Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of orthogonal space-time coded MCPM system in correlated channels

The paper investigates the average pair-wise error probability (PWEP) performance of orthogonal space-time coded MCPM (OST-MCPM) systems for two transmit antennas over independent and correlated channels. Simple upper boundary expression at high SNR is derived to evaluate the PWEP in correlated channel. The achievable diversity gain of this system decreases due to the signal correlation between the antennas. Simulation results show that the error performance over a correlated channel is degraded when the correlation coefficient increases. And the penalty on the code performance increases a lot in fully correlated channel. It can also be seen that the diversity gain decreases when the channel is fully correlated, which matches well with the theoretical analysis. The upper bounds can be looser when diversity order decreases. The effects of antenna spacing and azimuth power distribution parameters (angle of arrival/departure and angular spread) on the performance of OST-MCPM are also simulated.
An Exploration into Facilitating Higher Levels of Learning in a Text-Based Internet Learning Environment Using Diverse Instructional Strategies

The purpose of this action-research study was to explore how well various instructional strategies translate to a text-based Internet learning environment and facilitate higher levels of learning. The participants were 19 adult learners enrolled in an online degree program at a Western Canadian research university. The results of the study provide support for the position that text-based Internet communication technologies can facilitate effective learning environments through the use of certain instructional strategies, resulting in the ability to facilitate higher levels of learning. The outcomes may be useful to those involved in the design and/or instruction of online learning in postsecondary settings.
Fidelitous Augmentation of Human Accelerometric Data for Deep Learning

Time series (TS) data have consistently been in short supply but needed to train systems in prediction, modeling, classification etc. Synthesis can serve to expand the sample population, yet it is crucial to maintain the statistical characteristics between the synthesized and the original TS to ensure consistent sampling of data. In such synthesis the time domain characteristics of the data may not be preserved which motivates for our work, the objective which is to retain for a synthesized TS: fundamental statistical attributes as well as important time domain features such as its overall shape and significant transients. In a novel way, we isolate important TS features into various components leaving the residual signal to be randomized. Using accelerometer data in a clinical setting, we compare our method to others and show it has higher fidelity to the original signal features, has good diversity and performs better data classification.
Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005

In January 2005, an open evaluation of corpus-based text-to-speech synthesis systems using common speech datasets, named Blizzard Challenge 2005, was conducted. Nitech group participated in this challenge, entering an HMM-based speech synthesis system called Nitech-HTS 2005. This paper describes the technical details, building processes, and performance of our system. We first give an overview of the basic HMM-based speech synthesis system, and then describe new features integrated into Nitech-HTS 2005 such as STRAIGHT-based vocoding, HSMM-based acoustic modeling, and a speech parameter generation algorithm considering GV. Constructed Nitech-HTS 2005 voices can generate speech waveforms at 0.3 ×RT (real-time ratio) on a 1.6 GHz Pentium 4 machine, and footprints of these voices are less than 2 Mbytes. Subjective listening tests showed that the naturalness and intelligibility of the Nitech-HTS 2005 voices were much better than expected.
Breaking boundaries: harnessing unrelated image data for robust risky event classification with scarce state of polarization data

We present an innovative transfer learning method for classifying risky events with scarce state of polarization (SOP) data, utilizing a deep convolutional neural network pre-trained on unrelated images. Achieving a 96.3% accuracy on just 400 samples, this approach offers a robust solution for data-limited scenarios. ©2023 The Author(s)
Aquabis(4-methylbenzoato)-κO;κ2 O,O′-bis(pyridine-κN)nickel(II)

In the title mononuclear complex, [Ni(C8H7O2)2(C5H5N)2(H2O)], the NiII atom is in a distorted octahedral arrangement, coordinated by three carboxylate O atoms from one bidentate 4-methylbenzoate ligand and one monodentate 4-methylbenzoate ligand, two N atoms from pyridine ligands, axially positioned, and a water molecule. The equatorially positioned water molecule and uncoordinated carboxylate O atom form an intramolecular hydrogen bond. An intermolecular O—H⋯O hydrogen bond between the coordinated water molecule and carboxylate O atom of the 4-methylbenzoate ligand forms infinite chains along the b axis. These chains are connected by C—H⋯π interactions.
Around the World

© 1987 Wisconsin Fast Plants, University of Wisconsin-Madison, College of Agricultural and Life Sciences Department of Plant Pathology, 1630 Linden Drive, Madison, WI 53706 1-800-462-7417 wfp@fastplants.cals. wisc.edu Wisconsin Fast Plants, rapid-cycling brassicas, are part of a large family of plants called crucifers (Latin = Cruciferae). Crucifers are easily distinguished by the characteristic shape of their flowers—four petals in the shape of a cross or crucifix. A section, or genus, of the crucifer family is the genus Brassica.
Advanced Acceleration and Implementation of Convolutional Neural Networks on FPGAs

