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Software Defined Networking (SDN) promises greater flexibility for directing packet flows, and Network Function Virtualization promises to enable dynamic management of software-based network functions. However, the current divide between an…
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms due to several key advantages in latency, privacy and always-on availability. However, due to limited computing resources, efficient DNN…
Software-defined networking (SDN) is the concept of decoupling the control and data planes to create a flexible and agile network, assisted by a central controller. However, the performance of SDN highly depends on the limitations in the…
In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is Extreme Learning Machine (ELM). First, a method is…
Software Defined Networking (SDN) has become a new paradigm in computer networking, introducing a decoupled architecture that separates the network into the data plane and the control plane. The control plane acts as the centralized brain,…
This paper deals with cellular (e.g. LTE) networks that selectively offload the mobile data traffic onto WiFi (IEEE 802.11) networks to improve network performance. We propose the Seamless Internetwork Flow Mobility (SIFM) architecture that…
Micro-Actions (MAs) are an important form of non-verbal communication in social interactions, with potential applications in human emotional analysis. However, existing methods in Micro-Action Recognition often overlook the inherent subtle…
With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile…
Software-Defined Data Centers (SDDC) extend virtualization, software-defined networking and systems, and middleboxes to provide a better quality of service (QoS). While many network flow routing algorithms exist, most of them fail to adapt…
Event cameras unlock new frontiers that were previously unthinkable with standard frame-based cameras. One notable example is low-latency motion estimation (optical flow), which is critical for many real-time applications. In such…
Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation has mainly focused on better utilizing training data,…
For the last thirty years, a large variety of memory allocators have been proposed. Since performance, memory usage and energy consumption of each memory allocator differs, software engineers often face difficult choices in selecting the…
Data copy is a widely-used memory operation in many programs and operating system services. In conventional computers, data copy is often carried out by two separate read and write transactions that pass data back and forth between the DRAM…
Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and…
Datacenters (DCs) are deployed in a large scale to support the ever increasing demand for data processing to support various applications. The energy consumption of DCs becomes a critical issue. Powering DCs with renewable energy can…
Semantic segmentation in remote sensing (RS) has advanced significantly with the incorporation of multi-modal data, particularly the integration of RGB imagery and the Digital Surface Model (DSM), which provides complementary contextual and…
This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset. It enables simulated characters to adopt new motion skills efficiently and…
Named Data Networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management are critical to NDN performance,…
In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for…
Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the…