Miracle-dz/UniFAR-FTUs huggingface dataset:
Slice Allocation and Pricing Framework for Virtualized Millimeter Wave Cellular Networks

Traditionally, the cellular spectrum is allocated to operators (OPs) through auctions, as ideal mechanisms to discover market prices and allocate scarce resources. Even though spectrum is indeed scarce in sub-6 GHz bands, it becomes abundant in millimeter-wave (mmWave) bands. Interestingly, in that context, it is base station (BS) density which is limiting, and thus a critical factor, due to the outage phenomena in urban environments. Facing BS scarcity is one of the main reasons to foster virtualization techniques aimed at improving utilization and lowering costs. We consider a scenario with an infrastructure provider (InP) owner of a number of BSs and a set of OPs with their users (UEs). We propose a three-phase framework to price network infrastructure slices (NISs) and allocate them to OPs and to efficiently associate UEs with those NISs. The framework stages are: 1) an initial association, 2) a distributed auction mechanism across the BSs to allocate resources to Ops, and 3) a re-association process where the OPs can optimize the NISs they are awarded. The auction incentivizes OPs to bid truthfully and the outcome yields both socially optimal NISs and Vickrey-Clarke-Groves (VCG) prices. For the re-association phase, we propose deterministic and stochastic exchange-matching algorithms and demonstrate their convergence to stable matching and stable-optimal matching, respectively.
An analytical approach to LINC power combining efficiency estimation and optimization

A new approach for studying the combiner efficiency in a LINC amplifier based on analytical expressions of the instantaneous combiner efficiency and the signal's probability distribution function (PDF) is proposed. The analytical expressions, derived for both a matched combiner and a Chirex outphasing combiner, are applied to compute the combiner efficiency of a LINC amplifier under CDMA and 64QAM signals. A direct approach to determining the optimal shunt reactance used in an outphasing combiner is also proposed and applied to both signals. Validation with a commercial simulator is given.
Controlled ultrasound tissue erosion

The ability of ultrasound to produce highly controlled tissue erosion through cavitation was investigated. This study is motivated by the need to develop a noninvasive procedure to perforate the neonatal atrial septum in treatment of hypoplastic left heart syndrome. A total of 232 holes were generated in 40 pieces of porcine atrial wall by a 788 kHz single element transducer. The effect of various parameters (i.e. pulse repetition frequency (PRF), pulse duration (PD) and gas content of liquid) on the erosion rate and energy efficiency was explored. PDs of 3, 6 , 12 and 24 cycles, PRFs between 1.34 kHz and 66.7 kHz, gas saturation of 40-55% and 79-85%, and Ispaa's between 3000 W/cm/sup 2/, were applied. Results show that very short pulses delivered at an optimum PRF could be the most effective for controlled ultrasound tissue erosion. Results also show that the acoustic backscatter from the bubble cloud in the erosion zone is highly correlated with the erosion process.
Implementation and Evaluation of a Gigabit Ethernet FSO Link for 'The Last Metre and Last Mile Access Network'

In this paper, we propose a simple and a low cost 1 Gbps Ethernet free-space optical (FSO) communications link, which can be used both for the last meter and last mile access networks. In the emerging fifth generation wireless systems, which require at least an order of magnitude increase in the peak data rates and three orders of magnitude increase in network capacity with reduced latency, deploying multiple technologies will play a crucial role to meet these requirements. One possible complementary wireless technology to the radio frequency is the unlicensed FSO, which can bridge the gap between the existing RF wireless and optical fibre communication networks by providing high data rates, low installation costs and high energy efficiency. In this work, we propose a high-speed FSO system, which can be readily implemented using off the shelf components, and assess its performance experimentally under turbulence and fog conditions using the dedicated indoor atmospheric chamber. We show that, the proposed system under the turbulence condition with a scintillation index of 0.43 offers almost the same data rate (i.e., ∼99%) as the link under a clear channel, while the packeterror-rate reduces from 10<sup>−3</sup> to 2 × 10<sup>−2</sup>.
Minimum Downlink Band Duplex Isolation Requirements for LTE User Equipment

Duplexers based on self-interference cancellation can provide substantial transmit-to-receive (Tx-Rx) isolation, whilst being tunable over wide frequency ranges, presenting a promising alternative to fixed-frequency acoustic duplexers commonly used in today's mobile devices. However, the level of Tx-Rx isolation provided by tunable duplexers is typically lower compared to surface acoustic wave and bulk acoustic wave devices. This paper investigates the impact of reduced isolation on receiver (Rx) noise figure (NF), and quantifies the minimum requirement for Tx-Rx isolation in a long term evolution (LTE) mobile terminal. Tx noise in the Rx band is quantified through measurements taken from a cellular handset power amplifier, for a range of duplex separations and uplink bandwidths, and combined with a simple linear model to calculate the desensitized Rx NF as a function of Tx-Rx isolation. LTE downlink throughput simulations are used to assess the impact of isolation on LTE sensitivity, and establish the minimum isolation required for LTE sensitivity specification compliance. Results show that reduced duplexer isolation leads to substantial desensitization for some duplex separation/bandwidth combinations, however, to achieve the minimum LTE sensitivity requires only 38 dB of isolation in the Rx band; this is achievable using current tunable duplexing technologies.
Effect of Surface Finish on the Fatigue Behavior of Bi-based Solder Joints

The reliability of an electronic assembly is often limited by the fatigue failure of one of the interconnected solder joints due to the mismatch of Coefficient of Thermal Expansion (CTE) between the components and Printed Circuit Board (PCB). Fatigue properties of lead-free solder joints are normally studied on the large bulk samples. However, the microstructure and failure mechanism of actual size solder joints are different than bulk samples. The surface finish also affects the fatigue behavior of solder joints. The objective of this study is to investigate the effect of surface finish on the fatigue properties of the high-Bi lead-free solder joints. Lead-free alloys for harsh applications are commonly micro-alloyed with small percentages of metal elements, such as Bi, to enhance the fatigue life. In this study, individual solder joins were cycled using Instron micromechanical testers in stress-controlled experiments. The tested solder materials were Sn-3Ag-0.5Cu (SAC305), Sn-3.5Ag-0.7Cu-3Bi-l.5Sb-0.125Ni (Innolot), Sn-3.41Ag-0.52Cu-3.3Bi (CycloMax), and Sn-0.92Cu-2.46Bi (Ecolloy). The surface finishes were OSP, ImAg, and ENIG. Characteristic fatigue life and work dissipation per cycle were measured and compared. The results showed that characteristic fatigue life decreased with the increase of stress amplitude. Solder joints with high Ag and Bi contents demonstrated outstanding fatigue properties. OSP and ImAg surface finishes demonstrated more stable fatigue properties, while ENIG surface finish tended to show an embrittlement fracture between the Ni layer and solder joints during high-stress-amplitudes fatigue test.
Bean soup translation: flexible, linguistically-motivated syntax for machine translation

Machine translation (MT) systems attempt to translate texts from one language into another by translating words from a source language and rearranging them into fluent utterances in a target language. When the two languages organize concepts in very different ways, knowledge of their general sentence structure, or syntax, is crucial. The syntax of the target language is particularly useful, because it provides a means of testing whether the reorderings that a system might try are grammatically licensed. This thesis presents two novel syntactic techniques that aid in producing correct and grammatical translations. The first technique controls target language reordering using syntactic categories that span multiple words. The second technique complements the first by assessing the well-formedness of sequences formed by these reorderings using the same syntactic categories. These innovations are implemented in the context of statistical phrase-based machine translation [Zens et al., 2002; Koehn et al., 2003], which is the prevailing modern translation paradigm. The main contribution of this thesis is to use the flexible syntax of Combinatory Categorial Grammar [CCG, Steedman, 2000] as the basis for deriving syntactic constituent labels for target strings in phrase-based systems, providing CCG labels for many target strings that traditional syntactic theories struggle to describe. These CCG labels are used to train novel syntax-based reordering and language models, which efficiently describe translation reordering patterns, as well as assess the grammaticality of target translations. The models are easily incorporated into phrase-based systems with minimal disruption to existing technology and achieve superior automatic metric scores and human evaluation ratings over a strong phrase-based baseline, as well as over syntax-based techniques that do not use CCG.
Measuring robustness of Visual SLAM

Simultaneous localisation and mapping (SLAM) is an essential component of robotic systems. In this work we perform a feasibility study of RGB-D SLAM for the task of indoor robot navigation. Recent visual SLAM methods, e.g. ORBSLAM2 [9], demonstrate really impressive accuracy, but the experiments in the papers are usually conducted on just a few sequences, that makes it difficult to reason about the robustness of the methods. Another problem is that all available RGB-D datasets contain the trajectories with very complex camera motions. In this work we extensively evaluate ORBSLAM2 to better understand the state-of-the-art. First, we conduct experiments on the popular publicly available datasets for RGB-D SLAM across the conventional metrics. We perform statistical analysis of the results and find correlations between the metrics and the attributes of the trajectories. Then, we introduce a new large and diverse HomeRobot dataset where we model the motions of a simple home robot. Our dataset is created using physically-based rendering with realistic lighting and contains the scenes composed by human designers. It includes thousands of sequences, that is two orders of magnitude greater than in previous works. We find that while in many cases the accuracy of SLAM is very good, the robustness is still an issue.
UWB path loss characterization in residential environments

In this paper, we describe a simple method for measurement of the Ultra-Wideband Band (UWB) frequency response for evaluation of the path loss and impulse response of the UWB indoor channel. We propose a simple statistical path loss model for the residential channel that is based on over 300,000 frequency response measurements. The probability distributions of the model parameters for different locations are presented.
Realistic Super-Resolution Image Analysis for State of the Art 2D Contrast Enhanced Ultrasound Imaging

An ultrasound based super-resolution algorithm was developed for clinical 2D ultrasound imaging in contrast enhanced ultrasound (CEUS) mode. Synthetic and in vivo data from sheep ovaries were used. Development involved microbubble-specific detection and segmentation, resulting in optimal localisation, resulting in an image analysis algorithm that could be used with clinical data of a few minutes duration. Microscopy images from histology slices were available to allow comparison of the in vivo vascular network with that produced from the processed ultrasound data. A 5-fold resolution gain was demonstrated in vessel width, as vessels slightly under $120 \mu \mathrm {m}$ were detected $( \lambda \quad = 257 \mu \mathrm {m})$, while the synthetic data demonstrated that the microbubble (MB) localization uncertainty is $30 \mu \mathrm {m}$. These results were achieved by processing video loops of under 2-minute duration. In conclusion, the above algorithm provided complete images of microvascular structure down to arteriole level from data that do not extend beyond a reasonable patient examination period.
Leveraging Memory PUFs and PIM-based encryption to secure edge deep learning systems

There is a sheer growth of attention on the issue of deep learning attacks launched by adversaries. Especially with the spread of edge computing devices that cooperate with the central cloud, how to protect the neural network models and private data from being attacked becomes a hot topic. In this paper, we consider two collaborative edge-cloud deep learning scenarios. The first is that deep learning models are trained on resource-rich cloud and deployed on the terminal deep learning accelerators for delay-sensitive tasks. While in the second scenario, edge collected data is offloaded to the cloud for computationally-intensive tasks. In both scenarios, the valuable pre-trained neural network models and the private data are exposed to the risks of being attacked if they are transmitted and processed in an unencrypted way. To tackle this security problem, we present a lightweight protection scheme towards data-oriented attacks and model-oriented attacks. With the use of on-chip memory Physical Unclonable Functions (PUFs) and Processing-In-Memory (PIM), our method limits the model execution only on specific edge devices and also prevents the unauthorized analysis of private data. Experiments on state-of-the-art deep learning networks show that our method secures edge deep learning models and user-data with a negligible performance overhead.
Outage Performance of NOMA-Based Cognitive Hybrid Satellite-Terrestrial Overlay Networks by Amplify-and-Forward Protocols

