Vehicular Edge Computing (VEC) emerges as a rem-edy to achieve flexible and fine-grained air pollution monitoring, where vehicles equipped with onboard sensors can sense, process, calibrate and store air pollutants on the drive, and roadside units (RSUs) can be deployed for vehicles to offload data via low-cost vehicle-to-RSU (V2R) communication. However, existing VEC-based air pollution monitoring ...

Thetransitiontonet-zeroemissionsinEuropeisdeterminedbyapatchworkofcountry-levelandEUwide policy, creating coordination challenges in an interconnected system. We use an optimisation model to mapoutnear-optimal energy system designs for 2050, focussing on the planning flexibility of individual regions while maintaining overall system robustness against different weather years, cost assumptions, and ...

Accurate segmentation of subcellular structures from microscopy images is crucial for understanding cellular processes and functions, but it presents significant challenges due to factors such as noise, low signal-to-noise ratios, limited resolution, and complex spatial arrangements. To address these challenges, we introduce CMU-Net, a novel hybrid architecture that combines the strengths of U-Net, ...

Uncontrolled over-fishing has been exemplified by the UN as a serious ecological challenge and a major threat to sustainable food supplies. Emerging trends within governing bodies point towards digital solutions by deploying CCTV-based video monitoring systems on a large scale. We conjecture that such systems are not feasible when reliant on satellite broadband in remote areas, and expose workers ...

In the context of Edge Computing or Cyber-Physical Systems, cross-functional, and cross-geographical DevOps teams are in charge of automating deployments, configuration, and management (i.e., reconfiguration) of complex, large-scale, highly dynamic, and geo-distributed service-oriented software systems. In this context, DevOps teams cannot reasonably manually coordinate their reconfiguration operations ...

Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay. However, there is always a risk of patients being subject to the wrong diagnosis or to a certain treatment not pertaining to the desired effect, potentially leading to adverse events. Thus, there is a need to develop an anomaly detection system for deviations from expected ...

The adoption of decentralized energy market models facilitates the exchange of surplus power among local nodes in peer-to-peer settings. However, decentralized energy transactions within untrusted and non-transparent energy markets in modern Smart Grids expose vulnerabilities and are susceptible to attacks. One such attack is the False Data Injection Attack, where malicious entities intentionally ...

In the rapidly evolving landscape of digital platforms, the need for optimizing media representations to cater to various aspect ratios is palpable. In this paper, we pioneer an approach that utilizes object detection, scene detection, outlier detection, and interpolation for smart cropping. Using soccer as a case study, our primary goal is to capture the frame salience using object (player and ball) ...

Traditional deep learning models are trained and tested on relatively low-resolution images (< 300 px), and cannot be directly operated on large-scale images due to compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an effective learning strategy that allows us to train the existing CNN and transformer architectures (hereby referred to as deep learning models) on large-scale ...

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework ...