Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Convolutional Neural Network Learning Transfer to SVM-Based Segmentation Method for Brain Tumor

Brain tumor segmentation plays an important role in assisting diagnosis, planning treatment and surgical navigation. In this paper, we propose a convolutional neural network-based learning transfer to support vector machine classifier method for brain tumor segmentation. Our algorithm is composed of two cascaded stages. In the first stage, we trained CNN to learn the mapping from the image space to the tumor label space. During the testing phase, we used the predicted label output from CNN and sent it along with the testing image to an SVM classifier for accurate segmentation. Then we iterate our deep CNN-SVM classifier. Experiments and comparisons demonstrate that the proposed framework is better than the separate SVM-based segmentation or CNN-based segmentation.
Uplink Performance Analysis for Massive MIMO Linear Processing

This paper considers an uplink massive MIMO system, in which the base station (BS) is equipped with a very large antenna array to serve multiple users simultaneously in the presence of out-of-cell interferers. Multi-cell minimum mean-square-error (M-MMSE) detection is used to mitigate co-channel interference (CCI) and multipath fading effects. However, this receiver processing brings high computational complexity to bear in a real-time massive MIMO scenario. To overcome this higher complexity, novel two-layer linear receiver processing schemes are proposed in this work, which can achieve a good trade-off between performance and complexity. The proposed architecture consists of two layers: 1) splitting the antenna array into a number of subsets, and performing M-MMSE processing at the subset level; 2) combining the resulting subset outputs using either MRC or M-MMSE detectors. Taking into account both small- and large-scale fading, we investigate the system performance and the computational complexity of the proposed receivers. To further characterize the advantages of the proposed schemes, they are compared with conventional detectors. Numerical simulations show that the proposed schemes approach the performance of conventional M-MMSE processing, albeit with significantly reduced complexity. We also derived tight expressions for intra-cell and inter-cell residual interference powers at the output of the first processing layer. An important observation is that the inter-cell interference dominates the total interference, especially when shadowing is strong or the subset size is comparable to the total number of users. However, performing M-MMSE at the second processing layer provides significant gains in this context.
3D Digital Reconstruction of Heritage Artifacts: Parametric Evaluation

Preservation of cultural heritage artifacts using 3D digitization and modeling techniques is gaining momentum in the past few years. This paper presents an approach to digitize and reconstruct partially damaged artifacts. The digitization process begins with the selection of damaged artifacts by field survey followed by photogrammetry to acquire 2D points. Parameters such as number of images, image resolution and the cause of defect holes are evaluated and their influence with 3D reconstructed images are discussed. Autodesk Recap® and Blender® software® are used to generate 3D point clouds and to reconstruct damaged artifacts respectively. Experimental investigation is carried out to validate the evaluation parameters. The results obtained for digital structures based on distance measure and volume estimation methods have been compared with physical measurement. This is a preliminary investigation performed to automate the digital reconstruction of heritage artifacts.
Adversarial Robustness by One Bit Double Quantization for Visual Classification

In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images are injected with adversarial noise. The proposed system can remove the adversarial noise completely on this particular attack scenario. First, we analyze the potential sources of adversarial noise and classify adversarial examples into three classes. We then propose a novel effective solution, dquant, to target a specific class of adversarial examples. The first quantization is 1–bit dithering applied to both training and test images. The second one is linear quantization, which is applied to test images just before being inputted to a model to remove any adversarial noise. The linear quantizer guarantees that original 1–bit test images will be restored regardless of adversarial noise distance, and, therefore, dquant maintains identical accuracy whether or not the model is under attack. The results show that dquant achieves comparable accuracy, 85.28% on the CIFAR-10 and 94.99% on the Oxford-IIIT Pet datasets against three state-of-the-art adversaries with even a previously untested maximum adversarial distance of 64.
Differences in Android Behavior Between Real Device and Emulator: A Malware Detection Perspective

Behavioral data extracted from emulators or real devices, such as system calls, are utilized in research studies where machine learning models have been employed for mobile malware detection. However, these studies do not explore whether the selection of data source may have an impact on the performance of the models, assuming that both data sources generate similar outputs. We provide a comparative analysis of the data sets obtained from both sources by using statistical techniques, inducing learning models and demonstrating the impact of data source selection on detection models’ performance. Our study shows that emulators generate more distinguishable data than real devices, meaning that designers of detection models should pay attention to the data sources utilized in the various steps of the machine learning workflow.
Performance Analysis of Beamspace Domain Channel Estimation in mmWave Massive MIMO Systems

Millimeter wave based massive MIMO system is a significant technology to meet the proliferating demand for datarate and spectral efficiency. Millimeter can offer Giga bits of data transfer accompanies with challenge in utilization of radio frequency $(R$F) chain. Efficient utilization of RF chain often leads to complex hardware design and larger energy consumption. To reduce number of RF chains utilization, an adaptive selection circuit with reduced digital precoder is introduced to produce the beam. The adaptive selection of RF using reduced digital precoder yield poor performance under low signal to noise ratio environment. A support detection (SD) based method to select strongest element from the beamspace channel is introduced. The SD method offers better capacity improvement and reduced error compared to conventional compressive sensing method in low signal to noise ratio conditions.
Design of Twenty-One Level Modular Multilevel Converter Using a Three-level Submodule Block

In this paper, a modular multilevel converter of twenty one level is designed by using a three level submodule block. The submodule block can be half-bridge or full bridge. In a half bridge submodule block, the switching configuration is more efficient than the full bridge submodule block. But there is problem of DC fault current. Half bridge submodule block is not capable of blocking the DC fault current. In this paper, we have designed such kind of submodule block which is a combination of half bridge and flying capacitor. A capacitor which is floating with the half bridge circuit that is called flying capacitor. We have designed many modular multilevel converters of different levels using this submodule block but the twenty one level modular multilevel converter gives the best output. A simple control mechanism is implemented in this design. So, our proposed design of modular multilevel converter is simple and it shows better performance.
Surface State Controlled Superior Photodetection Properties of Isotype n-TiO2/In2O3 Heterostructure Nanowire Array With High Specific Detectivity

An integrate approach of growing vertical axial heterostructure (AH) nanowire (NW) with TiO<sub>2</sub> and In<sub>2</sub>O<sub>3</sub> may provide an alternative route for efficient improvement in internal gain and photosensitivity. Here, TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW is synthesized on p-Si substrate inside e-beam evaporator using glancing angle deposition technique. Field emission gun scanning electron microscopy confirms the growth of vertically aligned TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW NWs. Averagely, 1.5 times absorption enhancement is observed in TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW compared to the as-deposited TiO<sub>2</sub> NW sample. The designed TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW device shows superior optoelectronic performance in terms of fast response (T<sub>R</sub> = 52.96 ms and T<sub>F</sub> = 89.32 ms), high photosensitivity (∼1000 times), and good rectification ratio (∼10 times). In addition, maximum responsivity (11.17 A/W), high internal gain (36.8), superior detectivity (3.14 × 10<sup>14</sup> Jones) and low noise equivalent power (8.92 × 10<sup>−15</sup> W) are obtained for TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW. A large linear dynamic range value of 59.14 dB is obtained for TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW due to prolonged carrier recombination lifetime, multiple light scattering within NWs and efficient charge separation along the length of heterostructure. Therefore, the outstanding optoelectronic properties obtained with TiO<sub>2</sub>/In<sub>2</sub>O<sub>3</sub> AH NW will ensure a wider promising electronic and optoelectronic device applications.
Research on Safety Level DCS Automated Test Method Based on Symbolic Execution

Nuclear power plant safety-level DCS (Distributed Control System) is the central nerve of the entire nuclear power plant, ensuring the safety and reliability of safety-level DCS application software is crucial. The safety-level DCS has to deal with different working conditions, and the corresponding DCS software has to perform different signal processing processes, and each processing process forms a software path. The safety-level DCS serves as a safety protection for nuclear power plants and requires the desired results for each path. Testing the path is an important way to ensure software reliability. This paper uses symbolic execution to do automated path testing of nuclear safety-level DCS application software, and gives a complete automated testing methods. This gives a new idea to automated testing of DCS software paths.
Measured data and model for erosion of a spark gap at continuous 800 Hz, 10 J pulsed operation

Spark gap closing switches suffer from electrode erosion and the switching medium has to recover after each pulse. The recovery is strongly improved by flushing the gap. The performance of the spark gap of our pulsed corona system will be described on the aspect of erosion. The operation is a continuous flow of short kA pulses at kHz repetition rate. The experimental erosion results for six electrode materials are presented. Flushing gas is Nitrogen. An analytical and a numerical erosion model is proposed. Erosion was monitored during 65 million pulses. The investigated materials rank, at decreasing quality of performance, as follows: copper > brass > copper/tungsten > stainless steel > tungsten > aluminum. Time and space resolved heating of the electrode can be simulated, including vaporization and melting. The power input of the models is the product of the measured discharge current and estimated effective fall voltage.
Multimodal Spatiotemporal Networks for Sign Language Recognition

