Miracle-dz/UniFAR-FTUs huggingface dataset:
Exclusion Zone Assisted eICIC in Massive MIMO Enabled Cognitive HetNets

In a massive MIMO-enabled cognitive HetNet, a multi-user massive MIMO macrocell is underlaid with dense cognitive small cells. Then, a plenty of exclusion zones (EZs) are formed around co-scheduled macrocell users. Applying enhanced inter-cell interference coordination (eICIC) in each EZ separately, we propose an EZ-assisted eICIC scheme which arranges a customized eICIC configuration for each EZ independently to others. The proposed scheme can overcome potential weaknesses of current eICIC and that of cognitive HetNets. Then, the EZ-assisted eICIC configuration problem is formulated as a general optimization problem, which maximizes overall weighted sum rate of the network. Since the problem is intractable, a cluster segmentation approach is, therefore, proposed to convert the problem into a feasible version. Furthermore, we design an iterative multi-user scheduling-based algorithm to solve the problem. Numerical results demonstrate that the proposed EZ-assisted eICIC scheme significantly outperforms eICIC and further eICIC in dense HetNet.
Towards Global People Detection and Tracking using Multiple Depth Sensors

In this work a novel approach for multi depth sensor person detection and tracking from top view is presented. We propose a probabilistic framework formulating the problem of people detection in multiple overlapping depth images as an inverse problem. As a generative forward model, we employ a simple differentiable 3D person model allowing us to detect people from arbitrary viewpoints. Furthermore, we extend our probabilistic framework to allow for tracking of individuals over time. Finally, we show how to solve for the global person trajectories exploiting differentiable rendering. The preliminary evaluation shows promising qualitative results of our approach on samples of three stereo vision based depth sensors observing an indoor scene.
Driving-PASS: An Automatic Driving Performance Assessment System for Stroke Drivers Based on ANN and SVM

Although many stroke survivors are not fully capable of driving, they drive again without any formal assessment due to an absence of valid screening tools. This leads to an elevated risk of accidents. Although an on-road test is considered a standard assessment method for items relevant to actual driving, it may be dangerous to evaluate all stroke drivers with the on-road test. For safe pre-screening of unsuitable stroke drivers, we propose an automatic Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS aims to provide not only information about problematic driving assessment items but also a decision about fitness to drive. The problematic driving items are classified by abnormal classifiers while the decision item is determined by a decision classifier in Driving-PASS. For designing the system, we firstly propose a subjective assessment method consisting of ten assessment items and one decision item. And then, we propose an automated method of the subjective assessment method with a machine learning approach (i.e., ANN and SVM) by using assessment criteria from five expert's judgments. Evaluation results demonstrate that Driving-PASS automatically assess not only the ten assessment items (total average Accuracy of 90% and F1-score of 88%) but also the decision item (Accuracy of 93% and F1-score of 92%). We expect Driving-PASS provides analytical assessment results that can be used in driving rehabilitation programs and contributes to reducing the risk of vehicle accidents by pre-screening unsuitable stroke drivers with high accuracy and reliability.
Optimized Hierarchical Cascaded Processing

Recently, there has been an increasing demand for advanced classification capabilities embedded on wearable battery constrained devices, such as smartphones or watches. Achieving such functionality with a tight power and energy budget has proven a real challenge, specifically for large-scale neural network-based applications. Previously, cascaded systems have been proposed to minimize energy consumption for such applications, either through using a single wake-up stage, or by using a linear- or tree based cascade of consecutive classifiers that allow early termination. In this paper, we expand upon these concepts by generalizing cascades to hierarchical cascaded processing, where a hierarchy of increasingly complex classifiers, each designed and trained for a specific subtask is used. This hierarchical approach significantly outperforms the wake-up based approach by up to 2 orders of magnitude in energy consumption at iso-accuracy, specifically in systems with sparse input data such as speech recognition and visual object detection. This paper presents a general design framework for such systems and illustrates how to optimize them toward minimum energy consumption. The text further proposes a roofline model for cascaded systems, derives system level trade-offs and proves the approaches validity through a visual classification case-study.
Open-Circuit Fault Diagnosis of Neutral Point Clamped Three-Level Inverter Based on Sparse Representation

Decomposition of the signal on the orthogonal or nonorthogonal basis of the signal space is the traditional method for fault feature extraction in the field of inverter fault diagnosis. These signal analysis methods make the result of signal decomposition not sparse and they are not self-adaptive. In recent years, sparse representation has received considerable attention in signal processing because the method can overcome the shortcomings of traditional methods by decomposing the signal on an over-complete dictionary instead of on an orthogonal or nonorthogonal basis. This paper proposes a combination of sparse representation and support vector machine (SVM) for the fault diagnosis of neutral point clamped (NPC) three-level inverter. First, the three-phase phase voltage signals are sampled as the characteristic signals for analysis. Then, the K-SVD algorithm is used as the fault feature extraction technology to obtain an over-complete dictionary and the sparse representation coefficients of the characteristic signals. The latter are used as the feature information for the characteristic signals. Finally, the SVM with powerful generalization capability is used as the fault identification method to identify NPC three-level inverter fault types according to the extracted feature information and analyze the fault diagnosis effect. Simulation experiments show that the combination of sparse representation and the SVM for fault diagnosis of NPC three-level inverters has the advantage of high diagnostic accuracy. The fault diagnosis method proposed in this paper is compared with other methods to further verify its superiority in the fault diagnosis of NPC three-level inverters.
Distributed Artificial Intelligence Enabled by oneM2M and Fog Networking

Deep learning enabled by neural networks has been proven to be an effective Artificial Intelligence (AI) algorithm in sophisticated applications. The algorithm is normally divided into two phases: learning phase and inference phase. In this research, we assume the learning phase is already accomplished offline and focus on expediting the inference phase by replacing the centralized processing of Cloud with the distributed processing of Fog. In our approach, inference algorithms in AI are distributed to multiple layers of Fog networking, constructed from oneM2M Middle Nodes. We verify the performance improvement of our proposed distributed AI/Fog system by comparing it against a Cloud-centric system based on a use case of smart shopping mall.
Outlier Detection using Hierarchical Spatial Verification for Visual Place Recognition

Spatial verification is a key step to remove outliers for accurate feature matching in visual place recognition. In this paper, we propose a novel method for outlier detection using a hierarchical spatial verification scheme. Given a set of putative correspondences between a pair of images, we convert the matching problem into a 4D transformation space and identify promising similarity transformations using Hough voting. In the hierarchical scheme, we first use a hypothesize-and-verify technique to identify groups of correspondences according to each similarity transformation. Second, the group with the largest number of correspondences serves as a standard to subsequently remove outliers in other groups by explicit geometric consistency checking. We have compared the proposed method with the state-of-the-art solutions on five popular public datasets to show that our method has better performance in place recognition and loop closure detection.
Lifelong Multi-Agent Path Finding in A Dynamic Environment

In tradition, the problem of Multi-Agent Path Finding is to find paths for the agents without conflicts, and each agent execute one-shot task, a travel from a start position to its destination. However, making just one planning for the agents may not satisfy the requirement in dynamic environments such as logistics sorting center, where the paths of the agents may constantly need to be adjusted according to the incoming tasks. The challenging issue is to dynamically adjust the already planned paths while make planning for the agents ready to execute new incoming tasks. In this paper, we formulate it into Dynamic Multi-Agent Path Finding (DMAPF) problem, the goal of which is to minimize the cumulative cost of paths. To solve this problem, we propose an algorithm called Lifelong Planning Conflict-Based Search (LPCBS), which can efficiently and optimally make planning for the new incoming tasks while adjusting the already planned paths. Experiment results show that the LPCBS performs much better than the existing works in each planning.
Voltage Control Method Based on Coordinative Optimization of User Energy Saving and Power Loss Reduction in Distribution Network

Both distribution network power loss and users' energy cost are taken into account in voltage control in this paper. According to the voltage characteristics of load and network loss, the feasibility of optimizing the users' energy cost and power loss by voltage control is discussed. On this basis, a voltage control model based on coordinative optimization of load and loss reduction of distribution network is established, considering the regulation capability and cost of on load tap changer (OLTC) transformers, distributed generators (DG) and capacitors. The improved particle swarm optimization (PSO) algorithm is used to obtain the optimal regulation capacity of the voltage regulating equipment at all times of a day. A topology simplification method of distribution network is studied to improve the practicability of the method. The simulative results of the actual distribution network verify the feasibility and effectiveness of the proposed method.
Switched-Capacitor-Based Single-Source Cascaded H-Bridge Multilevel Inverter Featuring Boosting Ability

Cascaded multilevel inverter (CMI) is one of the most popular multilevel inverter topologies. This topology is synthesized with some series-connected identical H-bridge cells. CMI requires several isolated dc sources which brings about some difficulties when dealing with this type of inverter. This paper addresses the problem by proposing a switched-capacitor (SC)-based CMI. The proposed topology, which is referred to as switched-capacitor single-source CMI (SCSS-CMI), makes use of some capacitors instead of the dc sources. Hence, it requires only one dc source to charge the employed capacitors. Usually, the capacitor charging process in a SC cell is companied by some current spikes which extremely harm the charging switch and the capacitor. The capacitors in SCSS-CMI are charged through a simple auxiliary circuit which eradicates the mentioned current spikes and provides zero-current switching condition for the charging switch. A computer-aid simulated model along with a laboratory-built prototype is adopted to assess the performances of SCSS-CMI, under different conditions.
Multi-energy Coordination and Schedule Considering large-scale electric vehicles penetration

The paper propose a novel multi-energy management strategy considering the energy storage and electric vehicles(EVs) participation. Power grid load fluctuates violently because of the intermittence of renewable energy. Data center(DC) energy consumption is taken into account and sustainability supplied by a priority of renewable energy source(RES) in this paper. The priority of a large number of EVs for scheduling is calculated and formed a charge/discharge priority list of schedule EVs. And then based on this.the renewable energy scheduling algorithm(RESA) and optimal charging/discharging control strategy of EVs are presented couple with comprehensively considering the state of multienergy output and real-time grid load. The overall energy management strategy aims to minimize the power fluctuation of grid. The simulation results demonstrate that the strategy proposed could effectively neutralize the intermittence of renewable energy and balance the load fluctuation of power grid to some extent. Meanwhile the EVs users' requirement for routine use could be flexibly satisfied.
A Novel Methodology to obtain Optimal PI Controller Gains using Multi-gene Genetic Programming for FOPTD Systems

This paper presents a novel method for tuning a PI controller for a first-order plus time delay (FOPTD) system based on a Multi-gene Genetic Programming (MGGP) and a Particle Swarm Optimization (PSO) algorithm. In our approach, the PSO stablishes a set of optimal gains of the controller for a FOPTD system, based on the plant parameters. Then, the MGGP obtains the mathematical equations to estimate the optimal gains determined by PSO. Finally, to validate the methodology proposed, a group of random systems were selected and tested in MATLAB-SIMULINK, using the calculated equations, focused in its behavior with respect to the maximum overshoot (Mp) and the Integral Square Error (ISE).
Wind Power Accommodation Optimization Considering Voltage Safety and System Energy Consumption

with the increase of wind power penetration, the influence of large-scale wind power integration on the static voltage security of power network is ever-increasing. In this case, accommodation level of wind power will directly lead to the change of the voltage level at each node in the network. In order to meet the static voltage safety of the power network, energy saving and emission reduction by the integration of wind power, a multi-objective wind power accommodation optimization model is proposed in view of the safety of network voltage. In this model, the optimal cost of energy consumption and the highest voltage security are taken as the targets, and the multi-objective quantum particle swarm optimization (QPSO) algorithm is used to obtain the optimal wind power accommodation level. The model is analyzed and verified based on IEEE-30 bus. The results show that the model is feasible and effective.
ICFHR 2018 Competition On Document Image Analysis Tasks for Southeast Asian Palm Leaf Manuscripts

This paper presents the results of the Competition on Document Image Analysis Tasks for Southeast Asian Palm Leaf Manuscripts that was organized in the context of the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR-2018). For this competition, three different corpus of palm leaf manuscripts written in three different scripts and languages (Balinese, Sundanese and Khmer) are used. Four Document Image Analysis (DIA) tasks are proposed as the challenges in this competition: binarization, text line segmentation, solated character/glyph recognition, and word transliteration. The results of this competition will be very useful in benchmarking analysis for the collection of palm leaf manuscripts, accelerating, evaluating and improving the performance of existing DIA system for a new type of document collection. This paper describes the competition details including the dataset, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods
Artists of the floating world : rethinking art/sustainability relations in the late days of modernity

ii Preface . iii Table of Contents . iv List of Tables . ix List of Figures . x Acknowledgements . xi Dedication . xii Chapter 1: Art-Sustainability relations in the late days of modernity . 1 1.1 The right instinct, the wrong invitation? 1 1.2 Hypothesis: a quest for better leverage . 3 1.3 Theoretical reframing: from the informative to the imaginative . 5 1.3. 1 Information Deficit Models and the instrumentalization of art . 7 1.3. 1.1 An enduring faith in knowing better . 7 1.3. 1.2 Disenchanting the aesthetic . 8 1.3. 2 Art as ontological reflexivity . 11 1.3. 2.1 A phenomenological view of ontological reflexivity . 12 1.3. 2.2 Heidegger and the ontological agency of art . 14 1.3. 2.2.1 world/earth tensions . 15 1.3. 2.2.2 Tools vs. Art . 16 1.3. 3 Raulet’s critique: a scrutiny of agency . 19 1.3. 4 Sustainability in an imaginary world? 21 1.3. 5 Bruno Latour and the Modern Constitution . 23 1.3. 5.1 The dichotomies of Modernity and their relevance to sustainability . 23 1.3. 5.2 Map/territory: the collapse of the Modern Constitution . 24 1.3. 5.3 Sustainability as a metaphysical problem: the failings of the IPCC . 27 1.3. 5.4 Transcendence vs. Immanence . 29 1.3. 5.5 Adding the imaginary to the Modern; Adding the material to the post-modern . 33 1.3. 5.6 A worldview in our hands? 36 Chapter 2: Methodology: The Ontological Pursuit . 40 2.1 Research context: The Greenest City Conversations . 40 2.2 The Double Agent . 43 2.3 From myth to method . 46
Functional Electronic Textiles: Circuit Integration and Energy Harvesting Power Supplies

This paper summarizes the research at the University of Southampton towards integrated autonomous electronic textiles (e-textiles). Textiles are difficult materials to work with due to their surface roughness and pilosity, and the constraints they impose on the processing of materials such as low-temperature curing. Powering autonomous e-textiles is at present also a limitation. This paper presents a technique for reliably integrating electronic circuits into textiles. A wide range of functional e-textiles has been demonstrated. Energy harvesting and storage methods are also evaluated and discussed. Whilst these offer the potential for delivering and storing useful amounts of energy, developing these into reliable and practical solutions remains an ongoing challenge.
Test Analysis of High-Power Multifunction Borehole-Ground Electromagnetic Transmitting System Under Field Conditions

With the increasing demands for mineral resources in China, artificial-source electromagnetic prospecting is playing an increasingly significant role. Consequently, electromagnetic instruments, which are crucial for prospecting, have attracted considerable attention. In order to verify the design targets of a self-developed high-power multifunction borehole-ground electromagnetic transmitting system, field tests are carried out in the mining area of Linxi, in Inner Mongolia. Design targets, including the stability of the current waveform, maximum transmitting voltage, maximum transmitting current, maximum output power, the continuous working time of the transmitting system with high-power output, transmission in single and dual boreholes, and multifunction technical target, have been tested. The test results demonstrate that the proposed system attains the expected design targets, and its performance is excellent. Moreover, the transmitting system can meet the requirements of controlled-source electromagnetic exploration and provide valuable reference for researchers in related fields.
Zonal Segmentation in Transrectal Ultrasound Images of the Prostate Through Deep Learning

Segmentation of both prostatic and zonal boundaries in transrectal ultrasound images is of great value in current clinical practice and for advancing techniques in computer-assisted diagnosis and inter-modality fusion. In this work, we propose a deep-learning approach to automatically segment the prostate and its main zones. In comparison with conventional methods, this method shows increased accuracy and Dice coefficients for full-prostate delineation. Moreover, the mean deviation between the annotated and predicted contours decreased substantially. Albeit the method still requires validation for different scanners and configurations, its real-time inference rate highlights the potential of this technique to be applied in clinical practice.
An improved Frontier-Based Approach for Autonomous Exploration

A new approach for autonomous exploration in an unknown scenario based on the concept of frontiers is proposed in this paper. Exploration frontiers introduced by [4], are the regions on the boundary between open space and unexplored space. A mobile robot is able to construct its map by adding new space and moving to unvisited frontiers until the entire environment has been explored. However, the original frontier strategy, suffering from local minima, only considers distance and size of unknown spaces, resulting in low exploration efficiency in complex environments. By making use of the robot heading information using wheel odometry and coarse graph representation of the environment, the modified exploration method is able to balance the mapping coverage and time expenditure to a greater extent. The proposed method is experimentally verified on a mobile platform, exploring a real-world office environment cluttered with a variety of obstacles.
Integrated Gate Drivers Based on High-Voltage Energy Storing for GaN Transistors

This paper presents a fully integrated gate driver in a 180-nm bipolar CMOS DMOS (BCD) technology with 1.5-A max. gate current, suitable for normally OFF gallium nitride (GaN) power switches, including gate-injection transistors (GIT). Full-bridge driver architecture provides a bipolar and three-level gate drive voltage for a robust and efficient GaN switching. The concept of high-voltage energy storing (HVES), which comprises an on-chip resonant LC tank, enables a very area-efficient buffer capacitor integration and superior gate-driving speed. It reduces the component count and the influence of parasitic gate-loop inductance. Theory and calculations confirm the benefits of HVES compared to other capacitor implementation methods. The proposed gate driver delivers a gate charge of up to 11.6 nC, sufficient to drive most types of currently available GaN power transistors. Consequently, HVES enables to utilize the fast switching capabilities of GaN for advanced and compact power electronics.
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response

Randomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Therefore, we developed a randomized response-based privacy-preserving crowdsourcing data collection and analysis mechanism. We designed a complementary randomized response (C-RR) method to guarantee individuals’ data privacy and to preserve features from the original data for analysis. We formalized a machine learning framework; our proposed method uses randomized data in the form of binary vectors to generate a learning network. Extensive experiments on real-world data sets demonstrated that our heavy-hitters estimation scheme, which applies C-RR and our data learning model, significantly outperformed existing estimation schemes in terms of data analysis.