In this paper, we investigate the outage probability (OP) performance of amplify-and-forward (AF) cognitive hybrid satellite-terrestrial overlay networks (CHSTONs) with the non-orthogonal multiple access (NOMA) scheme, in which half-duplexing terrestrial secondary networks cooperate with a primary satellite network for dynamic spectrum access. In order to improve the fairness of overlay paradigm, a NOMA-based power allocation profile is determined by instantaneous channel conditions. Considering the generalized shadowed-Rician fading for satellite links and Nakagami- $m$ fading for terrestrial links, we derive the closed-form OP expressions for both the primary and secondary users. Then, the asymptotic OP expressions at the high signal-to-noise ratio (SNR) regime are also obtained to evaluate the achievable diversity order and coding gain. Finally, the numerical simulations are provided to validate the theoretical results as well as the superiority of the NOMA scheme in CHSTONs and proclaim the effect of key parameters on the performance of the NOMA users, such as fading configurations and the power split factor.
Battery-Integrated Multiple-Input Multiple-Output Energy Mixer for Power Packet Distribution System

Power can be delivered in a packet form using a power packet distribution system (PPDS). It is capable of avoiding intermediate voltage conversion stages by sending power packets of different voltage levels on the same power line. A mixer of the PPDS is used to generate PPs of different voltage levels. A four-port converter as a mixer is proposed, which can interface with a renewable energy source, a battery and loads through two power lines simultaneously. The battery storage serves as a buffer to mitigate intermittency of renewable energy sources. PPs can be delivered by configuring the proposed mixer in different modes. The proposed mixer has a bidirectional load port, which can be utilized for power sharing between load ports. The proposed mixer uses a single inductor to achieve a compact design. Experimental results are presented to verify the functionality of the proposed mixer.
Cellular-Connected UAV: Performance Analysis with 3D Antenna Modelling

The ever-increasing applications enabled by unmanned aerial vehicles (UAVs) calls for high-rate and ultra-reliable two-way wireless communication between UAVs and their associated ground entities. Integrating UAVs into cellular networks is regarded as a promising approach to meet such goals. However, given the unique UAV-ground channel characteristics and the fact that the existing cellular network has been mainly designed to serve ground users, it is not immediately clear whether the new aerial users can be supported in a similar manner as the ground users. In this paper, we provide the performance analysis for cellular-connected UAVs based on stochastic geometry, by studying the association probability and signal-to-interference-and-noise ratio (SINR) with two different association policies, namely nearest-distance based and maximum-power based associations. One striking difference of our work as compared to most existing analytical works is the use of the practical three dimensional (3D) antenna model at the base stations specified by 3GPP, which is important for more accurate performance analysis with UAV users of much higher altitude. Some useful observations are obtained based on our theoretical analysis and numerical results.
Fascinating rhythms by chaotic Hopfield networks

This papers aims at experimentally confirming in very small Hopfield networks that chaos becomes the spontaneous dynamics of a network, when the number of inputs to be learned and stored in its dynamical attractors is superior to the size of this network. The spontaneous dynamics of the network are shown to increase in complexity by increasing the size of the learning set. The type of chaos exploited to code these inputs is related with the frustrated chaos described in previous papers. When, following a brute-force but robust learning of the inputs, the network is presented with a series of unlearned ambiguous input, experimental results show how chaotic regimes become the natural response of the network, reflecting in its frustrated dynamics the inherent ambiguity of the input. A live demonstration can be shown where the rhythms associated with the dynamics can be heard. This experimental work might give additional support to the Skarda and Freeman strong intuition that chaos should play an important role in the storage and the search capacities of our brains.
New family of p-ary sequences with optimal correlation property and large linear span