In recent years, embedded platforms became attractive targets to deploy machine learning (ML) -empowered control and processing systems. However, securing a high performance executability to satisfy the hard real-time constraints of such systems on resource-limited platforms, such as FPGAs, is a challenging task. This paper introduces a methodology for deploying hardware-accelerated, adaptable convolutional neural networks (CNN) on embedded FPGA platforms. It enables automated synthesis of hardware IPs, instantiation of CNN architectures and mapping of the CNN layers processing to hardware IPs. This will result in iteration-free deployment reducing the expensive cost of HW synthesis, so that the acceleration hardware IPs are synthesized once and configured at runtime following the CNN architecture. To demonstrate the applicability and assess the performance of our deployment model, we implemented and deployed a segment-based acceleration of a image classification CNN on Xilinx ZYBO Z7 FPGA board. Among others, we have analyzed the computation performance, accuracy and trade-offs between CNN size, image segmentation size, resources utilization and scalability.
Signal integrity and dispersion control for ultra-high speed multi-gigabit - 10-40 Gb/s - PCB designs

As the bandwidth required on a given link path across a printed circuit board (PCB) increases so does the need to increase the link speed and frequency. However, there are some fundamental issues which limit the speeds across PCB’s that must be considered. Some of these issues include attenuation, parasitic impedance mismatch, driver and receiver process and design limitations, and dispersion. Thus, before one embarks on a design of such a link it is necessary to analyze and consider these issues in detail to guarantee the efficacy of the communication path. The purpose of this paper is to examine the aforementioned issues briefly and then to discuss an option for addressing the last issue of dispersion which can be the dominate issue at these very high signal speeds.
User involvement in software evolution practice: A case study

User involvement in software engineering has been researched over the last three decades. However, existing studies concentrate mainly on early phases of user-centered design projects, while little is known about how professionals work with post-deployment end-user feedback. In this paper we report on an empirical case study that explores the current practice of user involvement during software evolution. We found that user feedback contains important information for developers, helps to improve software quality and to identify missing features. In order to assess its relevance and potential impact, developers need to analyze the gathered feedback, which is mostly accomplished manually and consequently requires high effort. Overall, our results show the need for tool support to consolidate, structure, analyze, and track user feedback, particularly when feedback volume is high. Our findings call for a hypothesis-driven analysis of user feedback to establish the foundations for future user feedback tools.
Preparation of Biofilm Electrode with Xanthomonas sp. and Carbon Nanotubes and the Application to Rapid Biochemical Oxygen Demand Analysis in High‐Salt Condition

A Xanthomonas sp. was isolated from the sludge on the drain outlet of a pharmaceutical factory. Then, the bacterium and carbon nanotubes (CNTs) were co‐attached to an oxygen electrode for rapid analysis of biochemical oxygen demand (BOD). The response current was linear with BOD values in the range 10 to 300 mg/L for standard BOD solution with a response time of 35 seconds (R = 0.9994) and 20 to 580 mg/L for pharmaceutical wastewater with a response time ≤200 seconds (R = 0.9985), which means that this modified electrode might be used for online BOD analysis of pharmaceutical wastewater. Further studies revealed that the modified electrode can be used for BOD measurement in a high‐salt condition. Also, the bacterium/CNTs biofilm can maintain its activity and good performance, even after being sealed and stored at 4°C for 50 days.
Assessing 5G Connectivity in Medical Extended Reality (MXR) Applications: A Testbed Approach

The fifth generation (5G) cellular network is an enabler of connectivity that promises to facilitate technical innovations in many industry verticals. In the healthcare sector, the rapidly evolving medical extended reality (MXR) applications can benefit from 5G connectivity to deliver virtual content while achieving seamless mobility. In this paper, we address the challenges of testing 5G connectivity in MXR applications by showcasing an example MXR application setup (i.e., a virtual clinical room) and incorporating it into a 5G network testbed. This paper provides a comprehensive evaluation of the connectivity requirements and the potential impact of 5G network configurations on MXR by collecting communication traffic between different network entities. Experimental results offer insight into the traffic behavior of the MXR application and its supporting connectivity infrastructure.
Modeling and analyzing performance of software for wireless sensor networks

The development of software for Wireless Sensor Networks (WSN) is mostly based on code-and-fix techniques. Up today model-driven engineering techniques have only been limitedly considered in this domain, although they would enable a set of activities aimed at improving the quality of software. In this paper we investigate the possibility to adapt an existing model-based approach that exploits such techniques to combine the modeling and performance analysis of software for WSN. We introduce a UML-based framework where a system model (i) is extended with a new profile for representing NesC application along with the supporting hardware platform, and (ii) is annotated with performance parameters defined in the standard UML MARTE profile. Thereafter we apply a set of transformations to this enhanced UML model that targets a Queueing Network performance model. Finally we illustrate our approach at work on a case study in the agricultural domain.
Re-Examining the Rationale for Strategic Assessment: An Evaluation of Purpose in Two Systems

Strategic forms of impact assessment have seen increased application around the world since their conception. Expansion has produced considerable variation and this range of tools and processes can create practitioner confusion and blurred boundaries in practice. This research draws on empirical data from England and Scotland to examine different systems to understand how the purposes of strategic assessment are framed and to consider how purposes are translated into practice. Four key purposes of strategic assessment are examined; overcoming EIA shortcomings, strategic thinking, representation of the environment and consideration of sustainability. It is concluded that various scales (international, national, local and individual) influence how strategic assessment purpose is framed. We find that as multiple purposes come together they interact, with regulatory compliance potentially dominating. Strategic assessment is also found to be described as information provider, and excluded or distant from strategic thinking as part of plan formulation.
On a Hybrid BiLSTM-GCNN-Based Approach for Attack Detection in SDN

Software-Defined Networking (SDN) is a promising technology for the future Internet. However, the SDN paradigm opens the door to new attack vectors that do not exist in traditional networks, such as flow table overflow attacks and flow rule injection attacks, which traditional intrusion detection systems are no longer sufficient to identify. To address this problem, we propose a new method that uses deep learning for attack detection in an SDN environment. In this method, we first utilize fisher score to remove insignificant features, then design a network model combining bi-directional long short-term memory network (BiLSTM) and gated convolutional neural network (GCNN) to capture the spatio-temporal features of network traffic, and finally use a fully connected layer to perform seven classifications of data. We choose focal loss as the loss function due to the imbalance of samples. The proposed model is evaluated based on the InSDN dataset, which is the latest IDS dataset developed specifically for SDN environments, and the CIC-IDS2017 dataset. The results show that the proposed model improves the performance for anomaly detection and achieves an accuracy of 99.80% and 98.85% on the InSDN and CIC-IDS2017 datasets, respectively. This level of detection accuracy provides great confidence in protecting SDN networks from anomalous traffic.
Building Prioritized Pairwise Interaction Test Suites with Ant Colony Optimization

Interaction testing offers a stable cost-benefit ratio in identifying faults. But in many testing scenarios, the entire test suite cannot be fully executed due to limited time or cost. In these situations, it is essential to take the importance of interactions into account and prioritize these tests. To tackle this issue, the biased covering array is proposed and the Weighted Density Algorithm (WDA) is developed. To find a better solution, in this paper we adopt ant colony optimization (ACO) to build this prioritized pairwise interaction test suite (PITS). In our research, we propose four concrete test generation algorithms based on Ant System, Ant System with Elitist, Ant Colony System and Max-Min Ant System respectively. We also implement these algorithms and apply them to two typical inputs and report experimental results. The results show the effectiveness of these algorithms.
Deep Neural Network Based Daily Volume Prediction in Finance System

The goal of system capacity management is to understand the current capacity usage and forecast future capacity impact. One important task in capacity management is to predict business volume and alters concerned technology teams on potential capacity breach as early as possible. However, business volume in finance company is decided by numerous internal and external factors. Thus, identifying and selecting a good model to predict the business volume is important and challenging problem. In our paper, we first define the problem we want to solve. We discuss 2 regression approaches as baseline. Then we propose Deep Neural Network(DNN) based approach, which is more efficient to predict the volume for finance system. In addition, we propose several features which can further improve prediction accuracy. We collected 12 months real production volume data as our dataset. The experiment shows our proposed model and features can give better performance compared with baseline approaches. We also discussed how the predicted volume can be widely used. Unlike other research papers, our proposal focuses on problems in real finance industry.
Zooming in on quantum trajectories

We propose to use the effect of measurements instead of their number to study the time evolution of quantum systems under monitoring. This time redefinition acts like a microscope which blows up the inner details of seemingly instantaneous transitions like quantum jumps. In the simple example of a continuously monitored qubit coupled to a heat bath, we show that this procedure provides well defined and simple evolution equations in an otherwise singular strong monitoring limit. We show that there exists anomalous observable localized on sharp transitions which can only be resolved with our new effective time. We apply our simplified description to study the competition between information extraction and dissipation in the evolution of the linear entropy. Finally, we show that the evolution of the new time as a function of the real time is closely related to a stable Lévy process of index 1/2.
Trajectory Generation for Unmanned Aerial Manipulators Through Quadratic Programming

In this paper, a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e., for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows us to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.
In silico Characterization of Natural Blending Agents in Conjunction with Polyhydroxybutyrate (PHB) to Solicit Biopolymer film with Improved Thermal Stability

Polyhydroxybutyrate (PHB) is known to exhibit limitations in terms of thermal stability. Its susceptibility to thermal degradation, particularly when exposed to high temperatures, can hinder its performance in various applications. This study addresses the thermal stability limitations of Polyhydroxybutyrate (PHB) by exploring strategies to enhance its performance through blending with biodegradable polymers. Focusing on the PHB-pectin blend, computational simulations unveil a remarkable 346.92 K increase in the glass transition temperature (Tg), surpassing pure PHB. This elevation indicates strong intermolecular interactions, potentially involving hydrogen bonding, leading to a more rigid and thermally stable polymer system. The heightened Tg has practical implications for critical applications, such as automotive components and electronic devices, where resistance to elevated temperatures is essential. Emphasizing environmental benefits, the study highlights the use of biodegradable and bio-based polymers as eco-friendly alternatives to traditional plastics, contributing to reduced plastic waste. Overall, this research underscores the potential of thermally stable polymers to both enhance material performance and address environmental concerns across diverse industries.
Real-time 3D human tracking for mobile robots with multisensors

Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness.