Miracle-dz/UniFAR-FTUs huggingface dataset:
A 52-Gb/s Sub-1-pJ/bit PAM4 Receiver in 40-nm CMOS for Low-Power Interconnects

This article presents a quarter-rate source-synchronous PAM-4 receiver for energy-efficient chip-to-module communication. A novel single-stage multiple peaking continuous-time linear equalizer (MP-CTLE) using feedback enabled multiple peaking scheme for both high-frequency equalization (HF-EQ) and low-frequency equalization (LF-EQ) is proposed to improve the BER performance and overall energy efficiency. The LF-EQ of the MP-CTLE eliminates the need for many DFE taps in SR/VSR applications to save power and area. A 1-tap feedforward equalizer (FFE) is used to further compensate for the high-frequency loss. We also use a ring oscillator based wide bandwidth phase-locked loop (WBW-PLL) as the multiphase clock generator (MPCG) in the clock and data recovery loop to save power with acceptable phase accuracy. Fabricated in 40-nm CMOS technology, the prototype receiver chip achieves error-free operation up to 52 Gb/s PAM-4 with superior bit efficiency of 0.126pJ/bit/s/dB while compensating 7.3-dB channel loss at 13GHz. With the proposed single-stage MP-CTLE, the receiver extends the error-free operation from PRBS-7 to PRBS-9. The BER bathtub curve at $10^{-6}$ is improved from 0.018 UI to around 0.1 UI.
An Efficient and Light-weight Detector for Wine Bottle Defects*

Detecting defects on the wine bottle surface is a challenging task due to these factors: (1) tiny defects on the surface, (2) visually similar defects with design patterns on bottles, (3) reflective (metallic) bottle surface, (4) real-time requirement. In this paper, we propose an efficient and light-weight detector for the defects. To this end, we adopt EfficientNet- B3 [1] as the backbone of the detector and YOLO [2] as the detection head. In addition, we use channel pruning approach [3] to obtain a more compact model. In the experiments, we obtain a score of 0.77 on the validation dataset, similar performance comparing to the two-stage methods[23]. The parameters of our detector are only 5. $8\mathrm{M}$, which are about one tenth of the YOLO's [2]. The proposed detector achieves a good balance between speed and accuracy.
A Fast Approximation of the Nadaraya-Watson Regression with the k-Nearest Neighbor Crossover Kernel

For a system with inputs and outputs, a nonparametric regression has been proposed to clarify the relationship between inputs and outputs from a large amount of data. To improve estimation accuracy for the Nadaraya-Watson regression which is one of the nonparametric regressions, the regression with k-nearest neighbor crossover kernel, in which the kernel function by using neighborhood for each sample point in a sample set is made, is an effective method. However, there is a problem that the calculation time for estimation of this regression is very long, because it is needed to use all kernel functions made for all sample points. In this paper, we propose an estimation method with a fast approximation by using a few selected kernel functions instead of all kernel functions. These kernel functions are those made for only sample points included in neighborhood with the point that we want to estimate for. By this estimation method with a fast approximation, we show that the calculation time for estimation is short, and the estimation accuracy for the proposed method does not degrade, compared to that for a conventional estimation method without approximation. Moreover, we show theoretical aspects of the proposed approximation method for the regression with k-nearest neighbor crossover kernel.
Comparative Evaluation of Feature Descriptors Through Bag of Visual Features with Multilayer Perceptron on Embedded GPU System

