Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Behavior Guided Block Allocation in Hybrid Cache-Based Edge Computing for Cyber-Physical-Social Systems

In Cyber-Physical-Social Systems (CPSS), large-scale data are continually generated from edge computing devices in our daily lives. These heterogeneous data collected from CPSS are urgently needed to be processed efficiently with low power consumption. Hybrid cache based edge computing can accelerate the computing speed for the edge devices. Hybrid cache consisting of spin-transfer torque RAM (STT-RAM) and static RAM (SRAM) has been proposed as last level cache (LLC) for energy efficiency recently in CPSS. However, the write operations on STT-RAM suffer from considerably higher energy consumption as well as longer latency than SRAM, the proper allocation of data blocks has a significant effect on both energy consumption and performance in the hybrid cache. So it is very useful to adjust the data allocation for the asymmetric-access in hybrid cache. To enhance the performance of hybrid cache, this paper proposes a novel statistical behavior guided block allocation (SBOA) scheme to process CPSS data. The key idea is to estimate the cache block characteristics based on the statistical behavior of data read/write re-references. We design a theoretical analysis model to optimize the energy consumption and guide block allocation in both SRAM region and STT-RAM region. Experimental results demonstrate that the proposed scheme reduces the dynamic energy consumption by 18.5%, and reduces execution time by 7.4% on average compared to the baseline with negligible overhead.
Study on Accurate Thermal Modeling of Press-Pack IGBT Module with Asymmetric Double-Side Cooling Structure based on Structure Function Curve by Orthogonal Experimental Design

Press-pack insulated gate bipolar transistor (PP IGBT) has been applied to large-scale Voltage Source Converter based High Voltage Direct Current Transmission (VSC-HVDC) applications. It has a multi-layered structure, the internal electrical and thermal contact are maintained by external clamping force along with internal disc springs. In this paper, a finite element simulation model is presented, which can correct the material properties and interfacial thermal resistance of the device based on structure function curve. It obtains the structure function curve through deconvolution and network transformation. By using Bahrami plastic contacting thermal resistance model and orthogonal experimental design, contacting thermal resistance can be predicted and thermal model converges rapidly, respectively. Comparison between simulated and experimental structural function curve when FRD works in the same module has verified the thermal model.
Entity Set Expansion for Detecting Fashion Trends

The Fashion industry revolves around Fashion trends. Designers and merchandisers plan products based on predicted trends, and online catalogs sell new trends to customers. An important part of tracking trends is tracking the ever-evolving fashion terminology. We tackle this problem by expanding a given fashion entity set using automatic synonym discovery in an external fashion related corpus, thus adding new terms to the fashion vocabulary. This paper presents supervised methods for synonym discovery that modify two unsupervised methods based on linear and non-linear contexts, respectively. We demonstrate that the task of synonym discovery benefits from using non-linear context as opposed to linear contexts for learning word embeddings.
Dynamic Analysis of a One-Parameter Chaotic System in Complex Field

Chaotic dynamics analysis of complex-variable chaotic systems (CVCSs) is an important problem in real secure communication and encryption. In this paper, a simple one-parameter chaotic system in complex field is proposed, whose nonlinear terms are the same as Lorenz system but the linear terms are much simpler. The proposed system has circular equilibria and therefore multi-stability can be measured by phase portraits, bifurcation diagrams and Lyapunov exponent spectrum. Its basin of attraction is filled with initial points leading to chaotic behaviors. The coexistence of infinitely many attractors is found in the proposed system, which is not reported in the existing complex-variable Lorenz system. Finally, two complexity indexes are used to measure dynamic characteristic with respect to parameter.
A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

Multi-objective optimization problems (MOPs) have received much attention in recent years. To deal with these problems, many multi-objective optimization algorithms have been proposed, especially the heuristic algorithms. In this paper, we proposed a multi-objective optimization algorithm called vascular invasive tumor growth optimization (VITGO), which based on the invasive tumor growth optimization and utilized a vascular mechanism to solve the MOPs. The newly proposed algorithm contains two parts: the vascular units and tumor cells. The former ones are utilized to record the Pareto solutions of the MOPs and define the search direction, and the latter ones are utilized to co-operate with vascular units to search deeper and wider. The mechanisms in the VITGO algorithm includes: endpoint generation, approximate Pareto front guidance, opposite searching, and adaptively detailed searching. Experiments showed that compared with other state-of-the-art multi-objective optimization algorithms, VITGO performs better in convergence and diversity.
Izzy: A Distributed Routing Protocol for In-band SDN Control Channel Connectivity

