Many industrial automation systems rely on time-synchronized (and timely) communication among sensing, computing, and actuating devices. Advances in Ethernet enabled by time-sensitive networking (TSN) standards, being developed the IEEE 802.1 TSN Task Group, are significantly improving time synchronization as well worst case latencies. Next-generation expected to leverage advances distributed coordinated computing wireless communications enable greater levels of automation, efficiency,...

Recently vision transformer models have become prominent for a multitude of tasks. These models, however, are usually opaque with weak feature interpretability, making their predictions inaccessible to the users. While there has been surge interest in development post-hoc solutions that explain model decisions, these methods can not be broadly applied different architectures, as rules interpretability change accordingly based on heterogeneity data and structures. Moreover, is no method...

In today's era of Internet Things (IoT), efficient and real-time processing massive data generated by IoT device has become the primary issue for traditional cloud computing network architectures. As a supplement computing, edge enhances performance service completion offloading services to servers closer terminal execution, while reducing power consumption load in cloud. this article, we propose following solutions resolve different requests device: an “edge-cloud” heterogeneous...

Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces novel transformer-based explainable model for breast lesion segmentation (TEBLS), aimed at enhancing accuracy and interpretability medical imaging. TEBLS integrates multi-scale information fusion approach hierarchical vision transformer, capturing both local global features by leveraging self-attention mechanism. addresses limitations...

While programs contain a large number of paths, very small fraction these paths are typically exercised during program execution. Thus, optimization algorithms should be designed to trade off the performance less frequently executed in favor more paths. However, traditional formulations code optimizations incapable performing such trade-off. The authors present path profile guided partial redundancy elimination algorithm that uses speculation enable removal along at expense introducing...

With the rapidly development of mobile cloud computing (MCC), Internet Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve problem that UEs may face with insufficient capacity when dealing computationally intensive delay sensitive applications, we take Mobile Edge Computing (MEC) IoT as starting point study computation offloading strategy UEs. First, model application generated by a directed acyclic graph (DAG)...

The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation computational power devices (MDs) by offloading computationally intensive tasks to resource-rich nodes. This paper discusses decision-making process for task and resource allocation among multiple connected a base station. primary objective is minimize time taken complete while simultaneously reducing energy consumption on device under time-varying wireless fading channel. formulated as an...

Abstract Modern processors employ data prefetchers to alleviate the impact of long memory access latency. However, current are designed for specific patterns, which perform poorly on mixed applications with multiple patterns. To address these issues, RL-CoPref, a reinforcement learning (RL)-based coordinated prefetching controller prefetchers, is proposed in this paper. RL-CoPref takes diverse program context information as input, learns maximize cumulative rewards, and evaluates prefetch...

In order to accurately obtain potential features and improve the recommendation performance of collaborative filtering algorithm, this paper puts forward a algorithm based on deep neural network fusion (CF-DNNF). CF-DNNF makes best implicit attributes data, where text other are extracted from data through long short-term memory (LSTM) network, respectively, so as feature matrix that contains user item attribute information. Deep belief (DBN) uses outputs probability. Besides, initially...

We present a path profile guided partial dead code elimination algorithm that uses predication to enable sinking for the removal of deadness along frequently executed paths at expense adding additional instructions infrequently paths. Our approach optimization is particularly suitable VLIW architectures since it directs efforts optimizer towards aggressively enabling generation fast schedules by reducing their critical lengths. The paper presents cost-benefit data flow analysis profiling...

As the very large-scale integrated circuit designs enter deep sub-micron era, many-core processors are regarded as promising architectures to keep up with Moore's law. To provide effective communications between on-chip components, network-on-chip was proposed a new paradigm that exhibits better scalability than traditional buses. There have been previous researches on application mappings reduce power consumption, network latency and area overhead. However, some of algorithms such...

Orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems suffer from the large out-of-band emission (OOBE) that may interfere with other users. Since most existing OFDM OOBE suppression schemes are derived on base of an original system without any scheme, we first propose a generalized framework is capable describing these no matter whether one or more applied. Then, according to place where implemented in our framework, they classified into three groups, namely...

The IEEE 802.11 specification for Wireless Local Area Networks (WLANs) supports 20 MHz channels utilizing bandwidths up to 160 MHz. However, the support of devices simultaneously transmitting over different, non-overlapping channels, is not specifically addressed. Recently, there has been interest in developing a Carrier Grade Wi-Fi which led requirement solutions with efficient frequency resource utilization that are currently supported. In this paper we propose Medium Access Control (MAC)...

Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it nontrivial to maintain service performance under the erratic activities end-users. In this paper, we focus on placement problem continuous provisioning scenario in mobile for multiple users. We propose novel dynamic framework based deep...

In this paper, we propose a permission-based malware detection framework for Android platform. The proposed uses PCA(Principal Component Analysis) algorithm features selection after permissions extracted, and applies SVM(support vector machine) methods to classify the collected data as benign or malicious in process of detection. simulation experimental results suggest that is effective detecting unknown malware, compared with traditional antivirus software, it can detect effectively...

Abstract Multiple CPUs and GPUs are integrated on the same chip to share memory, access requests between cores interfering with each other. Memory from GPU seriously interfere CPU memory performance. Requests multiple intertwined when accessing its performance is greatly affected. The difference in latency increases average of accesses. In order solve problems encountered shared heterogeneous multi-core systems, we propose a step-by-step scheduling strategy, which improve system strategy...