Loop Closure Detection (LCD) is one of the most significant steps for a Simultaneous Localization and Mapping (SLAM) system of an autonomous robot. Many of the state-of-the-art techniques for the LCD problem are based on handcrafted resources and Bag of Words (BoW). Even with advances in Machine Learning (ML) area, Deep Learning (DL) methods and Convolutional Neural Network (CNN) are not fully explored in the LCD problem context. We addressed the LCD problem proposing a hybrid Deep Neural Network (DNN) architecture for deployment on an embedded GPU system: NVIDIA's Jetson Nano. The main idea of this work consists of reformulating convolutional filters of a CNN through Local Binary Descriptors to produce a sparse model as an efficient alternative to traditional CNNs. This paper discusses the evaluation of the Bag of Visual Features (BoVF) approach, extracting features through Local Feature Descriptors such as SIFT, SURF, and KAZE and Local Binary Descriptors such as BRIEF, ORB, BRISK, AKAZE, and FREAK were evaluated for the recognition and classification steps. For this task, we used six visual datasets (MNIST, JAFFE, Extended CK+, FEI, CIFAR-10, and FER-2013) through Multilayer Perceptron (MLP) classifier. Experimentally, we demonstrated the feasibility to produce promising results combining BoVf with MLP classifier. Therefore, empirically, we can assume that the computed descriptors generated by a Local Binary Descriptor alongside a hybrid DNN architecture, can accomplish satisfactory results addressed in future work in the Feature Detection step, and reformulated in the Feature Extraction step into convolutional filters of CNN architecture of the proposed system.
Transmission Lines-Based Impedance Matching Technique for Broadband Rectifier

A high-efficiency compact broadband rectifier is developed for wireless energy harvesting. A novel three-stage impedance matching technique is utilized in a broadband rectifier design to achieve high conversion efficiency with a compact size. In the rectifier, a low loss impedance matching technique is initiated by employing a linearly tapered transmission line for controlling the impedance curve at the required input power level, followed by two stages to make a circular impedance curve for wideband impedance matching. Both theoretical analysis and numerical simulations of the proposed rectifier are performed. For validation, a prototype is fabricated and demonstrated a broadband performance with a fractional bandwidth (FBW) of 74% (from 1.12 to 2.43 GHz) and power conversion efficiency of more than 50% at a 5 dBm input power level. Moreover, the rectifier can reach an efficiency of more than 50% extending from 0.97 to 2.55 GHz (FBW = 90%) at the input power of 10 dBm, which is the highest bandwidth reported under this condition. This low complexity design is suitable for realizing broadband rectifiers for wireless energy harvesting (WEH) applications.
Transparent and conductive undoped zinc oxide thin films grown by atomic layer deposition

Atomic layer deposition (ALD) was used to fabricate transparent and conductive thin films of ZnO. Two hundred‐nano metre thick ZnO films were deposited on glass substrates at low growth temperatures varied between 120 and 240 °C. As zinc and oxygen precursors we used diethylzinc (DEZn) and deionized water, respectively. To find optimal film parameters, the structure, surface morphology, optical and electrical measurements were carried on. The films obtained at 200 °C show the highest carrier concentration (∼1020 cm−3) and the lowest resistivity (2 × 10−3 Ω cm). The films exhibit mobilities up to 37 cm2/Vs that we associate to the process technology used. An important point of our approach was that the films studied were not intentionally doped (with Al or other group III elements) but the high electrical conductivity was achieved by playing with the sample stoichiometry and growth conditions.
Tunable Balanced Power Dividers: An Overview of Recently Developed Balanced Power Dividers and Couplers With Fixed and Tunable Functions

Signal crosstalk and common-mode noise are two issues for engineers when designing today's increasingly complicated microwave integrated circuits [1]. Compared with singleended circuits, balanced circuits can provide high immunity to environmental noise and broadband common-mode rejection capability [2]. Over the past few years, various balanced passive circuits, such as filters [3], antennas [4], power dividers, and couplers [5]-[17], have been extensively studied. Power dividers and couplers are widely used in microwave integrated circuits, such as antenna feeding networks, balanced mixers, and amplifiers. Balanced couplers are used in doublebalanced mixers [18] and balanced frequency doublers [19] to achieve higher efficiency and lower loss.
Joint Embedding of Words and Labels for Sentiment Classification

Due to the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to traditional methods. However, word embeddings always ignore information from dataset’s labels. Inspired by LEAM model proposed by Wang [1], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on Yelp dataset, which reached 64.99% accuracy.
Vehicle Localization in Six Degrees of Freedom for Augmented Reality

