Search

Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available.

In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing’s impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.

Modern cities are complex adaptive systems in which there is a lot of dependency and interaction between the various stakeholders, components, and subsystems. The use of digital Information and Communications Technology (ICT) has opened up the vision of smart cities in which the city dwellers can have a better quality of life and the city can be better organized and managed. The deployment of ICT solutions, however, does not automatically or invariably improve the quality of living of the citizens. Analyzing cities as complex systems with various interacting sub-systems can help us understand urban dynamics and the fate of smart cities. We will be able to analyze various policy interventions and ascertain their effectiveness and anticipate potential unintended consequences. In this paper, we discuss how smart cities can be viewed through the lens of systems thinking and complex systems and provide a comprehensive review of related techniques and methods. Along with highlighting the science of cities in light of historic urban modeling and urban dynamics, we focus on shedding light on the smart city complex systems. Finally, we will describe the various challenges of smart cities, discuss the limitations of existing models, and identify promising future directions of work.

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

With the world becoming flat with fluid boundaries, engineers have to be global in their outlook and their pedigree. Due to the need for international acceptance of engineering qualification, the incorporation of Outcome-Based Education (OBE) has become common and global accreditation treaties such as the Washington Accord have been ratified. Further, it becomes important, especially for an engineering university with a global outlook preparing its students for global markets, to ensure that its graduates attain the planned outcomes. Additionally, the higher education institutions need to make sure that all the stakeholders, including students, parents, employers, and community at large, are getting a quality educational service, where quality is categorized as (1) product-based ensuring that the graduate attained the planned outcomes and skills, and (2) process-based keeping an eye on whether the process is simple, integrated, and efficient. The development of quality movements, such as Total Quality Movement (TQM), Six Sigma, etc., along with quality standards such as ISO 9001 has been instrumental in improving the quality and efficiency in the fields of management and services. Critical to the successful deployment of a quality culture is the institutionalization of an integrated Quality Management System (QMS) in which formally documented processes work according to the Vision and Mission of an institute. At the same time, commitment to Continuous Quality Improvement (CQI) to close the loop through effective feedback, would ensure that the planned outcomes are attained to the satisfaction of all the stakeholders, and that the process overall is improving consistently and continuously. The successful adoption of quality culture requires buy-in from all the stakeholders (and in particular, the senior leadership) and a rigorous training program. In this paper, we provide a review of how a QMS may work for the provision of quality higher education in a 21st-century university.

In this paper, we strive to provide a primer for students on how to thrive and learn effectively in engineering education in the volatile, uncertain, complex, and ambiguous (VUCA) times following the onset of the COVID-19 global pandemic, which has disrupted the educational enterprise massively with universities physically closing in many parts of the world and students and faculty transitioning to remote learning. Although the immediate audience assumed in this paper comprises engineering students (such as those enrolled in electrical, electronics, or computer engineering programs) studying in an outcome-based education (OBE) environment—the global educational paradigm mandated by the Washington Accord that aims to standardize engineering competencies in terms of the attained student learning outcomes—the presented ideas are more general and broadly useful for learners of all types. We will describe seven evidence-based steps that the students can adopt to thrive in OBE settings in these challenging times. The main contribution of this paper is practical: we present a synthesis of the vast research literature on effective student learning in normal, online, and disrupted settings to present practical insights that students can leverage to substantially improve their learning. At the end of the paper, we also present a discussion of important issues related to remote teaching and online education such as ensuring equity and the handling of lab work for engineers in such settings (e.g., through simulators and virtual labs).

Owing to the harsh and unpredictable behavior of the sea channel, network protocols that combat the undesirable and challenging properties of the channel are of critical significance. Protocols addressing such challenges exist in literature. However, these protocols consume an excessive amount of energy due to redundant packets transmission or have computational complexity by being dependent on the geographical positions of nodes. To address these challenges, this article designs two protocols for underwater wireless sensor networks (UWSNs). The first protocol, depth and noise-aware routing (DNAR), incorporates the extent of link noise in combination with the depth of a node to decide the next information forwarding candidate. However, it sends data over a single link and is, therefore, vulnerable to the harshness of the channel. Therefore, routing in a cooperative fashion is added to it that makes another scheme called cooperative DNAR (Co-DNAR), which uses source-relay-destination triplets in information advancement. This reduces the probability of information corruption that would otherwise be sent over a single source-destination link. Simulations-backed results reveal the superior performance of the proposed schemes over some competitive schemes in consumed energy, packet advancement to destination, and network stability.

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution.

Despite the best efforts of networking researchers and practitioners, an ideal Internet experience is inaccessible to an overwhelming majority of people the world over, mainly due to the lack of cost-efficient ways of provisioning high-performance, global Internet. In this paper, we argue that instead of an exclusive focus on a utopian goal of universally accessible “ideal networking” (in which we have a high throughput and quality of service as well as low latency and congestion), we should consider providing “approximate networking” through the adoption of context-appropriate trade-offs. In this regard, we propose to leverage the advances in the emerging trend of “approximate computing” that rely on relaxing the bounds of precise/exact computing to provide new opportunities for improving the area, power, and performance efficiency of systems by orders of magnitude by embracing output errors in resilient applications. Furthermore, we propose to extend the dimensions of approximate computing towards various knobs available at network layers. Approximate networking can be used to provision “Global Access to the Internet for All” (GAIA) in a pragmatically tiered fashion, in which different users around the world are provided a different context-appropriate (but still contextually functional) Internet experience.