Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Matrix Generation Framework for Thermal Aware Placement in VLSI

Since hotspots and temperature gradients are reliability and performance-critical issues in processors, thermal awareness finds a vital place in the processor design cycle. Incorporating thermal awareness at the level of physical design, this work proposes a new, fast, and efficient thermal aware placement algorithm called the Thermal Aware Matrix Placement Optimizer (TAMPO) for gate arrays. The algorithm TAMPO is composed of the following components: an improved heat diffusion aware cell arrangement technique called the Initial Matrix Generator (IMaGe), a unique stochastic thermal model based on a thermally improved interpretation of the well known Matrix Synthesis Problem (MSP) and a Simulated Annealing (SA) engine for finding the global optimum solution. TAMPO targets to reduce the peak temperature while maintaining improved values of temperature gradients and the standard deviation in cell temperature with respect to the average chip temperature. This work also presents a methodology, the Co-optimized TAMPO, which extends the concept of TAMPO to simultaneously optimize the thermal attributes and the wirelength of a chip. The proposed algorithms realize a placement in matrix arrangement and upon experimentation on the ISCAS89 benchmark circuits encouraging results have been obtained.
A Deep Adaptation Network for Speech Enhancement: Combining a Relativistic Discriminator With Multi-Kernel Maximum Mean Discrepancy

In deep-learning-based speech enhancement (SE) systems, trained models are often used to handle unseen noise types and language environments in real-life scenarios. However, since production environments differ from training conditions, mismatch problems arise that may cause a serious decrease in the performance of an SE system. In this study, a domain adaptive method combining two adaptation strategies is proposed to improve the generalization of unlabeled noisy speech. In the proposed encoder-decoder-based SE framework, a domain discriminator and a domain confusion adaptation layer are introduced to conduct adversarial training. The model has two main innovations. First, the algorithm optimizes adversarial training by introducing a relativistic discriminator that relies on relative values by applying the difference, thus avoiding possible bias and better reflecting domain differences. Second, the multi-kernel maximum mean discrepancy (MK-MMD) between domains is taken as the regularization term of the domain adversarial loss, thereby further decreasing the edge distribution distance between domains. The proposed model improves the adaptability to unseen noises by encouraging the feature encoder to generate domain-invariant features. The model was evaluated using cross-noise and cross-language-and-noise experiments, and the results show that the proposed method provides considerable improvements over the baseline without an adaptation in the perceptual evaluation of speech quality (PESQ), the short time objective intelligibility (STOI) and the frequency-weighted signal-to-noise ratio (FWSNR).
End-to-end Learning for Optical Fiber Communication with Data-driven Channel Model

An end-to-end autoencoder is proposed for optical fiber communication system, which is based on the data-driven channel modeled by BiLSTM technique. The data-driven channel not only solve the problem about back-propagation but reflect the complex channel characteristic. The proposed can effectively reduce the dependence on expert knowledge and obtains the lower BER than conventional method.
In-field Vis/NIR hyperspectral imaging to measure soluble solids content of wine grape berries during ripening

Monitoring the quality attributes of grapes is a practice that allows to check the grapes’ state of ripeness and to decide when it is appropriate to proceed with the harvest. In the present study, a non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field using a Vis/NIR (400–1000 nm) hyperspectral camera (HSC) between the rows of ‘Sangiovese’ (Vitis vinifera L.) vineyard destined for wine production. One vineyard row was analyzed on 13 different days. During the trials, 33 berries were collected and the soluble solids content (SSC) expressed in terms of °Brix (°Bx) was measured by a portable digital refractometer. The mean spectra of the selected berries were extracted from each hyperspectral (HS) image. The pre-treated mean spectra were used to predict the SSC of the berries by means of partial least squares (PLS) regression, obtaining a value of R2 = 0.75 in cross-validation, with RMSECV = 0.84 °Bx. The present study shows the potential of the use of HSI technology directly in the field through proximal measurements under natural light conditions for the prediction of the SSC quality attribute of grapes.
Adaptive Multi-Channels Allocation in LoRa Networks

In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell’s devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways’ intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.
The Effect of Cylinder Deactivation on Engine Performance

A simulation of a diesel engine under cylinder deactivation at part load was conducted in GT-SUITE. The prototype engine is a 16-cylinder diesel engine for locomotive and marine purpose. Cylinder deactivation was implemented by operating valve train system of inactive cylinders in the following different strategies: all valves operate normally; intake valves are closed, exhaust valves operate normally; intake valves operate normally, exhaust valves are closed; all valves are closed. The fuel efficiency was evaluated in terms of relative brake specific fuel consumption. To identify the factors affecting fuel efficiency, an analysis of fuel energy distribution including friction losses, pumping losses, heat transfer to cylinder walls and exhaust gas energy was carried out. The histograms of fuel energy distribution for different strategies of cylinder deactivation were shown. The effect of fuel saving was obtained by closing intake and exhaust valves in the inactive cylinders; other options of cylinder deactivation lead to a decrease in fuel economy.
Design Method of Aircraft Electric Machines for Hybrid Propulsion Systems

This paper describes the methodology adopted for the development of electrical machines for hybrid propulsion systems (HPS) of aircrafts. A nominal power of 400 kW and a rotating speed of 12,000 rpm are the main specifications of the machine taken as reference. All the design steps, including the machine concept generation, the multi-physics numerical approach and the experimental evaluation of the performance are considered. Additionally, challenges encountered during the design process and the methods to overcome them are also presented. The technical failure observed during the testing of the prototype is being documented and the reasons which led to it are analyzed.
QoE Assessment of Video Codecs for Video Streaming over 5G Networks

Nowadays, the need for more efficient video compression algorithms is growing, due to the increasing consumption of video content in higher resolutions. Thus, it is obvious that video compression technologies play a key role in the distribution of video content in broadcasting. In this paper, a comparative analysis and QoE assessment over the most recent video codecs in terms of video transmission over 5G networks are presented. The video compression comparison takes into account performance metrics considering the bitrate savings versus the image quality, in order to provide evidence about their adaptation under real-time streaming conditions. The video test sequences used in our experiments were retrieved from miscellaneous sources and their common characteristic is that they have High, Full or Ultra High Definition resolution. Video codecs’ performance was tested by using commercial coding libraries and reference software of the coding standards.
Support for Decision-Making to Ensure Reliable Operation of Transformers as Part of a Responsible Power Facility

Power transformers are critical equipment for power plants and electrical networks. Their trouble-free operation generally determines the reliable and safe operation of any power facility. The article examines the features of using individual means of transformers on-line monitoring, which makes it difficult to ensure their reliable operation as part of responsible power facilities. The feasibility and effectiveness of integrating individual monitoring components into a single complex with the organization of information and analytical support for decision-making based on the development of models and an algorithm for predictive assessment of the technical condition of power transformers are shown. Statistical models of pattern recognition formed using the Bayesian identification method able to provide a predictive assessment of the technical condition with a high degree of confidence, which allows identifying dangerous defects in transformers at an early stage of development and preventing any failures in the operation of the power facility equipment. The results obtained are implemented on the example of block transformers of one of the lowland hydroelectric power stations. They illustrate and discuss the possible prospects of the proposed solution for ensuring the operational reliability of the transformer equipment of a responsible power facility.
Detection of Breast Cancer From Whole Slide Histopathological Images Using Deep Multiple Instance CNN

Histopathological Whole Slide Imaging (WSI) has become a standard in the detection of breast cancer. Automated image analysis methods attempt to reduce the workload from the clinicians and Convolutional Neural Networks (CNNs) are a popular choice for this purpose. However, size of a WSI image typically is approximately $40,000\times 40.000$ pixels (can reach up to $100,000\times 100.000$ pixels). CNNs cannot handle such large images. Moreover, downscaling a WSI image causes degradation of small-scale visual information. Hence, a large number of small patches (containing critical visual information) from a WSI image are extracted by a trained pathologist and are used for training. However, it requires massive amounts of time to precisely search and label appropriate class-representative patches. To address this issue, a Deep Multiple Instance Learning (MIL) based CNN framework has been introduced in this paper. In the proposed framework every slide is represented as a bag of extracted patches. Only the bag label is used for training, thus eliminating the requirement to provide patchwise labels. The patches inherit the label of the bag containing them. A WSI image (i.e. a bag) is labeled benign if all its patches are benign and labeled malignant even if a single patch contains malignant cells. Learning can be carried out at the bag level even with noisy patch labels. Performance of this method was evaluated using the BreakHis, IUPHL and UCSB breast cancer datasets where 93.06%, 96.63%, 95.83% accuracy was achieved respectively.
Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process—especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neural network (CNN) with three different word representation techniques: Word to Vector (Word2Vec), Global Vector (GloVe), and Embeddings from Language Models (ELMo). Experiments were performed on datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was measured as an evaluation metric. The experimental results suggest that the ELMo-based CNN approach performs best for the bug triage problem. GloVe-based CNN slightly outperforms Word2Vec-based CNN in many cases. Word2Vec-based CNN outperforms GloVe-based CNN when the number of samples per class in the dataset is high enough.
Visuomotor Steering angle Prediction in Dynamic Perception Environment for Autonomous Vehicle

Visuomotor coordination in driving assists the driver in performing necessary action expeditiously. Over the recent years, the development towards autonomous vehicles has accelerated tremendously, and an increase in the computational capabilities motivates us to train the complex deep neural network models using visual cues for better understanding of visuomotor coordination in the autonomous vehicles. In this work, we exploit this problem by using visual cues in the images to predict the steering angle for a self-driving car. The future frames are predicted to estimate a look-ahead steering angle so that self-driving car can make appropriate decisions. In our dynamic predictive model, we have used the deep convolution-LSTM model for predicting the future frames and a series of ResNet blocks for predicting the steering angle for the self-driving car. Moreover, we have used segmentation as an auxiliary information for training the dynamic predictive network. The efficacy of our dynamic predictive model is rigorously tested on our collected and Udacity dataset.
Acoustic Enhanced Camera Tracking System Based on Small-Aperture MEMS Microphone Array

The camera tracking systems based on visual image processing face a problem that they are completely ineffective in their blind zones. To address this problem, a design of acoustic enhanced tracking system combining visual and auditory target tracking methods is reported in this article. The system holds the abilities of performing sound direction estimation and target tracking in real-time. Estimating direction of arrival of the sound accompanied with the target helps the camera turn towards the target outside the field of view. This sound-triggered mode of camera operation makes a significant supplement to conventional cameras’ working state. Considering the embedded system is necessary in consideration of the cost and size of the system in practical application, we designed a small aperture array with 7 digital omnidirectional MEMS microphones and built the overall system based on FPGA and ARM. The experiments were carried out in a normal indoor environment and the results confirmed that the system can perform auditory and visual tracking in real-time.
Geometrical Features based Visual Relocalization for Indoor Service Robot

In traditional SLAM methods, the environment map is the simply assembled points or lines, which makes it difficult to directly perform relocalization using such map. This paper presents a new implementation method for indoor environment representation and visual relocalization using RGB-D sensor. The method is developed for indoor service robots to perform relocalization using the observed point and line features. In this paper, the sparse feature map, line segment map, and dense point cloud map of an environment are learned by a random forest to regress the correspondences between visual features and 3D world locations, geometrical features and 3D world locations. Using the learned forest, landmark positions are efficiently predicted and the camera poses are then estimated in a probabilistic framework. The performance of the proposed method is demonstrated under various challenging environments using public benchmark dataset and our own dataset collected in an office environment. These conditions contain ambiguous areas, long corridor, moving people, viewpoint changes, or illumination variation. The proposed method is thoroughly evaluated against several strong state-of-the-art baselines. Experimental results prove the efficacy of our method.
Trade-Off Performances in Multiuser MIMO Networks with Quantized CSI Feedback

Multiuser multiple-input multiple-output transmission is identified as one of the promising techniques to vastly increase the transmission rate especially in densely deployed networks where inter-user interference is a major issue. To achieve this, channel state information (CSI) is needed to ensure accurate precoder design at the transmitter in order to effectively mitigate inter-user interference. However, the CSI is usually estimated at the receiver and, after quantization, is sent back to the transmitter. The feedback of quantized CSI incurs additional overhead which then leads to trade-off performances between transmission rate and energy efficiency. We will analyze this trade-off and propose the economy efficiency metric as a potential tool to determine the best operating point when the network is jointly optimized using transmission rate and energy efficiency.
DPSec: A blockchain-based data plane authentication protocol for SDNs

Software-Defined Networking (SDN) is a promising networking architecture that enables central management along with network programmability. However, SDN brings additional security threats due to untrusted control and data planes. In this work, we focus on authenticating SDN’s data plane since it can be exploited to attack SDN’s control plane. As a result, the whole SDN network will be paralysed. On the other hand, Blockchain (BC) can be utilized to provide more secure data plane by introducing a fault-tolerant, decentralized and secure ledger without relying on any trusted third-party intermediaries. To this end, in this work we propose, DPSec, a consortium BC-based protocol for authenticating SDN’s data plane including SDN switches and hosts. We also provide a proof-of-concept that demonstrates the applicability and feasibility of our protocol in SDNs. Finally, we present a security analysis that shows how DPSec can address several attacks against SDNs including CVE-2018-1000155 vulnerability [1] that targets SDN controllers due to the untrusted data plane.
Vegetation and Drought Trends in Sweden’s Mälardalen Region – Year-on-Year Comparison by Gaussian Process Regression

This article describes analytical work carried out in a pilot project for the Swedish Space Data Lab (SSDL), which focused on monitoring drought in the Mälardalen region in central Sweden. Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI) – commonly used to analyse drought – are estimated from Sentinel 2 satellite data and averaged over a selection of seven grassland areas of interest. To derive a complete time-series over a season that interpolates over days with missing data, we use Gaussian Process Regression, a technique from multivariate Bayesian analysis. The analysis show significant differences at 95% confidence for five out of seven areas when comparing the peak drought period in the dry year 2018 compared to the corresponding period in 2019. A cross-validation analysis indicates that the model parameter estimates are robust for temporal covariance structure (while inconclusive for the spatial dimensions). There were no signs of over-fitting when comparing in-sample and out-of-sample RMSE.
Unidirectional Plasmon Propagation in Metallic Nanowires

We report the observation of unidirectional plasmon propagation in metallic nanowires over distances >10 μm. Through control of the incident excitation wavelength and rod composition, we demonstrate the selective coupling of photons into the plasmon mode of a 20 nm diameter nanowire. This mode then propagates in a nonemissive fashion down the wire length before being emitted as an elastically scattered photon at the distal end. As expected from previous studies of plasmon excitation in nanoparticles and thin films, we observe a strong wavelength and material dependence of this phenomenon. This metal-dependent plasmon propagation is exploited to produce a wire through which plasmons propagate unidirectionally. A bimetallic wire with a sharp Au/Ag heterojunction is shown to display both wavelength dependence and unidirectionality with respect to plasmon propagation across the heterojunction. It is expected that these results will contribute to the growing interest in optical energy transport in molecular-le.
Copper wire based electrical contacts for direct interfacing of stretchable sensors

Stretchable sensors based on piezoresistive polymers enable new conformal and biocompatible wearables. However, physically interfacing such structures to create electrical contacts is still challenging. This is due to the localisation of stresses at the interface between soft polymers and metallic interconnections. Even if stretchable contacts or conditioning circuits are realised, there must be a rigid-soft interface to connect stretchable structures with conventional devices. Here, five ways to electrically connect soft Ecoflex based strain sensors are evaluated. The results show that a filament cable exhibits the most stable behaviour under cyclic loading of the contacts, while using a multi strand cable or a copper mesh results in lower contact resistance. In addition, loading forces from 0.27 N to 6.92 N are supported. Finally, stretchable piezoresistive strain sensors are fabricated using the 5 different contact methods. Employing customised interfaces with an optimal performance trade-off will help to establish reliable contacts with future stretchable electronics.
Detecting a Stealthy Attack in Distributed Control for Microgrids using Machine Learning Algorithms

With the increasing penetration of inverter-based distributed generators (DG) into low-voltage distribution microgrid systems, it is of great importance to guarantee their safe and reliable operations. These systems leverage communication networks to implement a distributed and cooperative control structure. However, the detection of stealthy attacks with a large impact and weak detection signals on such distributed control systems is rarely studied. In this paper, we address the problem of detecting a stealthy attack, named MaR, on the communication network of a microgrid while an attacker modifies the voltage measurement with the reference values. We collect datasets from a hardware platform modeled after a simplified microgrid and running the MaR attack performed with a Man-in-the-Middle (MitM) technique. We use the collected datasets to compare different attack detection algorithms based on multiple categories of machine learning algorithms. Our results show that the Random Forest algorithm outperforms the others to detect suspicious packets modified by a MitM attacker with an accuracy close to 97%.