Augmented reality visualizes map content or sensor observations by projections that fit naturally into the surroundings-thereby providing relevant information in a convenient and safe way for the customer. A stable projection of map data into the driver's field of view requires precise knowledge of the vehicle's pose in six degrees of freedom (dof) with respect to a map. We developed an efficient 6dof localization based on odometry, camera recognized landmarks and a high definition (HD) map using an extended Kalman filter (EKF). As a result, we compared the accuracy of two fusion methods on simulated driving scenarios based on real HD map data: (i) fusing 2D landmarks from the image plane directly, and (ii) fusing 3D landmarks assuming prior 3D reconstruction. Using five types of traffic signs and pole-like landmarks, we found that the accuracy difference between the approaches reduces with increasing number of landmarks per cycle. In presence of five or more landmarks per cycle, the 2D measurement approach reaches the accuracy of the 3D measurement approach. Thus, with many landmark types, the 2D approach becomes a beneficial alternative: While achieving comparable accuracy, it circumvents 3D reconstruction, thereby reducing the computational effort, which is highly desirable in autonomous driving.
Energy Trading Based on Smart Contract Blockchain Application

Energy and clean energy are big concerns and interests. As the needs differ from area to another, different solutions appear. Energy cost, availability, reliability and trading rules are important keys in energy market. Energy sharing is a hot topic as a consumer being a part of the sustainable distributed system also making benefits such as Prosumer. Blockchain technology provides more secure, distributed and fast way to transact financial payments between clients. This paper provide a simulation case for energy sharing concept using smart contract as a tool to rule the sharing process on isolated network to obtain the optimum performance and efficiency of the service. Creating a base for more conditions and complex scenarios and linking the sharing process to financial transactions.
Substrate factors that influence the synergistic interaction of AA9 and cellulases during the enzymatic hydrolysis of biomass

The polysaccharide monooxygenase enzyme AA9 (formerly known as GH61) was shown to interact synergistically with cellulases to enhance the enzymatic hydrolysis of a range of “commercially-relevant” pretreated and “model” cellulosic substrates. Although an exogenous source of reducing power was required when AA9 was added with cellulases to a “pure” cellulosic substrate, it was not required when added to pretreated lignocellulosic substrates. It appears that the non-cellulosic components such as soluble components, lignin, and possibly hemicellulose, can all act as AA9 reducing cofactor. Of the various substrate characteristics that influenced the efficacy of the enzyme mixture, the relative amount of accessible crystalline cellulose, assessed by the specific cellulose binding module (CBM), appeared to be the most critical. Cellulases and AA9 acted synergistically when hydrolysing cellulose I but it did not occur during the hydrolysis of cellulose II and III.
Multi-step Spatio-Temporal Temperature Forecasting

Atmospheric analyses and simulations requires high resolution physical models on super computers that consume many hours of computations. Deep learning and machine learning methods used in forecasting revealed new solutions in this area. Main goal of this paper to solve high-resolution numeric forecasting problem. We present a model architecture for spatiotemporal prediction. Model is composed of Convolutional Longshort Term Memory (Conv-LSTM) units with encoder-decoder structure. Model takes sequence of inputs and outputs the next time sequence. Model is trained in a supervised manner. Experiments are made on high-scale benchmark numerical weather dataset. All selected baseline models are surpassed by 3°C mean square error (MSE) with statistically signiﬁcant results. Both spatial and temporal changes in the temperature is captured. Model forecasted 5 time steps with 1, 3 and 24 time difference successfully.
Silicon Hall-effect Multisensor

A novel in-plane sensitive Hall-effect multisensor is presented, in the active transducer region of which, two Hall voltages are generated independently. This is achieved by the original design and by the supply formed by two constant currents. The residual offset drift is about 140 times smaller than the drift of a single Hall device. The magnetosensitivity reaches $\mathrm{S}_{\mathrm{RI}}\approx170\mathrm{V}/\mathrm{AT}$. The signal-to-noise ratio is increased, the measured minimal magnetic induction is $B_{\min}\approx 9\mu \mathrm{T}$. These characteristics will have impact on quantum communication, and medicine.
Domain Adaptation for Degraded Remote Scene Classification

Remote scene classification serves a vital role in many applications. However, satellite images are often blurred and degraded due to aerosol scattering under fog, haze, and other weather conditions, reducing the image contrast and color fidelity. State-of-the-art remote sensing classification models building upon convolutional neural networks (CNNs) are mostly trained on annotated datasets of clear satellite images. When applied to blurred images, they will suffer a great degradation in performance. To address this problem, we adopt the domain adaptation algorithm TADA and propose Transferable Attention enhanced Adversarial Adaptation Network (TA3N), which utilizes annotated data in clear images by applying knowledge transferring from clear image domain to blurred image domain. Our TA3N first integrates spatial attention to focus on salient areas which are discriminative and transferable. In addition, domain discriminator and adversarial training via gradient reversal layer are used to minimize the discrepancies in extracted features from clear and degraded domains. We synthesize degraded remote scene classification dataset SSI based on FoHIS model. Experiments on degraded SSI showed that TA3N significantly outperforms baseline and other state-of-the-art domain adaptation methods.
Data Augmentation with Generative Adversarial Networks for Grocery Product Image Recognition

Image recognition tasks have gained enormous progress with a tremendous amount of training data. However, it isn't easy to obtain such training datasets that contain numerous annotated images in the domain of grocery product recognition. A small number of training data always results in a less than stellar recognition accuracy. Here we attempt to address this challenge by using generative adversarial networks (GAN), which can generate natural images for data augmentation. This paper aims to investigate the feasibility of using GAN to create synthetic training data, and thus to improve grocery product recognition accuracy. In this work, different GAN variants and image rotation are employed to enlarge the fruit datasets. Then, we train the CNN classifier using different data augmentation methods and compare the top-1 accuracy results. Finally, our experiments demonstrate that Auxiliary Classifier GAN (ACGAN) has achieved the best performance, which obtains l.26%~3.44% increase in recognition accuracy. As an additional contribution, the results show that the effectiveness of using generated data is very close to that of using real data, which in our best experimental case, are 93.85% and 94.25%, respectively.
Deep Learning Based Surface EMG Hand Gesture Classification for Low-Cost Myoelectric Prosthetic Hand

In this paper, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository, and UCI Machine Learning Repository, is shown. Applying transfer learning and CNN-LSTM neural network architectures, we find out a suitable control scheme for a myoelectric prosthetic hand (we mention it as DUFAB Hand). At first, the continuous wavelet transform (CWT) is exploited to create images from the sEMG signal, which serves as a powerful feature for the classification of different hand gestures. Then, we transferred the learning of various neural nets of image classification, e.g., AlexNet, and ResNet-18 to the sEMG image classification. Application of these deep neural networks outperformed general machine learning techniques with higher accuracy and performance. For example, the combination of CNN and LSTM has achieved the state of the art accuracies for these data sets, of 99.72% for UCI Machine Learning Repository and 99.83% for Rami Khusaba EMG repository respectively. The main contribution of this paper is, establishing an algorithmic pipeline using continuous wavelet transform (CWT) and CNN-LSTM deep neural networks to achieve high accuracy in two sEMG datasets.
Suitability of Single-channel Acoustic Myography for Classification of Individual Finger Movements

Acoustic Myogram (AMG) is the vibration or sound signal produced during muscle contraction and relaxation. A simple system like a condenser microphone is enough to capture an AMG signal from muscles, unlike complex systems that are used in surface Electromyography (sEMG). Moreover, AMG signal is not highly sensitive to sensor placement like sEMG signal. Therefore, Acoustic Myography is a potential research area to find an alternative to complex and bulky sEMG system. This work focuses on verifying the suitability of single-channel AMG for the classification of individual five finger movements. AMG data were recorded from 14 subjects at two different sites on hand, namely forearm muscle and wrist. Temporal, Spectral, and Cepstral features were extracted from the collected data after required pre-processing. Two Machine learning algorithms: Support Vector Machine and K-Nearest Neighbors were applied to classify the features. From this analysis, three main outcomes were achieved: Independent five finger movements cannot be differentiated precisely using single-channel AMG data solely, whether they are from the forearm or wrist. Class reduction and grouping of some fingers increase the classification accuracy, which infers that vibrations due to different finger movements have very similar attributes. AMG from both forearm and wrist yielded similar classification accuracy, with no evidence of a site being significantly better. Contribution: This research reveals the competence of single-channel Acoustic Myography (AMG) in classifying individual hand finger movements.
Effect of Hydrogen on Reactive Sputtering of Transparent Oxide Films

The article reports on the effect of addition of H2 into a mixture of Ar + O2 on the process of dc reactive magnetron sputtering of oxides and the electrical conductivity of transparent oxide films. Four systems were investigated: (i) InSnO (ITO), (ii) TiO2, (iii) TiYO, and (iv) ZrO2 films. It was found that the addition of H2 into Ar + O2 sputtering gas mixture results in: (i) decrease in the magnetron discharge voltage Ud, (ii) strong change of the dependence , and (iii) dramatic decrease of the electrical resistivity of sputtered transparent oxide films; here and are the partial pressure and flow rate of oxygen, respectively. These results are described in detail.
Multi-Image Crowd Density Estimation using Multi Column Deep Neural Network

—Crowd density estimation is a challenging problem for security applications which is a regression task consisting of feature extraction and regression steps. In this paper, multi-column deep neural networks (MDNN) is proposed for crowd density estimation. Regression task is performed using the feature vector obtained from MDNN. 2000 images selected from the UCSD pedestrian dataset are used in the experiments. In the images, the region of interest (ROI) is ﬁltered and the remaining parts are removed. In order to avoid distortions due to camera position, perspective normalization is applied as a pre-processing step which yields considerable performance improvement.
Cluster Analysis of Vehicle Measurement Data for Improved Understanding of Behavior in Urban and Highway Environments

The comprehension of the behavior of objects in the environment of an automated vehicle is essential information to be extracted out of traffic measurement data. It can be made accessible by applying a data description process consisting of a cluster analysis and subsequent classification step. In this paper, two different algorithms, TRACLUS and Self-Organizing Maps are utilized for a clustering of laser scanner measurement data, recorded by experimental vehicles in real traffic. The results are evaluated and discussed. A remaining open issue of a cluster analysis is the necessity for a classification of clusters, which is missing or solved through manual solutions in related work. We address the matter with a novel approach for an automated classification of clusters using reference trajectories, which is successfully validated for a first exemplary maneuver on highway measurement data.
Istatistiksel Yöntemler ile DDoS Saldırı Tespiti DDoS Detection Using Statistical Methods

The Distributed Denial of Service attack is an attempt to make an online service unavailable by consuming resources of the target system such as bandwidth, memory, or processor. Large businesses, such as Internet service providers lack a network-based, simple but effective detection method for lowdensity (DDoS) attacks, which is still an essential problem for computer networks. DDoS attacks cause a variety of changes in the statistical distribution of attributes in the network traffic. Using the advantage of statistical changes in traffic features, this study proposes two simple but effective network-based DDoS attack detection method based on statistical signal processing approach. The experimental results show that the proposed approach achieve %98 true positive rate and %0.34 false positive rate.
Critical-point equations for polydisperse polymer solutions

Based on the generalized Flory-Huggins concept replacing Huggins’s χ-term by a function depending on a finite number of moments of the molar mass distribution, a simple form of the necessary conditions for the critical state of a polydisperse polymer solution is established in the framework of classical Gibbs theory. The derived critical-point equations permit a substantial diminution of the order of the corresponding determinants.