Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuromodulated Learning in Deep Neural Networks

In the brain, learning signals change over time and synaptic location, and are applied based on the learning history at the synapse, in the complex process of neuromodulation. Learning in artificial neural networks, on the other hand, is shaped by hyper-parameters set before learning starts, which remain static throughout learning, and which are uniform for the entire network. In this work, we propose a method of deep artificial neuromodulation which applies the concepts of biological neuromodulation to stochastic gradient descent. Evolved neuromodulatory dynamics modify learning parameters at each layer in a deep neural network over the course of the network's training. We show that the same neuromodulatory dynamics can be applied to different models and can scale to new problems not encountered during evolution. Finally, we examine the evolved neuromodulation, showing that evolution found dynamic, location-specific learning strategies.
Design and Analysis of Fuzzy Logic Controllers for Microgrid Voltage Control

In this paper, a fuzzy logic controller (FLC) is added to the switching of the voltage source converter (VSC) to enable the operation of the microgrid under stable conditions, with the help of the energy storage system and photovoltaic power supply and demand side power balance. The performance of the FLC was examined in different loading situations of the system and it was compared with the PI controller. In addition, the microgrid is operated in both island-mode and grid-tied mode, where normal and critical loads are supplied and voltage and frequency oscillations are quickly switched to the steady state within the allowable tolerance limits. The modeling of the microgrid is built with the MATLAB/Simulink program to reflect the behavior in the real system.
An Approach to Balance Maintenance Costs and Electricity Consumption in Cloud Data Centers

We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers’ CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several scenarios from a real CDC, show that MECDC largely outperforms two reference algorithms, which instead either target the load balancing or the energy consumption of the servers.
Event-Triggered Finite-Time Integral Sliding Mode Controller for Consensus-Based Formation of Multirobot Systems With Disturbances

This paper deals with the distributed fast and finite-time consensus problem of multiagent systems with bounded disturbances in a leader-follower-based frame work. A novel integral sliding mode-based robust finite-time event-triggered control strategy has been proposed. A fast reaching law is also utilized to improve the speed of convergence. The triggering condition is derived based on the defined novel measurement error, suited for systems with model uncertainties/disturbances. The expression for the lower bound for the interexecution time has been derived to ensure that the zeno behavior is avoided. The proposed nonlinear consensus protocol is such that the desired relative state deviation between the agents can be achieved with a directed graph topology. The theoretical results are validated through real-time experimentations done using Pioneer P3-DX as well as FireBird VI robots.
Using Simulation to Improve Human Pose Estimation for Corner Cases

Recognizing actions of humans, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Today's technical perception solutions have been developed and tested mostly on standard vision benchmark datasets where manual labeling of sensory ground truth is a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in such data leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework which offers to train and validate algorithms on various environmental conditions. For this paper we created a dataset, containing rare human activities in urban areas, on which a current state of the art algorithm for pose estimation fails and demonstrate how to train such rare poses with simulated data only.
An Area-Efficient On-Chip Memory System for Massive MIMO Using Channel Data Compression

Massive multiple–input-multiple–output has proven to deliver improvements in both spectral and transmitted energy efficiency. However, these improvements come at the cost of critical design challenges for the hardware implementation due to the huge amount of data that has to be processed immediately, especially the storage of large channel state information (CSI) matrices. This paper presents an on-chip memory system equipped with CSI which provides high area efficiency, while supporting flexible accesses and high bandwidths. Optimization across system-algorithm-hardware is used to develop hardware-friendly compression algorithms exploring propagation characteristics and large antenna-array features. More specifically, group-based and spatial-angular transform algorithms are implemented in a heterogeneous memory system, which consists of an unified memory for storing compressed CSI and a parallel memory for flexible access. Up to 75% memory can be saved for a 128-antenna system, at a less than 0.8dB performance loss. Implemented in ST 28nm FD-SOI technology, the capacity of designed system is 1.06Mb, which is equivalent to 4Mb uncompressed memory and can store 100 $128\times 10$ channel matrices. The area is 0.47 mm2, demonstrating a 58% reduction compared with a memory system without CSI compression. With a supply voltage of 1.0V, the memory system can run at 833 MHz, providing a 833Gb/s access bandwidth.
Mitigation of Torsional Vibrations in Drilling Systems: A Robust Control Approach

Stick-slip vibrations decrease the performance, reliability, and fail safety of drilling systems. The aim of this paper is to design a robust output-feedback control approach to eliminate torsional stick-slip vibrations in drilling systems. Current industrial controllers regularly fail to eliminate stick-slip vibrations, especially when multiple torsional flexibility modes play a role in the onset of stick-slip vibrations. As a basis for controller synthesis, a multimodal model of the torsional dynamics for a real drill-string system is employed. The proposed controller design strategy is based on skewed- $\mu$ DK iteration and aims at optimizing the robustness with respect to uncertainty in the nonlinear bit–rock interaction. Moreover, a closed-loop stability analysis for the nonlinear drill-string model is provided. This controller design strategy offers several benefits compared with existing controllers. First, only surface measurements are employed, therewith avoiding the need for down-hole measurements. Second, multimodal drill-string dynamics are effectively dealt with in ways inaccessible to state-of-practice controllers. Third, robustness with respect to uncertainties in the bit–rock interaction is explicitly provided and closed-loop performance specifications are included in the controller design. Case study results confirm that stick-slip vibrations are indeed eliminated in realistic drilling scenarios using the designed controller in which state-of-practice controllers fail to achieve this.
A Deep Neural Network Compression Algorithm Based on Knowledge Transfer for Edge Device

The computation and storage capacity of the edge device are limited, which seriously restrict the application of deep neural network in the device. Toward to the intelligent application of the edge device, we introduce the deep neural network compression algorithm based on knowledge transfer, a three-stage pipeline: lightweight, multi-level knowledge transfer and pruning that reduce the network depth, parameter and operation complexity of the deep learning neural networks. We lighten the neural networks by using a global average pooling layer instead of a fully connected layer and replacing a standard convolution with separable convolutions. Next, the multi-level knowledge transfer minimizes the difference between the output of the "student network" and the "teacher network" in the middle and logits layer, increasing the supervised information when training the "student network". Lastly, we prune the network by cuts off the unimportant convolution kernels with a global iterative pruning strategy.
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images

In this paper, we propose a saliency detection method (SAMM) by using the surroundedness and absorption Markov model. First, the approximate area of the salient object is predicted by the surroundedness to the eye fixation point prediction. Second, a simple linear iterative clustering algorithm is applied to the original image to calculate superpixels, and a two-ring image graph model is formed. We calculate two initial saliency maps <inline-formula> <tex-math notation="LaTeX">$S_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula>. Prior map <inline-formula> <tex-math notation="LaTeX">$S_{1}$ </tex-math></inline-formula> is calculated by applying the absorption Markov chain, as the superpixel-based region of the two boundaries farthest from the predicted salient object is taken as the background region, while map <inline-formula> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula> is calculated by using the absorption Markov chain to detect the superpixels in the approximate region of the salient object as a foreground region. The final saliency map is obtained by combining <inline-formula> <tex-math notation="LaTeX">$S_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula>. Finally, a guided filter is used to reduce the background noise from the saliency map. For the evaluation, experiments are performed on six publicly available test datasets (MSRA, ECSSD, Imgsal, DUT-OMRON, PASCAL-S, and MSRA10k), and the results are compared against 10 state-of-the-art saliency detection algorithms. Our proposed saliency detection algorithm (SAMM) performs better with higher precision_recall, AUC, <inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>-measure, and minimum mean absolute error values.
Analysis and Design of a Wideband Coaxial Transition to Metal and Printed Ridge Gap Waveguide

In this paper, a wideband coaxial to ridge gap transition is proposed and implemented. The transition has a compact size, wide bandwidth, and simple structure. It can be used to excite ridge gap waveguides implemented by the printed circuit board or computer numerical control (CNC) technologies. A similar circuit model for a coax-to-microstrip junction is proposed and used to establish a systematic design procedure for the proposed transition. Perfect electric conductor and perfect magnetic conductor (PMC) boundaries are used to make the procedure independent of the fabrication technology. The PMC is replaced by a bed of nails for ridge gap realized by CNC technology and mushroom-shaped structure in the case of PCB technology. The proposed transition based on the PCB technology is fabricated and measured. There is a good agreement between simulated and measured results which validates the proposed design. The transition has a 59.22% bandwidth with <inline-formula> <tex-math notation="LaTeX">$S_{11} < -10$ </tex-math></inline-formula> dB and <inline-formula> <tex-math notation="LaTeX">$S_{21} > -0.5$ </tex-math></inline-formula> dB.
A Framework for Real-time Traffic Sign Detection and Recognition using Grassmann Manifolds

We propose a novel, real-time framework for traffic sign detection and recognition using a camera mounted on the dashboard of a moving vehicle. Traffic sign detection and recognition plays an important role in Advanced Driver Assistance Systems (ADAS) as it helps increase driving safety. However, it is very challenging to detect and recognize traffic signs because of challenges such as perspective distortion, illumination variation, occlusion and motion blur from moving vehicle. In our framework, we use Hue Saturation Value (HSV) color filtering for traffic sign detection. In our Grassmann manifold based traffic sign recognition framework, we create subspaces of each unique traffic sign. These subspaces accommodate the uncertainties that occur during detection and variations in different instances of the same traffic sign. These subspaces lie on a Grassmann manifold and we use discriminant analysis on Grassmann manifolds for recognising them. We have carried out extensive experiments on multiple publicly available traffic sign datasets and compared our proposed framework with multiple state-of-the-art methods. Experimental results show that our system is robust and has a high degree of accuracy.
Fast Maneuver Planning for Cooperative Automated Vehicles

A lane following and lane changing maneuver planning method for automated vehicles is investigated, which is capable of evaluating and incorporating cooperative agreements between several automated vehicles. An application level cooperation protocol is discussed, which allows vehicles to negotiate space time reservations in conflict areas via Vehicle-to-Vehicle communication. The planning method is based on decoupling of longitudinal and lateral movement directions, formulation of convex quadratic programming problems and input-output linearization for recovery of a full state reference trajectory and feed-forward controls. Several different lane following and merging maneuvers can be planned in one update cycle in order to support an informed selection of the currently best driving strategy. We demonstrate and evaluate the communication protocol and the maneuver planning method on cooperative lane changing scenarios with a physical automated vehicle as well as in a real time simulation.
Multilevel Single Phase Isolated Inverter with Reduced Number of Switches

This paper proposes a cascaded single phase multilevel inverter using an off-the-shelf three-phase inverter and transformer. The concept is based on a cascaded connection of two inverter legs using a typical three phase inverter in such a way that the third leg is shared between the other two phases. The cascaded connection is achieved through an integrated series transformer with a typical three-phase transformer core. Utilization of a special transformer design has been previously proposed in the Custom Power Active Transformer. However, cascaded connection of inverter legs has not been previously investigated with such a concept. In this way, a three-leg inverter and a three-phase transformer are converted into an isolated multilevel single-phase inverter based on an unique configuration and modulation technique.
Modeling User Intrinsic Characteristic on Social Media for Identity Linkage

Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this article, we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users’ interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user’s characteristics from two datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and users’ group psychology can result in mis-linkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mis-linked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results.
A Comparative Study of Machine Learning Techniques for Real-time Multi-tier Sentiment Analysis

Nowadays, Big Data, both structured and unstructured data, are generated from Social Media. Social Media are powerful marketing tools and social big data require real-time tracking and analytics because the speed may indeed be the most important competitive business profits. Compared to batch processing of Sentiment Analysis on Big Data Analytics platform, Real-time analytic is data intensive in nature and require to efficiently collect and process large volume and high velocity of data. Real-time multiclass Sentiment Analysis is oriented towards classification of text into more detailed sentiment labels in real-time manner. But Multiclass Sentiment Analysis with Single-tier architecture where single classification model is developed and entire labeled data is trained may decrease the classification accuracy. In this paper, Real-time Multi-tier Sentiment Analysis system (RMSA) is proposed to achieve high level performance of multi-class classification in Real-time manner. Lexicon and learning based classification scheme with Multi-tier architecture are combined to develop the proposed system. Real-time twitter stream data is collected by apache flume and, large volumes and high velocity of social data is efficiently analyzed by Spark. To improve the classification accuracy, the suitable classifier is selected by comparing the accuracy of three different learning based multiclass classification techniques: Naïve Bayes, Linear SVC and Logistic Regression. The evaluation results show that Real-time Multi-tier Sentiment Analysis will achieve the promising accuracy and Linear SVC is better than other techniques for Real-time Multi-tier Sentiment Analysis.
A CNN-Based Approach to Detecting Text from Images of Whiteboards and Handwritten Notes

Detecting handwritten text from images of whiteboards and handwritten notes is an important yet under-researched topic. In this paper, we propose a convolutional neural network (CNN) based approach to address this problem. First, to detect text instances of different scales, a feature pyramid network is adopted as a backbone network to extract three feature maps of different scales from a given input image, where a scale-specific detection module is attached to each feature map. Then, for a pixel on each feature map, a detection module is used to predict whether there exists a text instance at its corresponding location in the input image. For positive prediction, the bounding box of the detected text segment and the links between the concerned pixel and its 8 neighbors on the feature map are predicted simultaneously. Based on the linkage information, text segments extracted from each feature map are grouped into text-lines respectively and wrongly grouped text-lines are separated by a graph-based text-line segmentation method. Finally, detection results from three different feature maps are aggregated by a skewed non-maximum suppression algorithm. Our proposed approach has achieved superior results on a testing set consisting of 285 natural scene images of whiteboards and handwritten notes.
Toward A Low Cost, High Performance ECG Based Biometrics: A Preliminary Work

An electrocardiogram (ECG) test is not only a very important tool in diagnosing heart diseases, but also it has been proved that the ECG shows unique patterns for biometric recognition. Hence, Individual can be identified once ECG signature is extracted. In this paper we prepare the field to design a low cost and high performance person recognition using mobile sensor. As the work can be divided into three parts, sensing signals from mobile device, simulating the recognition algorithm and implementing the solution in a mobile application, we focus in this paper on the second part. Indeed we develop a basic identification method with MATLAB fed by some ECG signals from Physionet database, which involves preprocessing, features extraction, dimensionality reduction and classification. Results are very encouraging and can highly be improved, first by enlarging database and second, by the improvement of each block in the system.
Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects' attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training.
Design and Implementation of a Frequency Hopping Hybrid Multiple Access Protocol on FPGA

The frequency hopping ad hoc network system is the product of frequency hopping (FH) technology and ad hoc network technology. Because of its anti-interference, mobility and invulnerability, the frequency hopping ad hoc network is widely applied to military and civil communications systems. To make efficient use of the transmission channel, a frequency hopping hybrid multiple access (FHMA) protocol is proposed for frequency hopping ad hoc network. Each hop duration of the FH is divided into three parts. The first one is the transition period for frequency synthesis, and the following part is the time division multiple access (TDMA) period for reservation based multiple access, and the last part is the carrier sensing multiple access with collision avoidance (CSMA/CA) period for random access. Furthermore, an FPGA based implementation design is given for FHMA, where the transmitter module as well as the receiver module are designed with the finite state machine (FSM) method. Finally, the FHMA is implemented in Xilinx Vivado design suite to show the project is feasible and efficient. This protocol implementation is done in Verilog HDL language so that it can be easily transplanted. Behaviour simulation results verifies the correctness of the implementation.