Software-defined networking proposes to physically separate control and data plane. This raises the question on how these two planes communicate, especially if the control channel is in-band, i.e., it shares physical network paths with normal network traffic. In-band control requires calculating multi-hop paths between controller and switches. Connectivity of the control channel is critical regarding network operation. Routing should provide robust connectivity under failures.We propose to achieve robust connectivity by distributing responsibility of maintaining connectivity of the control channel to the switches. We further present the design of a new distributed routing protocol Izzy for in-band SDN control channels. Izzy is based on a combination of a spanning tree with temporary addresses and includes state-of-the-art robustness mechanisms. In this paper, we describe Izzy, while focusing on update consistency on top of temporary addresses.
A Dual-Differential Subtractor-Based Auto-Nulling Signal Conditioning Circuit for Wide-Range Resistive Sensors

Analog signal conditioning circuit based on dual differential subtractor for wide range resistive measurement of single element resistive sensor is presented in this paper. The differential output from the sensor and a voltage-controlled reference resistor is followed by an auto-nulling technique, which helps to achieve high accuracy and wide dynamic range by generating a control voltage proportional to the sensor resistance. The presented automatic approach not only enhances the auto-nulling range but also ensures constant sensitivity. The interface design provides high accuracy, fast and linear response at the same time avoids drawbacks of lead wire variations. A prototype PCB of the proposed circuit is fabricated and tested. The experimental result shows that the circuit can measure a wide range of resistance (20 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> - 1.1 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega$ </tex-math></inline-formula>) with relative error less than 0.78%, compared to other bridge-based circuits which are capable of measuring the maximum resistance of up-to 10 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> for the same accuracy. Multiple variations of the proposed circuit are designed and experimentally tested for verifying the tunability of the circuit in terms of the sensitivity and the range of resistance measurement. The prototype circuit is also interfaced with an in-house fabricated MEMS-based ammonia sensor, proving its efficacy in resistive sensor system development.
Exploiting Stereo Sound Channels to Boost Performance of Neural Network-Based Music Transcription

In recent years deep learning begins to show great potential for automatic music transcription that reproduces MIDI-like music composition information, such as note pitches and onset and offset times, from music recordings. In the literature without exception the two stereo sound channels coming with music recordings were averaged into a single channel to alleviate the computation overhead, which, from an entropy standpoint, definitely sacrifices information. In this paper we propose a method to properly combine the two sound channels for deep learning-based pitch detection. In particular, through modifying the loss function the network is forced to focus on the worse performing sound channel. This method achieves start-of-the-art frame-wise pitch detection performance on the MAPS dataset.
Explainable Density-Based Approach for Self-Driving Actions Classification

This paper describes a new self-organizing neuro-fuzzy approach to autonomously learn interpretable models by self-driving cars. A new explainable self-organizing architecture and a new density-based feature selection method are proposed. These new approaches are used to classify different action states occurring from different self-driving conditions. The proposed approach is able to provide human understandable IF . THEN rules representation due to its learning engine which is composed of a massively parallel set of 0-order fuzzy rules. The proposed density-based feature selection method is based on the ranking of the densities of each feature in the data space, and takes advantage of the parallel characteristic of the proposed explainable self-organizing approach to create individualized subsets of features per class. The main goal of both proposed methods is to provide highly accurate models with high transparency, interpretability, and explainability for self-driving vehicles. In order to validate our proposal, experiments were realized using a real dataset provided by Ford Motor Company. The dataset contains different driving states occurring during self-driving performances. Results demonstrate that the proposed approach could surpass its state-of-the-art competitors in terms of accuracy for this challenge multiclass classification problem.
Multi-agent Learning for Energy-Aware Placement of Autonomous Vehicles

Mobility gets an increasing amount of meaning and significance in the modern society. In this paper, we introduce a multi-agent learning application for a multi-agent system in e-mobility. In particular, we propose a geospatial model for free-floating and autonomously driving e-trikes and demonstrate a calculation method of positioning e-trikes on a given area by using different methods of cluster analysis. The solution of the cluster analysis contains cluster centers which represent a positioning for the e-trikes. The solution is then evaluateded by a simulation model with more sophisticated parameters. This research field opens different opportunities of application scenarios, which are discussed in the conclusion.
Minimum Cost Survivable Routing Algorithms for Generalized Diversity Coding

Generalized diversity coding is a promising proactive recovery scheme against single edge failures for unicast connections in transport networks. At the source node, the user data is split into two parts, and their bitwise XOR is computed as a third redundancy sub-flow. In order to guarantee instantaneous failure recovery without costly node upgrades, the network must ensure that any two of the three sub-flows reach the destination node in case of a single edge failure only by allowing flow duplication or merging identical flows, and avoiding any coding operation in the core network. In this paper, we investigate the corresponding routing problem to calculate capacity-efficient routes for these sub-flows. We propose a polynomial-time algorithm for topologies without capacity constraints on the links and without capability limitations of the nodes. We show that with node limitations the presented algorithm (as well as a minimum cost disjoint path-pair) provides a 4/3-approximation for the routing problem. Furthermore, we formulate an integer linear program to provide a minimum cost solution with arbitrary constraints in general graphs and we propose a polynomial-time algorithm in directed acyclic graphs. Our simulation results suggest that with upgrading only a small set of core network nodes with flow duplication and merging capabilities most of the benefits of generalized diversity coding can be achieved.
Low to High Dimensional Modality Hallucination Using Aggregated Fields of View

Real-world robotics systems deal with data from a multitude of modalities, especially for tasks such as navigation and recognition. The performance of those systems can drastically degrade when one or more modalities become inaccessible, due to factors such as sensors’ malfunctions or adverse environments. Here, we argue modality hallucination as one effective way to ensure consistent modality availability and thereby reduce unfavorable consequences. While hallucinating data from a modality with richer information, e.g., RGB to depth, has been researched extensively, we investigate the more challenging low-to-high modality hallucination with interesting use cases in robotics and autonomous systems. We present a novel hallucination architecture that aggregates information from multiple fields of view of the local neighborhood to recover the lost information from the extant modality. The process is implemented by capturing a non-linear mapping between the data modalities and the learned mapping is used to aid the extant modality to mitigate the risk posed to the system in the adverse scenarios which involve modality loss. We also conduct extensive classification and segmentation experiments on UWRGBD and NYUD datasets and demonstrate that hallucination allays the negative effects of the modality loss. Implementation and models: https://github.com/kausic94/Hallucination.
Simulation Study of a Monolithic III-V/Si V-Groove Carrier Depletion Optical Phase Shifter

In this paper, we propose a new carrier depletion-type hybrid III-V/Si optical phase shifter using a n-III-V/p-Si hetero-junction, which can be fabricated with direct epitaxial growth of III-V semiconductors on Si. We numerically analyzed the performance of the III-V/Si hybrid optical phase shifter by comparing the performance in reverse bias operation with that of pure Si or III-V p-n optical phase shifters. The hybrid III-V/Si optical phase shifter showed improved modulation efficiency and lower optical loss compared to pure Si and III-V p-n optical phase shifters, owing to the large electron-induced refractive index change of III-V compound semiconductors, while avoiding the large hole-induced optical loss of III-V compound semiconductor. The simulation study suggests the feasibility of a very low voltage-length product <inline-formula> <tex-math notation="LaTeX">$(V_{ {\pi }} L)$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$0.07~\text {V}\cdot \text {cm}$ </tex-math></inline-formula>, a low insertion loss (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>) of 16 dB/cm, and a very low <inline-formula> <tex-math notation="LaTeX">$\alpha ~V_{ {\pi }}L$ </tex-math></inline-formula> product close to 1 <inline-formula> <tex-math notation="LaTeX">$\text {V}\cdot \text {dB}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$1.31~\mu $ </tex-math></inline-formula> m, which is 10<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> lower than for Si p-n optical phase shifters.
Temperature Independent Triaxial Force and Torque Sensor for Minimally Invasive Interventions

In this article, a triaxial force and torque sensor is developed and its response is evaluated. The sensor is designed for minimally invasive interventions. The fabrication method of the sensing structure is 3-D printing and the material used is a biocompatible acrylic plastic. Optical fibers are used as sensing elements in this sensor and the working principle is based on the Bragg wavelength shift of Bragg gratings. The response of the sensor is highly linear along all axes of measurement with the range of working around 0.7 N. The sensor has a high resolution of force and torque measurement which are 0.05 N and 0.1 Nmm, respectively. Experiments show that the sensor can accurately measure the applied force in a dynamic environment. Experiments are conducted to evaluate the thermal response of the sensor. The results show that the sensor can measure all components of the applied forces and torques to the surgery instruments without temperature cross-sensitivity.
Using machine learning in an open optical line system controller

The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.
A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

Sparse recovery (SR) based space-time adaptive processing (STAP) has attracted much attention due to its small requirement of snapshots in the estimation of the clutter plus noise covariance matrix (CNCM). However, most of the existing SR STAP methods suffer from the grid mismatch effect of the dictionary matrix. In this paper, a novel grid-less total variation minimization (TVM) based STAP approach is proposed, which avoids the discretization of the spatial-temporal profile and possible mismatch of the spatial-temporal gird. The optimization problem is firstly introduced to estimate the clutter subspace steering vector by minimizing the defined atomic norm based on the TVM. Then the optimization problem is reformulated via utilizing the property of the radar space-time steering vector and approximation of the Bessel function. Finally, with a solution obtained by the optimization problem, a projection method is presented to obtain an accurate estimation of the CNCM. The proposed STAP method can be applied for both the side-looking and non-side-looking case. Numerical results validate its effectiveness compared with the other SR STAP methods.
On Activity Identification Pipelines for a Low-Accuracy EEG Device

Analyzing EEG signals can help us make implications about the user's activities or even thoughts which can result in a myriad of applications. However, clinical EEG monitoring tools are expensive, often immobile and in need of professional supervision. Lately a couple of companies started the production of relatively cheap, easy-to-use, and mobile devices with significantly lower accuracy. In this paper we intend to investigate the usability of these devices in recognizing concrete activities e.g. winking, raising a hand etc. To this end, we provide a comprehensive analysis by applying several signal processing and machine learning methods to the time series recorded by the EMOTIV Epoc+. In addition to the usability analysis of these devices, we also propose a flexible and general pipeline for processing and classifying time series of EEG signals. We hope that this pipeline will make a contribution to researchers' work on this field.
Current Sharing Control of Parallel Bidirectional DC-DC Converter for Energy Storage Applications in Islanded Microgrid

The paper explores parallel connected bidirectional DC-DC converters for energy storage applications in microgrid. The parallel operated bidirectional DC converter confronts with inaccurate current sharing due to the voltage imbalance and the circulating current. The parallel DC-DC power stage and its control in the micro grid proves to be more prominent and its efficiency is predetermined by the factors like the minimum circulating current and voltage balance. This paper aims at achieving accurate current sharing with a power based droop mechanism. The cynosure of the current work is carried out in two stages involving the proposed droop mechanism and the cascaded control. Due to the self-regulating voltage gain, bidirectional capability, the dual active bridge bidirectional DC-DC converter is chosen for the current work. The proposed droop strategy is deployed for battery fed converters to share power irrespective of instantaneous estimation of state of charge of batteries. This can be achieved by deriving a relation between the output power and the state of charge of batteries and formulating a droop equation based on power. The system is simulated in the MATLAB Simulink environment and the results were analyzed experimentally.
Hardware-in-the-Loop Test of an Open-Loop Fuzzy Control Method for Decoupled Electrohydraulic Antilock Braking System

To verify the functionality of an intelligent open-loop fuzzy-logic-based antilock braking system control method for four on-board motor drive electric sport utility vehicle, a hardware-in-the-loop experiment is conducted in this article. The experimental facility includes a novel decoupled electrohydraulic brake test bed characterized by highly nonlinear dynamics and time-varying behavior. It reproduces real pressure dynamics of the brake circuit allowing for simulation of various tire–road adhesions conditions and brake blending scenarios. To cope with degradation of antilock braking system performance induced by unexpected changing environmental conditions, such as road surface, the developed fuzzy control features a very simple yet effective and robust road surface recognition tool with estimation of the peak braking demands. Thus, the fuzzy logic serves as a controller and road surface estimator simultaneously allowing for complex mathematical modeling and feedback control avoidance not sacrificing safety system's performance. The results indicate that the control method manages highly nonlinear and time-variant dynamics of the brake system and offers significant feasibility for optimal slip control at regenerative braking, ensuring fuzzy control's potentiality for real-time application.