Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of MACE Parameters on Electrical and Optical Properties of ALD Passivated Black Silicon

Metal-assisted chemical etching (MACE) enables efficient texturing of diamond-wire sawn multicrystalline silicon wafers. However, the excellent optics are often sacrificed by polishing the surface to achieve better surface passivation with chemical-vapor-deposited (CVD) silicon nitride (SiN<italic><sub>x</sub></italic>). In this paper, we show that a polishing step is not required when CVD SiN<italic><sub>x</sub></italic> is replaced with atomic-layer-deposited (ALD) aluminum oxide (Al<sub>2</sub>O<sub>3</sub>). Indeed, while polishing increases reflectance, it has in general only very modest effect on surface recombination velocity of ALD-passivated b-Si. Furthermore, since ALD Al<sub>2</sub>O<sub>3</sub> is compatible with various surface morphologies due to its excellent conformality, the MACE parameters can be more freely adjusted. First, the concentration of silver nitrate (AgNO<sub>3</sub>) in AgNO<sub>3</sub>/H<sub>2</sub>O solution that is used to deposit Ag nanoparticles is shown to affect the final b-Si morphology. Instead of needle-shaped b-Si produced by 5 mmol/L AgNO<sub>3</sub> concentration, two orders of magnitude lower AgNO<sub>3</sub> concentration produces porous structures, which are more challenging to passivate. Additionally, we demonstrate that a separate Ag nanoparticle removal step in nitric acid (HNO<sub>3</sub>) is not a prerequisite for high carrier lifetime. Instead, Ag nanoparticles present during polishing in a HF/HNO<sub>3</sub>/H<sub>2</sub>O solution affect the final b-Si morphology by accelerating the etching of Si. The results demonstrate that no tradeoffs are necessary between optical and electrical properties of MACE b-Si when using ALD.
Evaluation of the Likelihood of Friend Request Acceptance in Online Social Networks

Recent years online social networks (OSNs) have become an essential digital platform in the daily life of billions of earth inhabitants. Despite the advantages of easy communication and information sharing, OSNs users often fell in trouble causing by security breaches and violations. A recurring example of the troubles arises due to a rash acceptance of the friendship request, which can lead to the disclosure of personal information and vulnerability to an attack. Support for making a secure friendship decision is limited in the modern OSNs, making their use hazardous especially for the groups of children and young people. To overcome this issue, the paper proposes a method for evaluating the likelihood to become a friend in support of promoting hazard-free cyber environments. The proposed approach allows a user to define a model of a friend-to-be, and incoming friend requests are evaluated with reference to this model. The model takes into account the attributes (like common interests) and the behavioral properties of a friend-to-be (like frequency of the posts). The method allows for filtering friend requests to the given users and triggering notification of anomalous behaviors in an OSN. An empirical study proves the validity of the proposed model and its favorable characteristics against existing methods in the current OSN platforms.
One-shot signal detection in asynchronous CDMA systems

One-shot multiuser detectors in asynchronous CDMA (code division multiple access) systems are studied. This type of detector is shown to be near-far resistant, although only parts of the interfering symbols are estimated to conduct the multiuser detector for the desired signal. Performance of the one-shot detectors usually depends upon the delay distribution of users, which may result in some performance variation. However, by proper design of the PN (pseudonoise) sequences and the number of users in a system, the variation can be limited within a very small range. <<ETX>>
Glide-symmetric printed corrugated transmission lines with controlable stopband

Here, we demonstrate that the dispersion properties of printed lines can be controlled by using glide symmetry. Glide symmetry is introduced by means of corrugations in the printed lines. The glide-symmetric configuration provides a more linear propagation constant, avoiding the presence of stopband between first and second propagating modes. Additionally, the breakage of the glide-symmetric geometry introduces a tunable stopband that can be used for filtering.
Dialogue Act Classification Based on Intra-Utterance Features∗

We present recent work in the area of Dialogue Act (DA) tagging. Identifying the dialogue acts of utterances is recognised as an important step towards understanding the content and nature of what speakers say. Our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features — principally involving word n-gram cue phrases. Such a classifier performs surprisingly well, rivalling scores obtained using far more sophisticated language modelling techniques for the corpus we address. We also discuss the potential utility of classifiers that identify the n most likely dialogue acts for each utterance, leaving it to some later process to choose amongst these alternatives.
Trajectory control of an articulated vehicle with triple trailers

Presents backing up control for a vehicle with triple trailers. In particular, we consider constraints on input and output and disturbance rejection that are incorporated in the LMI conditions. In application to the truck with triple trailers setup, we utilize these LMI conditions to explicitly avoid the saturation of the steering angle and the jack-knife phenomenon in the control design. The simulation and experimental results demonstrate that the controller effectively achieves the backing up control of the vehicle with triple trailers while avoiding the saturation of the actuator and "jack-knife" phenomenon. Moreover, the feedback controller guarantees the stability and performance even for disturbance.
FPGA Architecture Enhancements for Efficient BNN Implementation

Binarized neural networks (BNNs) are ultra-reduced precision neural networks, having weights and activations restricted to single-bit values. BNN computations operate on bitwise data, making them particularly amenable to hardware implementation. In this paper, we first analyze BNN implementations on contemporary commercial 20nm FPGAs. We then propose two lightweight architectural changes that significantly improve the logic density of FPGA BNN implementations. The changes involve incorporating additional carry-chain circuitry into logic elements, where the additional circuitry is connected in a specific way to benefit BNN computations. The architectural changes are evaluated in the context of state-of-the-art Intel and Xilinx FPGAs and shown to provide over 2x area reduction in the key BNN computational task (the XNOR-popcount sub-circuit), at a modest performance cost of less than 2%.
ENTT: A Family of Emerging NVM-based Trojan Triggers

Hardware Trojans in the form of malicious modifications during the design and/or the fabrication process is a security concern due to globalization of the semiconductor production process. A Trojan is designed to evade structural and functional testing and trigger under certain conditions (e.g., after a number of clock ticks or assertion of a rare net) and deliver the payload (e.g., denial-of-service, information leakage). A wide variety of logic Trojans (both triggers and payloads) have been identified, however, very limited literature exists on memory Trojans in spite of their high likelihood. Emerging Non-Volatile Memories (NVMs) e.g., Resistive RAM (RRAM) possess unique characteristics e.g., non-volatility and gradual drift in resistance with pulsing voltage that make them a prime target to deploy a Hardware Trojan. In this paper, we present a delay and voltage-based Trojan trigger by exploiting the RRAM resistance drift under pulsing current. Simulation results indicate that these triggers can be activated by accessing a pre-selected address 2500–3000 times (varies with trigger designs) since the proposed trigger requires a large number of hammerings to evade test phase. Due to non-volatility, the hammering need not be consecutive and therefore can evade system-level techniques that can classify hammering as a potential security threat. We also propose a mechanism to reset the triggers. The maximum area and static/dynamic power overheads of the trigger circuit are 6. 68μm2 and 104. 24μW/0. 426μW, respectively in PTM 65nm technology.
Implementing NEF Neural Networks on Embedded FPGAs

Low-power, high-speed neural networks are critical for providing deployable embedded AI applications at the edge. We describe an FPGA implementation of Neural Engineering Framework (NEF) networks with online learning that outperforms mobile GPU implementations by an order of magnitude or more. Specifically, we provide an embedded Python-capable PYNQ FPGA implementation supported with a High-Level Synthesis (HLS) workflow that allows sub-millisecond implementation of adaptive neural networks with low-latency, direct I/O access to the physical world. We tune the precision of the different intermediate variables in the code to achieve competitive absolute accuracy against slower and larger floating-point reference designs. The online learning component of the neural network exploits immediate feedback to adjust the network weights to best support a given arithmetic precision. As the space of possible design configurations of such networks is vast and is subject to a target accuracy constraint, we use the Hyperopt hyper-parameter tuning tool instead of manual search to find Pareto optimal designs. Specifically, we are able to generate the optimized designs in under 500 iterations of Vivado HLS before running the complete Vivado place-and-route phase on that subset. For neural network populations of 64-4096 neurons and 1-8 representational dimensions our optimized FPGA implementation generated by Hyperopt has a speedup of 10-484× over a competing cuBLAS implementation on the Jetson TX1 GPU while using 2.4-9.5× less power. Our speedups are a result of HLS-specific reformulation (15× improvement), precision adaptation (4× improvement), and low-latency direct I/O access (1000× improvement).
The Effectiveness of Collaborative Strategic Reading (CSR) for Teaching Reading Comprehension at Muhammadiyah University of Sidoarjo

The arrival of digital technology has done much to change the way in which learning is displayed, shared, and analyzed in multimedia, as well as hypermedia environments. The adoption level of emerging web technologies is on the rise in academic settings. Students become more technologically literate and the trends of writing are shifting from paperbased texts to screen based texts. This paper first points out the teaching of writing in the digital age, with the integration of Information and Communication Technologies where Weblog is used as a promising tool. Toward this end, this paper further explains briefly about Weblog in writing course, its features and types, and technical matters concerning how to use weblog for writing instruction. The advantages of using Weblog are also described in this paper, carrying out the idea of utilizing Weblog in the writing subjects in the higher level of education.
Study on the satellite and ground based aerosol measurements over Himalayan region

In this study, the evaluation of satellite retrievals with ground based aerosol measurements and a case study on hygroscopic aerosol transport is discussed. Latest aerosol product levels 1B and 2 (version 4.10) of Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) are evaluated with its earlier versions at a regional scale. A marked improvement is observed in the surface detection and tropospheric aerosol classification over the complex mountainous terrain in the latest version. A typical case on the measurements from ground-based sensors, in conjunction with CALIPSO, and reanalysis products is also discussed.
Ramp Input Response of RC Tree Networks

Closed form expressions are presented to accuratelydescribe the delay characteristics of RC tree networks.The Penfield-Rubinstein-Horowitz approach to estimating the stepfunction response of RC trees has been extendedto consider ramp inputs. This result improves timing accuracyby considering the shape of the input waveform driving each individualinterconnect tree while maintaining computational simplicityfor use in the automated timing analysis of complex VLSI circuits.
Multipath Propagation Characteristics for 5G Vehicular Communications Based on 28 GHz Expressway Measurements

This paper presents multipath propagation characteristics, such as a power delay-Doppler profile, based on measurement data collected in expressway environments considering millimeter-wave 5G vehicle-to-infrastructure communications. With a 500 MHz-channel sounder at 28 GHz, the measurement campaigns were conducted in two expressway roads: a test expressway and an actual inter-state expressway, which are located parallel to each other in Yeoju, Korea. We investigate the propagation characteristics in terms of delay spread and Doppler frequency expressway for vehicle-to-infrastructure channels. Characteristics such as delay and Doppler frequency of the two expressway roads are compared. The results show that higher Doppler frequency is observed in the actual expressway environment, which is about 3 times higher than that of the line-of-sight component.
Clinical Study with a Time-Domain Microwave Breast Monitor: Analysis of the System Response and Patient Attributes

This paper presents statistical results of a clinical trial with a multistatic time-domain microwave breast health monitor. The wearable prototype was tested on patients that reported a previous abnormality on a mammogram or MRI. Within this study, we investigate the correlation between system response and patient specific data like breast size, breast density, and age. The goal is to determine if certain patient attributes result in a specific signal response, thereby identifying avenues for optimization of the overall prototype, all with the aim of improving signal quality.
Resource Allocation for Performance Enhancement in Mobile Ad Hoc Networks

The low-cost and easy-deployable mobile ad hoc networks (MANETs) have great potential to serve as the underlay network architecture for implementing lots of critical applications. In order to support different demands in such applications, understanding the fundamental performance of the MANETs and exploring their performance enhancement are of great importance. To this end, this paper, for the first time, investigates the performance enhancement of the MANETs by appropriately allocating the network resource. Specifically, we consider both the transmission resource and storage resource of a network node and use a two-tuple to depict a general resource allocation configuration. First, for a MANET with any given resource allocation configuration, we establish an analytical framework to model the network dynamics as queuing processes. With the help of the network modeling, we then derive the expressions of fundamental performance metrics, including per node throughput, expected an end-to-end delay, and throughput capacity. Based on these results, we further develop optimal resource allocation algorithms to determine the optimal setting of resource allocation, for enhancing network performance. Finally, we provide extensive simulation and numerical results to verify the efficiency of our theoretical modeling and illustrate the effects of resource allocation on network performance.
Heterogeneous Face Recognition Based on Multiple Deep Networks With Scatter Loss and Diversity Combination

Due to the gap between sensing patterns of different domains and a lack of sufficient training sample, heterogeneous face recognition (HFR) is still a challenging issue in the computer vision community. In this paper, we propose a novel method called multiple deep networks with scatter loss and diversity combination (MDNDC) for solving the HFR problem. As we know, the performance of deep models is affected by data, network structure, and loss function, so we devote much effort to improve the HFR performance from all these three aspects. First, to reduce the intra-class variations and increase the inter-class variations, the scatter loss (SL) is used as an objective function that can bridge the modality gap while preserving the identity information. Second, we design a multiple deep networks (MDN) structure for feature extraction and propose a joint decision strategy called diversity combination (DC) to adaptively adjust the weights of each deep network and make a joint classification decision. Finally, instead of using only one publicly available dataset, we make full use of multiple datasets to train the networks, which can further improve the HFR performance. The extensive experiments are carried out on two challenging NIR-VIS HFR datasets, CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Forecasting Abilities of MIMO and SISO Neural Networks: A Comparative Study using Telecommunication Traffic Data

The study compared the forecasting performance of two multiple-input and multiple-output (MIMO) and two single-input and single-output (SISO) neural networks using 4G network traffic data aggregated into daily, weekly and monthly time spans. To explore the best configuration of SISO and MIMO neural networks, the empirical traffic data of 1-input, 2- input and 3-input were used together with varying the parameters of the models. The study concluded that for 2-input, MIMO Radial basis function neural (RBFN) network performed better than the 2-input MIMO Multilayer perceptron (MLP) neural network in predicting the traffic data. In the case of 3-input, MLP network was found to be more efficient than RBFN network. In the scenario of SISO architecture, the MLP network outperformed the RBFN network for 4G daily, weekly and monthly traffic data.
Training Neural Networks using Memristive Devices with Nonlinear Accumulative Behavior

Memristive devices when organized in crossbar arrays can be used to accelerate the training of deep neural networks through in-memory computing. In this paper, we propose a scheme that addresses a key challenge, namely, the non-linear accumulative behavior of memristive devices. Data entering and leaving the memristive crossbar array is pre- and post-processed to change the effective mapping of weight matrices onto the conductance matrix. The impact of the proposed scheme is studied for training neural networks using phase-change memory (PCM) devices as synaptic elements. The scheme is shown to significantly improve the network's classification accuracy, allowing us to reach the performance of an ideal linear device.
MBIC: A Novel Influence Propagation Model for Membership-Based Influence Maximization in Social Networks

Influence maximization is the problem of finding a certain amount of seed nodes that can trigger the largest expected number of remaining nodes under a pre-defined influence propagation model. However, most studies in this field apply online social network analysis methods mechanically and take commodity marketing in a broad sense as the research background, whose models are relatively simple, lacking characterization of specific business situation. Membership is a kind of classical marketing method. By selling membership, the company provides differentiated services to members and ordinary users, stimulating the former to consume. In this paper, we focus on the membership business model and study the membership-based influence maximization problem. First, due to the models used by predecessors failing in meeting the particularity of the membership, a novel influence propagation model membership-based influence cascade (MBIC) is proposed. According to the characteristics of membership, the MBIC model divides the influence propagation process into two stages, the influence stage and the reference stage. At the same time, the concepts of activity and intimacy are introduced to better model real social networks. Then, we propose the influence-reference rank (MBIC) algorithm that quantifies the ability of nodes at each stage in the MBIC to solve the membership-based influence maximization problem. Finally, experiments using real-world dynamic social networks with up to 1.4 million edges are conducted. The experimental results show that the influence-reference rank (IRR) achieves a better performance than several alternative algorithms under acceptable complexity conditions under MBIC model.
Improving Parkinson's Disease Diagnosis with Machine Learning Methods

Parkinson's disease is a type of disease caused by the loss of dopamine-producing cells in the brain. As the amount of dopamine decreases, the symptoms of Parkinson's disease emerge. Parkinson's disease is a slow-developing disease, and symptoms such as hands, arms, legs, chin and face tremors are increasing over time. As the disease progresses, people may have difficulty in walking and speaking. There is no definitive treatment for Parkinson's disease; however, with the help of some drugs, the symptoms of the disease can be reduced. Although there is no definitive treatment for Parkinson's disease, the patient can continue his normal life by controlling the problems caused by the disease. At this point, it is important to prevent early detection and progression of the disease. In this study, different types of classification methods such as Logistic regression, Support Vector Machine, Extra Trees, Gradient Boosting and Random Forest are compared in order to predict Parkinson's disease. A total of 1208 speech data sets consisting of 26 features obtained from Parkinson's patients and non-patients were used in the classification stage. The feature space of the dataset is expanded due to correlation maps. These correlation maps are constructed with the features which are obtained by using Principal Component Analysis (PCA), Information Gain (IG) and all features respectively. It is concluded that, classification results which are attained with expanded features outperform the classification results attained with the original features of the data.
Improving the Diagnostic Capability of Microwave Radar Imaging Systems using Machine Learning

Breast microwave sensing (BMS) is a potential breast cancer detection technique that uses low-power microwave radiation to detect the presence of cancerous lesions. This work presents the results of the application of a multilayer perceptron (MLP) and support vector machine with radial basis function (SVM RBF) to breast cancer detection for a portable BMS prototype. Numerical 2D phantoms belonging to either BI-RADS Class 1 or Class 2 classifications were used to produce simulated data as collected by the portable system using an array of twelve sensors operating at five frequencies between 2.3 GHz and 6.5 GHz. Five feature preprocessing pipelines and their impact on classification performance were evaluated. An area under the curve of the receiver operating curve (ROC AUC) as high as (95 ± 1)% for BI-RADS Class 1 and as high as (94 ± 1)% for BI-RADS Class 2 were obtained using the SVM RBF, and as high as (94 ± 1)% for Class 1 and (92 ± 2)% for Class 2 using the MLP.