Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neural Network Based Model Reference Adaptive Controller

A model reference adaptive controller based on the ability of neural networks to learn arbitrary mappings is described. The controller architecture is motivated by certain stability results for systems controlled by ‘approximate controllers’ [4]. The model reference approach allows the inclusion of a priori knowledge about the system whiie the neural network provides a ”non-parametric” model of the system. The system is applied to the control of a cart/inverted pendulum system.
Scalable multi-objective optimization test problems

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.
Cray T3D: a new dimension for Cray Research

The authors present Cray Research's massively parallel processing (MPP) philosophy, together with a brief description of the design of the Cray T3D, the first MPP designed by Cray Research. They give a brief overview of the important features of the Cray T3D, including the 3-D torus interprocessor interconnect. They discuss in more detail the motivation for the 3-D torus interconnect. Using a very simple capacity model of network performance, they show how three dimensions provide a solid balance between locality and scalability up to thousands of nodes. <<ETX>>
Minimization of location update and paging cost for PCS networks

We propose a mobile tracking scheme consisting of a movement based location update and intelligent paging strategy known as sectored sequential paging (SSP) for PCS networks. The SSP is based on the directional probability of an MT, utilizing the fact that the MT movement pattern is not entirely random, as suggested by the random walk model (RWM), and it tends to follow a direction of movement. An analytical model is developed to calculate the total cost/call and compared with the existing movement based location update and with the shortest-distance-first (SDF) paging strategy. To implement the directional probability of the MT, a simple mathematical model for directional probability is first developed. For better understanding of the mathematical model, a statistical distribution function is considered for the directional probability. Numerical results for total cost per call with the variation of different parameters (delay, call-to-mobility ratio, etc.) show the validity of the two models. The proposed model reduces the total cost per call.
Power performance with gated clocks of a pipelined Cordic core

This paper presents the evaluation in power consumption of gated clocks pipelined circuits with different register configurations in Virtex-based FPGA devices. Power impact of a gated clock circuitry aimed at reducing flip-flops output rate at the bit level is studied. Power performance is also given for pipeline stages based on the implementation of a double edge-triggered flip-flop. Using a pipelined Cordic core circuit as an example, this study did not find evidence in power benefits either when gated clock at the bit-level or double-edge triggered flip-flops are used when synthesized with FPGA logic resources.
Transmission-line modelling (TLM): A brief introduction and recent advances

Transmission-line modelling (TLM) is a general time-domain differential numerical simulation technique suitable for solving field problems. Its main application area has been in electromagnetics but it has also been applied to thermal, and combined electromagnetic and thermal problems. The development of TLM into a general discrete transform for solving circuits and equations has also been described. The author emphasises the development and application of TLM as a general field solver.
An optimal dimensioning of ATM networks

A number of optimization methods for the dimensioning problem of ATM networks have been proposed in the literature. Two important constraints mentioned in the Recommendations for the dimensioning of ATM networks (e.g., E.735, E.736, E.737) but missed in most of the literature are the modularity of resources allocated to a transmission path (TP) and the non-bifurcation of a VP link over more than one TP. Most of the existing dimensioning methods simply allocate resources to established VPCs and link groups without considering the module sizes of the resources and the non-bifurcation of the VP link. We propose mathematical models for dimensioning ATM networks, based on the network synthesis methods and a generalized bin packing problem, where the modularity of resources allocated to a TP, the non-bifurcation of a VP link over more than one TP, and the concept of bandwidth modules are taken into account. Algorithms and numerical results are also presented.
A Biological Simulator using a Stochastic Approach for Synthetic Biology

Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell. Thesis Supervisor: Thomas F. Knight Jr. Title: Senior Research Scientist
CHAMELEON SHARED MEMORY: IMPLEMENTING A THIN ARCHITECTURAL LAYER TO SUPPORT SHARED MEMORY ACROSS MULTIPLE BLADES

Highly integrated SMPs can execute a broad range of workloads, but are expensive and monolithic. It is difficult incrementally to add processing power to a highly integrated SMP, which requires the system to be large enough to handle all possible workloads. This can be unnecessarily expensive and wasteful when executing tasks that do not require such computational power. Clusters are cheap and modular, but cannot execute the same workloads, and are more difficult to manage. Although it is easier incrementally to add more processors to a cluster, communication time between processors is much larger, and running applications with high interprocessor communication is not feasible. Clusters must use message passing instead of shared memory, and managing a large cluster can be difficult, due to the extensive system administration required for many individual systems. We present a system that gives us the best of both worlds: modular and scalable systems that are easy to manage and can execute a broad range of applications.
Network Forensic Readiness: a bottom-up approach for IPv6 networks

A computer network is considered forensically ready, when crucial evidence for a forensic investigation is proactively collected and easily available. While the benefits of a forensically ready network are well understood, the exact information required to be collected to achieve forensic readiness is largely unknown. This thesis focuses on identifying and locating the information that is essential for successful forensic investigations in an IPv6 network. Without the knowledge of what information should be retained, the approach to achieving forensic readiness is likely to be unstructured and crucial information for an investigation might be missed. This study conducted an empirical investigation to identify and extract forensic information from network protocol standards and related literature. Malicious and genuine network scenarios were run and retraced in a test bed to elicit the information that is significant for a forensic investigation. The network scenarios were grouped by network layer and the layers were processed bottom-up to resolve dependencies of the higher layers on the lower layers. A subset of network scenarios was exclusively used to ascertain the effectiveness of the identified information (hold-out approach). This thesis identifies the information in an IPv6 network that is relevant for a successful forensic investigation. Further, the thesis also proposes an optimisation phase as an extension of the National Institute of Standards and Technology (NIST) forensic life-cycle. This phase allows to improve the forensic readiness further through the identification of missing information after conducting a forensic investigation in the network. Finally, design and deployment strategies for implementing a forensically ready network are outlined and recommendations are made for mastering key issues related to forensic readiness.
Nexus - an open global infrastructure for spatial-aware applications

Due to the lack of a generic platform for location- and spatial-aware systems, many basic services have to be reimplemented in each application that uses spatial-awareness. A cooperation among different applications is also difficult to achieve without a common platform. In this paper we present a platform that solves these problems. It provides an infrastructure that is based on computer models of regions of the physical world, which are augmented by virtual objects. We show how virtual objects make the integration of existing information systems and services in spatial-aware systems easier. Furthermore, our platform supports interactions between the computer models and the real world and integrates single models in a global 'Augmented World'.
Qualitative Research: A Personal Skills Approach

Chapter 1 Becoming a Skilled Qualitative Researcher Chapter 2 Observing Chapter 3 Conversing Chapter 4 Participating Chapter 5 Interpreting Chapter 6 Questioning Chapter 7 Data Gathering Chapter 8 Analyzing Chapter 9 Narrating Chapter 10 Writing Chapter 11 The Seven Deadly Sins of Qualitative Research Chapter 12 Once and Future Qualitative Research References Index
Weight Fusion Scheme for single channel based CASA system

Fusion plays an important role to retrieve the single output data from the input data set and in turn improves the quality of the results. Single channel based separation of speech process is to separate the utter speech from the composite speech recorded with a single mic. This research work proposed a weight fusion scheme to combine the voiced matrix and unvoiced speech matrix obtained using feature based single channel speech separation systems. The voiced segment matrix obtained using periodicity features and unvoiced segment matrix obtained using onset/offset feature are combined using the proposed weight fusion principle. The proposed system is evaluated using the standard speech and noise database. The experimental outcome results of the weight fusion system reveal that there is a better improvement than the other speech separation systems
Motor-Imagery BCI Task Classification Using Riemannian Geometry and Averaging with Mean Absolute Deviation

Brain Computer interface (BCI) is thought as a better way to link within brain and computer alternative machine. Many types of physiological signal will work BCI framework. Motor imagery (MI) has incontestable to be a excellent way to work a BCI system. Recent research concerning MI based mostly BCI framework, lower performance accuracy and intense of time have common issues. Main focuses of this paper is select the appropriate central point of tangent space in Tangent Space Linear Discriminant analysis-based Motor-Imagery Brain-Computer interfacing. Method name tangent space mapping LDA (TSMLDA) analysis takes its moves from the observations that normally, the EEG signal embodies outliers, so the centrality as a geometric mean of tangent space might not be the simplest alternative. We tend to propose the employment of strong estimators of variance matrices average. Specifically, Median Absolute Deviation(MAD) going to be planned and mentioned. Associate in Nursing experimental analysis can show the advance of Tangent house Linear Discriminant Analysis corresponding to the planned strong estimators. Experimental results show that our proposed method performs 3% better than the recently developed algorithms.
Theoretical performance analysis on transmission reserve table buffer with triage ranking for resource‐limited control networks

Performance of the ‘transmission reserve table’ (TRT) buffer, which the authors recently proposed, is theoretically investigated. The buffer's triage ranking queuing was designed for resource‐limited control networks that are possibly exposed to a rush of control commands. Our previous study analyzed the performance of the TRT buffer by means of computer simulation. In this paper, we derive a stochastic analytical model for the TRT buffer and verify the model in comparison with computer simulations. Theoretical calculation demonstrates that the triage ranking of the TRT plays a crucial role in keeping the real‐timeness of the control network even in a rush of commands by our triage ranking mechanism. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Co-Design of DNN Model Optimization for Binary ReRAM Array In-Memory Processing

In this work, a novel design of ReRAM neuromorphic system is proposed to process deep neural network (DNN) fully in array efficiently. A binary neural network model is constructed and optimized on MNIST dataset. The obtained model is simulated to be processed with the proposed ReRAM array. Co-design between hardware and network model optimization in software is analyzed to achieve feasible hardware design and generalizable model. Deployed with such co-design model, ReRAM array processes DNN with high robustness against fabrication fluctuation.
One-Plane Glide-Symmetric Structures Over Dielectric Substrate

This work presents a new configuration to create glide-symmetric structures in a single plane, which can be printed on the metallic face of a dielectric substrate. This type of glide symmetry facilitates the fabrication and avoids alignment problems in the assembly process when compared to traditional glide-symmetric structures based on several planes. This article also includes a study based on dispersion diagrams on the appearance of stop-bands by breaking the symmetry. Finally, we present the simulated S parameters of structures with 10x10 unit cells to illustrate the attenuation in these stop-bands.
The Study of Load Identification Based on Raw Current Data

The quality of datasets is very important for load identification. This paper proposes a method for identifying household appliances based on single-cycle raw current data. And the mainstream deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN) are used to evaluate the datasets generated from the method we proposed. Meanwhile, the load identification effects of three deep learning methods are compared on the same dataset. The experimental results show the load identification method based on single-cycle raw current data has good recognition effect on small power appliances, similar power appliances, multi-state appliances and combined appliances. Among them, the best performance RNN model reaches an accuracy of 98.34% for the identification of 58 kinds of single and multiple electrical appliances, which realizes the accurate load identification of electrical appliances commonly used in the family.
A low profile wideband unidirectional antenna for wearable device

In this paper, a wideband wristband antenna for wearable device, characterized by low profile and unidirectional radiation property, is proposed. First, the Theory of Characteristic Mode (TCM) is employed to expound the operation principle of the generation of unidirectional radiation. Then, an appropriate feeding structure is chosen to excite the desired modes. As a result, the corresponding modes are excited and the unidirectional radiation is obtained. Moreover, the desired cellular operation bands (from 1710 MHz to 2155 MHz) are achieved. The total efficiency accompanied with flatness feature are at the levels of -1.5 dB and -6.5 dB in free space and with phantom respectively.
Three-Dimensional Microwave Imaging by Using a Conjugate-Gradient Approach in Lp Banach Spaces

An inverse scattering procedure for addressing three-dimensional microwave imaging problems is presented in this paper. The approach is based on the full-vector integral formulation of the scattering phenomena and exploits the regularization capabilities of a Newton scheme integrating a conjugate-gradient approach developed in Lp Banach spaces. An initial performance assessment of the proposed imaging algorithm, obtained by using numerically simulated data, is reported.
Modeling and Simulation of the Aurora Attack on Microgrid Point of Common Coupling

The smart grid paradigm promises dramatic improvements in reliability, resiliency and efficiency of power systems while addressing environmental concerns. Yet, the extensive reliance of these smart power systems on communication and information technologies increases the cyberattack surface opening up a new class of vulnerabilities to disturb power system operation. In this paper, we study the aurora attack class of assaults specifically against a microgrid point of common coupling (PCC). In contrast to the aurora attack on synchronous generator breakers, it is shown that the microgrid load level, storage and distributed generation characteristics play a key role in determining attack success. Moreover, a mitigation method based on distributed control is proposed and investigated. A co-simulation platform based on OPAL-RT real-time power system simulator and OPNET communication network simulator using the System-in-the-loop feature is employed to validate the analytical results and discussions.
Convolutional LSTM Networks and RGB-D Video for Human Motion Recognition

In this paper, we present a novel deep neural network architecture for addressing the problem of human motion recognition. The proposed architecture enables direct mapping of RGB-D input video to output classification through an end-to-end approach. The RBG images and the depth images pass through a two-stream deep convolutional long short-term memory networks, thereby outputting a plurality of feature maps including complete spatiotemporal features of the entire sequence. Then, a couple of deep convolutional neural networks further extracts the high-level features of these feature maps and classification of action through a classifier. This network structure allows the spatiotemporal structure of the image sequence to be preserved entirely from start to finish and improves the robustness of the network through the separate processing of RGB information and depth information. The experimental results show that the recognition accuracy of this structure in the SBU Kinect Interaction Dataset and MSRDailyActivity3D Dataset is higher than other existing structures.
A fault-tolerant implementation using multiple-task triple modular redundancy

This paper studies an implementation of the multiple-task triple modular redundancy (MTTMR) technique applied to a scenario involving a controlling application in an automobile factory. Active replication, namely triple modular redundancy (TMR), is used to tolerate faults in the controlling application. The manufacturing message specification (MMS) is used as a communication protocol between the controlling application and remote devices in the factory. We show how MTTMR improves the system's performance and continues to guarantee the fault tolerance of the TMR technique.
Bi-directional affine motion compensation using a content-based, non-connected, triangular mesh

A new mesh-generation scheme for motion compensation is presented and discussed. To start with, spatio-temporal segmentation is applied to an image (and a reference frame) in order to identify the boundaries of moving regions, which are then approximated with polygons. A triangular mesh is generated within each polygon, thus ensuring that no triangle straddles multiple regions. The affine motion parameters are then estimated separately for each triangle, using bi-directional motion estimation. Results for various test sequences demonstrate the advantages offered by the use of a contentbased mesh, and by affine motion compensation.
Effect of Slot Structure in Isolation of a Closely-spaced PIFA MIMO Antenna Design for Future 5G Applications

This paper presents a study on a rectangular slot structure at the ground plane of a PIFA MIMO antenna. The two-element PIFA MIMO have been designed on full copper plate material with a thickness of Tc = 0.45mm. The performance of the proposed antenna in terms of the reflection coefficient, isolation, envelope correlation coefficient (ECC) radiation pattern and gain are observed and analysed using CST Microwave Studio Software. The proposed antenna is fabricated and measured to validate the simulation results. A very good agreement of the reflection coefficient is demonstrated between simulated and measured results at 15 GHz. The measured gain 6 dB is slightly lower than the simulated 9 dB. However, the proposed slot antenna shows a better isolation with -32 dB at the resonant frequency. Thus, this offers useful for study and can be applied for future 5G wireless applications.
Phase Noise Influence on Radar Signal Focusing and SAR Tomography

This paper studies the influence of phase noise (PN) on radar signal focusing and SAR tomography. Firstly, we create and add PN to simulated data for 1D focusing. A quasi-linear degradation of resolution is observed for 1 kHz PN level higher than -22 dBc/Hz. Secondly, PN is added to multi channel measured data showing that 1 kHz PN level higher than -60 dBc/Hz affects SAR tomographic reconstruction quality.
High Throughput CNN Accelerator Design Based on FPGA

Due to the fact that FPGA on-chip memory capacity increases significantly, the feature maps and weights of convolutional layers can be stored on chip, which can reduce the data movement between on-chip memory and off-chip memory. Hence, the bottleneck can shift from the bandwidth to the computing resources in convolutional layers, which will improve the performance dramatically. Under this circumstance, this paper quantitatively analyzes how to design the hardware architecture based on the roofline model to optimize the performance under the constraints of available on-chip computing resources and propose an efficient architecture. Our accelerator is implemented on Xilinx UltraScale+ FPGA with the performance of 9.39 TOPS and 6.86 TOPS for 8-bit data width with 100MHz main frequency and 400MHz DSP frequency on ResNet-50 and AlexNet, which outperforms the existing FPGA-based CNN accelerator.