Energy costs are now one of the leading criteria when procuring new computing hardware. Until recently, developers and users focused only on pure performance in terms of time-to-solution. Recent advances in energy-aware runtime systems render the opt...
Resource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware...
We present an open-source code, Xyst, intended for the simulation of complex-geometry 3D compressible flows. The software implementation facilitates the effective use of the largest distributed-memory machines, combining data-, and task-parallelism o...
Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud c...
In parallel CNC machining systems, machine deterioration can simultaneously increase energy-related operating costs, affect delivery performance, and change the timing of preventive maintenance. This study develops a reliability- and energy-aware dec...
As CPUs alone are often insufficiently efficient for running Vision Transformers (ViTs), contemporary computing platforms, particularly edge systems, increasingly adopt heterogeneous architectures that combine CPUs with GPUs and Neural Processing Uni...
Cloud–edge continuums are driving the shift of cloud-native applications from centralized data centers to latency-, mobility-, privacy-, and energy-saving applications. The serverless architecture provides an attractive “Function-as-a-Ser...
The rapid growth of large language models (LLMs) has made training energy efficiency a major systems and sustainability challenge. Training requires substantial computation, memory, communication, and electrical energy, yet the literature often treat...
Tiny Machine Learning (TinyML) is expanding beyond compact convolutional and feed-forward models toward Transformer-family architectures adapted to microcontrollers and extreme-edge systems. This systematic review evaluates the deployment readiness o...
In this study, we propose a novel hybrid metaheuristic framework that leverages symmetry awareness to enhance the performance of multi-objective optimization in renewable energy systems. Motivated by the repetitive and structured patterns commonly fo...
The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous dat...
Hybrid wind–wave energy systems (HWESs) require coordinated geometry and control design to balance long-term energy capture and floating-platform response under variable offshore conditions. This study develops a constrained multi-objective fra...
Large language model (LLM) serving has emerged as a major source of energy consumption in modern AI infrastructure. In current deployments, graphics processing units (GPUs) are typically operated at default high-frequency settings to maximize perform...
Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data center...
Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building just-in-time (JIT) compilers, besides its reliable front end represented by a clang compiler and its elaborated middle end containing different optimizations that...
Globally, downstream petroleum operations face increasing pressure to optimize energy use and reduce carbon emissions, with electrically driven pumping systems representing the dominant site-level electricity load. This study investigates why anticip...
Visual–Inertial Odometry (VIO) has become a key technology for motion estimation in robotics and autonomous systems. However, deploying accurate VIO pipelines on embedded platforms remains challenging due to the trade-off between estimation acc...
During the transition of global energy systems toward low-carbon and high-reliability operation, nuclear power plant (NPP) operation and maintenance require environmental perception methods that are safe, energy-efficient, and sufficiently accurate f...
Unmanned aerial vehicles (UAVs) have become essential tools for monitoring crop condition, detecting early signs of plant stress, and supporting timely interventions in modern precision agriculture. However, real-time onboard image analysis remains c...
Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated le...
Flight data-driven modeling has become an important approach for trajectory prediction, anomaly detection, and risk assessment in unmanned aerial vehicles and other aerial systems. Such data are usually high-dimensional, nonlinear, multirate, and non...
Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments. Classical path planning approaches, including graph-based search, sampl...
Offshore wind power generation is recognized as an indispensable component of renewable energy systems. However, growing concerns regarding operational safety have emerged with the increasing deployment of offshore wind turbine foundations. Current m...
The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and f...
Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of fou...
Hydraulic actuation can deliver substantial force or torque within a restricted robotic limb, yet installed joint capability emerges from more than actuator pressure. This critical engineering review connects mechanical output mechanism, hydraulic so...
Future internet services are expected to increasingly depend on IoT–edge deployments, in which intrusion detection must operate close to constrained, heterogeneous devices rather than only in cloud or data-center environments. Although the lite...
GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without...
Efficient deployment of neural-network detectors on field-programmable gate array (FPGA) accelerators depends not only on model complexity but also on compiler-visible graph structure. On deep learning processing unit (DPU) platforms, unsupported ope...
Modern engineering increasingly operates within socio-technical networks, such as the interdependence of energy grids, transport systems, and building codes, where decisions must be reliable and transparent. Large language models (LLMs) such as GPT p...
Large language models (LLMs) are increasingly being explored for integration into sensor-driven control systems across robotics, industrial automation, energy infrastructure, healthcare, smart environments, and other sensor-rich domains. This review...
To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edge-deployment settings, this study proposes a cost-effective Cantonese ASR system...
1 February 2012
Despite the promising performance improvement observed in emerging many-core architectures in high performance processors, high power consumption prohibitively affects their use and marketability in the low-energy sectors, such as embedded processors...
Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles...