare [10–14]. With the rapid development of IoTs, wearable electronics, which shape the human lifestyle, are in a great demand for the applications of human-machine interfaces (HMIs) [15–22], control [23,24], communication [25] and wireless networks [1,26–29]. Due to its unique characteristics, such as light-weight, soft, permeable and conformable, the cost-effective textile has become a promising candidate for wearable sensors in IoT networks [30–33]. However, the large power consumption for long-term connectivity, especially with the increasing number of sensors becomes the major bottleneck of WSN technologies [34]. To overcome this issue, different kinds of energy harvesting techniques, which scavenge various forms of energy from the ambient environment, are explored extensively in recent years, including the solar energy [35], kinetic energy [36], thermal energy [37], and the radio frequency (RF) energy [38]. Nevertheless, most of the reported energy harvesting techniques for wireless sensing could only fulfil the function of battery life extension rather than eliminating the usage of power supply, where the scavenged energy is stored into a capacitor or battery for later use.

1- Introduction

2- Related work

3- Methodology and experiments

4- Experimental results and discussions

5- Conclusion

References

Abstract

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.

Introduction

Android operating system, which is provided by Google, is predicted to continue have a dramatic increase in the market with around 1.5 billion Android-based devices to be shipped by 2021 sta. It is currently leading the mobile OS market with over 80% market share compared to iOS, Windows, Blackberry, and Symbian OS. The availability of diverse Android markets such as Google Play, the official store, and third-party markets makes Android devices a popular target to not only legitimate developers, but also malware developers. Over one billion devices have been sold and more than 65 billion downloads have been made from Google Play (Smartphone, 0000). Android apps can be found in different categories, such as educational apps, gaming apps, social media apps, entertainment apps, banking apps, etc. As a technology that is open source and widely adopted, Android is facing many challenges especially with malicious applications. The malware infected apps have the ability to send text mes-sages to premium rate numbers without the user acknowledgment, gain access to private data, or even install code that can download and execute additional malware on the victim’s device. The malware can also be used to create mobile botnets (Anagnostopoulos et al., 2016). Over the last few years, the number of malware samples attacking Android has significantly increased. According to a recent report from McAfee, over 2.5 million new Android malware apps were discovered in 2017, thus increasing the number of mobile malware samples in the wild to almost 25 million in 2017 (McA, 0000).

1- Introduction

2- Related works

3- The proposed method

4- Experimental results

5- Conclusions

References

Abstract

The existing probability based reversible authentication schemes for demosaiced images embed authentication codes into rebuilt components of image pixels. The original demosaiced image can be totally recovered if the marked image is unaltered. Although these schemes offer the goal of pixel-wise tamper detection, the generated authentication codes are irrelevant to the image pixels, causing some undetectable intentional alterations. The proposed method pre-processes the rebuilt components of demosaiced images and hashes them to generate authentication codes. With the guide of a randomly-generated reference table, authentication codes are embedded into the rebuilt components of demosaiced images. Since the distortions of image pixels are sensitive to the embedded authentication codes, the proposed method further alters the pre-processed pixels to generate a set of authentication codes. One of the authentication codes that minimizes the distortion is embedded to generate marked demosaiced images. The results show that the proposed method offers a better image quality than prior state-of-the-art works, and is capable of detecting a variety of tampering.

Introduction

Digital cameras are popular today because they are handy and versatile. In a digital camera, an image sensor is employed to acquire and convey the information for constituting a digital image. Because of the monochromatic nature of image sensors, it can only detect the intensity of light but color differences are indistinguishable. Several solutions are implemented to capture the colors of visual scenes. A cost-reduced solution is to place a mosaic-like color filter array (CFA) in front of the sensor to capture three primitive colors, namely, red, green and blue (RGB). Through the CFA, RGB colors are separated and their intensities are recorded in sensor cells. Therefore, the CFA image is indeed a mosaic-like grayscale image where each pixel location carries the intensity of a color element called the sampled component. The other two missing elements termed rebuilt components have to be determined based on their known neighbors using an interpolation technique called image demosaicing [1–4]. The resultant interpolated image is called the demosaiced image. A number of CFAs with varied patterns of color filters are used in practice, and the well-known Bayer pattern [5] has an advantage over others since the periodic nature of its patterns simplifies the demosaicing process. The image demosaicing system is shown in Fig. 1.

1- Introduction

2- Methodology

3- Online cardiac monitoring

4- Discussion and reference model

5- Open issues, learned lessons and future trends

References

Abstract

The Internet of Health Things promotes personalized and higher standards of care. Its application is diverse and attracts the attention of a substantial section of the scientific community. This approach has also been applied by people looking to enhance quality of life by using this technology. In this paper, we perform a survey that aims to present and analyze the advances of the latest studies based on medical care and assisted environment. We focus on articles for online monitoring, detection, and support of the diagnosis of cardiovascular diseases. Our research covers published manuscripts in scientific journals and recognized conferences since the year 2015. Also, we present a reference model based on the evaluation of the resources used from the selected studies. Finally, our proposal aims to help future enthusiasts to discover and enumerate the required factors for the development of a prototype for online heart monitoring purposes.

Introduction

Recently, cardiovascular diseases (CVD) have become the leading cause of death in the world, accounting for about 17.7 million, or 31%, of all deaths in 2017 [1]. CVDs are associated with disorders of the heart and blood vessels which might often lead to cardiac arrhythmia, stroke, hypertension, and heart failure. CVDs are classified as chronic non-communicable diseases (NCD) which also include cancer, diabetes, and chronic respiratory diseases, for example [2]. NCDs are associated with a slow, long-term, or even lifelong advancement, and are usually silent or symptomatic, thereby affecting the quality of life [3]. Several factors contribute to the development of CVDs, but poor lifestyle management such as inadequate diet, sedentary lifestyle, stress, and legal and illicit drug use are determining factors for the progression of these pathologies [4]. Therefore, proper care can significantly reduce the likelihood of heart problems as well as routine examinations and follow-ups by a cardiologist as treatment expenditures become excessive when cardiovascular risk factors are poorly controlled.

1- INTRODUCTION

2- RELATED WORK

3- METHOD

4- EVALUATION

5- CONCLUSION

References

Abstract

We introduce a perceptually motivated approach to bandwidth expansion for speech. Our method pairs a new 3-way split variant of the FFTNet neural vocoder structure with a perceptual loss function, combining objectives from both the time and frequency domains. Mean opinion score tests show that it outperforms baseline methods from both domains, even for extreme bandwidth expansion.

INTRODUCTION

This paper introduces a deep learning-based method for bandwidth expansion of human speech. The goal of the bandwidth expansion (BWE) problem, also called “bandwidth extension” and “audio super-resolution,” is to expand the frequency range of an input audio signal. Its traditional applications are in telephony, where the bandwidth of telephone speech may be limited to below 4 kHz, thus aiming to render muffled speech more intelligible [1]. In the context of newer audio synthesis tasks, such as textto-speech (TTS) and consumer digital media creation, there arises a need for more extreme BWE, such as to 44.1 kHz or 48 kHz. In WaveNet-like applications, for example, speech is synthesized at a low sampling rate for efficiency reasons [2]. BWE may be applied to synthesized audio to improve the listening experience. In another use case, many consumers record speech on low-bandwidth devices, such as a consumergrade microphone, and would like higher-resolution versions of their recordings for podcasts or other artistic purposes. In these applications, the input bandwidth might not be as low as that of telephone transmission, but rather around 8 kHz. Our objective is to super-resolve speech to high-definition audio – in our experiments, we convert 8 kHz to 44.1 kHz, although these are just parameters of the method. By expanding beyond 16 kHz, we emphasize not intelligibility as in traditional BWE, but high perceptual quality and sense of presence in the recording, since the extreme upper bands offer information beyond just speech content, including the finer details of the speaker’s voice and environment.