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Multi-level clustering offers the scalability that is essential to large-scale ad hoc and sensor networks in addition to supporting energy-efficient strategies for gathering data. The optimality of a multi-level network largely depends on two design variables: 1) The number of levels, and 2) The number of nodes operating at each level. We characterize these variables within a multi-hop, multi-level...
The Phantom cell concept is a solution for high-traffic outdoor environments that can also support good mobility and connectivity in ultra-dense network. In a phantom cells network, a centralized Macrocell controls all phantom cells within its coverage area and the C-Plane and U-Plane are split. Many issues need to be addressed for this architecture including radio resources allocation and cross/co...
In this paper we present a k-means clustering algorithm for the Versat architecture, a small and low power Coarse Grained Reconfigurable Array (CGRA). This algorithm targets ultra low energy devices where using a GPU or FPGA accelerator is out of the question. The Versat architecture has been enhanced with pointer support, the possibility of using the address generators for general purposes, and cumulative...
Clustering is a crucial tool for analyzing data in virtually every scientific and engineering discipline. The U.S. National Academy of Sciences (NAS) has recently announced "the seven giants of statistical data analysis" in which data clustering plays a central role [1]. This research also emphasizes that more scalable solutions are required to enable time and space clustering for the future...
Library based design and IP reuse have been previously proposed to speed up the synthesis for large-scale FPGA designs. However, previous library based design flow faces several unresolved challenges. Firstly, they may result in large waste area between the modules due to the difference in module sizes. While utilizing multiple ratio modules can help to reduce the waste area, pre-synthesis each module...
Peer-to-peer (P2P) design offers many benefits over a single-master or a multi-master architecture in terms of scalability, reliability and independence. The Clondike project is being converted from a grid computing system into a universal non-dedicated P2P cluster where every participating node can benefit from its membership in the cluster. But different design requires different types of algorithms...
The exponential growth of complex, heterogeneous, dynamic, and unbounded data, generated by a variety of fields including health, genomics, physics, climatology, and social networks pose significant challenges in data processing and desired speed-performance. Existing processor-based software-only algorithms are incapable of analyzing and processing this enormous amount of data, efficiently and effectively...
Considerable efforts have been expended on the centralised administration of WSNs. However, only very small functions, such as processing, memory, battery unit, and communication ability, can be configured using WSN nodes due to the associated resource restrictions. Moreover, unbalanced cluster construction and unbalanced energy dissipation can reduce network lifespan by a large extent. Therefore,...
Current transients caused by energetic particle strikes are a serious threat for digital circuits in aerospace applications. Such single-event transients (SETs) can corrupt the circuit state, with possibly devastating consequences. Although it is possible to protect circuits with spatial redundancy techniques, the area and power overhead is high. Therefore aerospace circuits would benefit from adopting...
We propose methods to accelerate machine learning (ML) on sparse datasets with a distributed memory vector architecture. First, we propose a new communication method that reduces the amount of communication by exploiting the sparsity of the data. Second, we propose a new sparse matrix vector multiplication (SpMV) for a vector architecture, which often becomes the kernel operation of ML on sparse datasets...
Heatmap visualization is a well-known type of visualization to alleviate the overplot problem of point visualization. As such, it is well suited to visualize Big Data. In order to tackle the velocity problem of Big Data, one has to leverage streaming computations. Recently, canopy clustering was shown to be well suited for Big Data heatmap visualization. In this article, we present how to design a...
SDN controller platforms have supported clustering architecture to meet high scalability and availability requirements for large scale carrier grade networks. As a well-known open source project, OpenDaylight provides a clustering and distributed datastore architecture. Datastore is distributed into shards such that a subset of shard can be located in any cluster member. To guarantee strong consistency...
Cooperation of software and hardware with hybrid architectures, such as Xilinx Zynq SoC combining ARM CPU and FPGA fabric, is a high-performance and low-power platform for accelerating RSA Algorithm. This paper adopts the none-subtraction Montgomery algorithm and the Chinese Remainder Theorem (CRT) to implement high-speed RSA processors, and deploys a 48-node cluster infrastructure based on Zynq SoC...
Driven by developments such as mobile computing, cloud computing infrastructure, DevOps and elastic computing, the microservice architectural style has emerged as a new alternative to the monolithic style for designing large software systems. Monolithic legacy applications in industry undergo a migration to microservice-oriented architectures. A key challenge in this context is the extraction of microservices...
The multi-tenancy aware discovery of configurable Cloud services is one of the most important and difficult issues, because of multiplicity and non-standardization of their description in the Cloud. In this paper, relying on a feature model based specification of configurable WSDL services, we develop a multi-tenancy aware approach for their discovery. Our approach empowers multiple tenants to discover...
Organizations increasingly utilize cloud services such as Infrastructure as a Service (IaaS) where virtualized IT infrastructure are offered on demand by cloud providers. A major challenge for cloud providers is the security of virtual resources provided to its customers. In particular, a key concern is whether, for example, virtual machines (VMs) in the datacenter are performing tasks that are not...
Many approaches for tracking objects in lidar data have been proposed in recent years. However, most practical real time systems assume that clean segmentation of lidar points into individual objects can be achieved. Unfortunately, efficient lidar segmentation approaches are prone to under-segmentation when objects are very close to each other; one solution is to introduce additional segmentation...
The extracellular measurement of brain electrical activity contains local field potentials and mixtures of action potentials generated by the neurons. It is essential to determine which individual neuron produces the recorded unit activity, so spike sorting methods are used. High channel-count neural probes are capable of recording the activity of large neural ensembles from up to more than hundred...
In order to save patients with cerebral tumor disease, analysis and time processing of MRI brain images must be efficient, fast and relevant. The implementation of BCFCM algorithm on parallel graphics cards (GPUs) is an adequate remedy for the problem of processing time which can be elevated in urgent pathological cases. In this paper we present two implementations of Bias Correction Fuzzy C-means...
Orthogonal frequency division multiple access (OFDMA) is considered as a promising technique to overcome interference in multi-cell cellular networks. However, the mobility of heterogeneous user equipment brings new challenge to traditional OFDMA network. The current research about OFDMA system mostly based on multi-cell cellular system that is not perfectly compatible with the future networks, for...
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