For an odd prime p and an integer n=(2m+1)k, a new family of p-ary sequences with optimal correlation property is constructed using Helleseth-Gong sequences. Period of the sequences is p/sup n/-1 and the size of family is p/sup n/. It is also derived that the linear span of the sequences in the family is (m+2)/spl middot/n except the m-sequence in the family. The maximum nontrivial correlation value R/sub max/ does not exceed 1+/spl radic/(P/sup n/) which means the optimal correlation property in terms of Welch's lower bound.
Comprehensive Design Methodology of Compound-Split Hybrid Electric Vehicles: Introduction of the Compound Lever as a Design Tool

Powertrain configurations described with elementary (physical) levers can intuitively depict the connections between planetary gear (PG) shafts and powertrain components. However, finding optimal compound-split hybrid configurations using the elementary lever is practically impossible due to the large design space. In fact, each of the existing 252 compound-split configurations has three design variables: two PG gear ratios and a final drive gear ratio. In this paper, a compound (virtual) lever-based design methodology that eliminates redundant elementary lever designs is proposed to enable a full compound-split hybrid electric vehicles design domain search. The performance metrics were assessed in the compound lever design space. Later, the designs were converted back to elementary lever configurations by applying a design space conversion map, and their performance metrics were plotted on a fuel economy versus acceleration performance plane to compare the potential of the 252 compound-split configurations. Finally, an optimal configuration that can reach 0–160 kph in 15.36 sec, which is 5.90 sec faster than that of the Prius configuration, while maintaining a competitive fuel economy, was selected. The proposed method revealed that there are still many configurations that are potentially better than the commercially available split hybrids.
Segmentation and Recovery of Pathological MR Brain Images Using Transformed Low-Rank and Structured Sparse Decomposition

We present a common framework for the simultaneous segmentation and recovery of pathological magnetic resonance (MR) brain images, where low-rank and sparse decomposition (LSD) schemes have been widely used. Conventional LSD methods often produce recovered images with distorted pathological regions, due to the lack of constraint between low-rank and sparse components. To address this issue, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method, which is robust for extracting pathological regions. Moreover, the well recovered images can be obtained using both structured sparse and computed image saliency as the adaptive sparsity constraint. Experimental results on MR brain tumor images demonstrate that our TLS2D can effectively provide satisfactory performance on both image recovery and tumor segmentation.
Fragment class analysis for testing of polymorphism in Java software

Adequate testing of polymorphism in object-oriented software requires coverage of all possible bindings of receiver classes and target methods at call sites. Tools that measure this coverage need to use class analysis to compute the coverage requirements. However, traditional whole-program class analysis cannot be used when testing partial programs. To solve this problem, we present a general approach for adapting whole-program class analyses to operate on program fragments. Furthermore, since analysis precision is critical for coverage tools, we provide precision measurements for several analyses by determining which of the computed coverage requirements are actually feasible. Our work enables the use of whole-program class analyses for testing of polymorphism in partial programs, and identifies analyses that compute precise coverage requirements and therefore are good candidates for use in coverage tools.
Distinguishing the Themes Emerging from Masses of Open Student Feedback

Student feedback is one of the key methods for assessing the quality of teaching in higher education. Feedback is often collected using both Likert-type scales and open-ended questions. However, open-ended text answers are a difficult resource to utilize because of the manual work involved in qualitative analysis, and it is a challenge to gain insight of the underlying themes or issues behind the feedback. This paper presents a study in which we create and analyze topic models from open-ended student feedback. First, 6087 individual student evaluations were collected from university courses between two academic years, from 2016 to 2018. Then, topic models from the feedback texts were created using the Latent Dirichlet Allocation method with the R programming language and environment for statistical computing. After analyzing the resulting topic models, six categories of feedback were distinguished: 1) Positive comments about arrangements, 2) dissatisfaction in the teaching, 3) comments about course arrangements and deadlines, 4) lack of student motivation, 5) interest in the topic and understanding the material, and 6) comments about interesting, rewarding but challenging courses. Finally, this paper discusses the topic modelling results to provide an insight into the automatic analysis of student feedback.