Different from other human behaviors, sign language has the characteristics of limited local motion of upper limb and meticulous hand action. Some sign language gestures are ambiguous in RGB video due to the influence of lighting and background color, which affects the recognition accuracy. We propose a multimodal deep learning architecture for sign language recognition which effectively combines RGB-D input and two-stream spatiotemporal networks. Depth videos, as an effective compensation of RGB input, can supply additional distance information about the signer’s hands. A novel sampling method called ARSS (Aligned Random Sampling in Segments) is put forward to select and align optimal RGB-D video frames, which improves the capacity utilization of multimodal data and reduces the redundancy. We get the hand ROI by joints information of RGB data for local focus in spatial stream. D-shift Net is proposed as depth motion feature extraction in temporal stream, which fully utilizes three dimensional motion information of the sign language. Both streams are fused by convolutional fusion layer to get complementary features. Our approach explored the multimodal information and enhanced the recognition precision. It obtains the state-the-of-art performance on the datasets of CSL (96.7%) and IsoGD (63.78%).
Sentiment Analysis on Grab User Reviews Using Support Vector Machine and Maximum Entropy Methods

One business that has developed along with the increase in information and communication technology is the transportation service business. The last decade has emerged as a technology-based transportation business innovation, for example Grab. It is important for a company or organization to find out about people’s responses to their services or products. Public opinion on the product is not small in number, even though it is undeniable that public opinion has an impact on the company’s image. Therefore, a technique for analyzing the opinion is needed so that the company can monitor and organize their services. Public opinion is classified into positive or negative sentiment classes using SVM and Maximum Entropy methods. The labeling results are then analyzed by text association to find the relationship of each information obtained. Classification with SVM method produces 89.01% accuracy. Whereas Maximum Entropy obtained higher accuracy that is 90.46%. Text associations obtained from the positive sentiment class and negative sentiment class. The results of negative reviews are analyzed for causes and consequences using fishbone diagrams for problem solving.
A 76-GHz to 81-GHz, 0.6° RMS Phase Error Multichannel Transmitter With a Novel Phase Detector and Compensation Technique

A multichannel transceiver operating in 76–81 GHz with precisely phase-controlled transmitter for the automotive radar application is presented. To achieve high phase accuracy of transmit signal, a novel phase detector using third-order distortion is proposed to compensate the phase control error. To avoid the insensitivity region on specific phase condition, the frequency-shifted method applying the propagation delay difference between transmit and reference signal path is also presented. The multichannel transmitter using proposed detector achieves less than 0.6° root-mean-square (RMS) phase error in 76-GHz to 81-GHz frequency range, over −40 °C–125 °C ambient temperature condition. Since the proposed phase detector does not use the other TX as reference, it is suitable for extending the number of channels. This transceiver, including the proposed phase detector is implemented in 65-nm CMOS technology. The phase detector consumes 1.8 mW per channel and occupies 0.15 mm2 per channel.
Classification of Intrinsic Mode Spectra of EEG Signal for Epilepsy Detection

In this paper, we proposed EEG based epileptic seizure detection where statistical bandwidth of intrinsic mode functions (IMFs) are used. EEG signals are decomposed into IMFs through the implementation of Empirical Mode Decomposition. These IMFs are turned into analytical functions by means of Hilbert transformation. Euclidean value of Amplitude and Frequency Modulated Bandwidths are calculated from these analytic functions. Finally, the statistical distribution bandwidth of these different IMFs is considered as a feature. The classification was done by means of Support Vector Machine (SVM) with the Gaussian kernel. For training purpose, 75% of the data is utilized. Finally, 89.6% accuracy was gained for the remaining 25% test data in the SVM classifier.
Electro-Optical Co-Design of Power-Efficient 100-Gbps/λ VCSEL Transmitter

In this paper, we propose a comprehensive electro-optical co-design framework for power-efficient high-speed VCSEL transmitter targeting at 50 Gbaud. The framework integrates a rate-equation based VCSEL chip model and driver circuit models. Subject to the transmitter specifications defined by IEEE 400G standard, the driver circuit design of NRZ and PAM-4 modulations, together with the energy efficiency of transmitter, is analyzed and compared for two types of 20-GHz class VCSELs with different damping factors while keeping the same parasitic parameters. For NRZ modulation, analysis results indicate that both types of VCSEL transmitters show similar achievable data rate and energy efficiency. Both types of VCSEL transmitters are beneficial with small driver resistance in order to achieve high-speed signaling integrity. The best energy efficiency for both types of VCSEL transmitters is 520.8 fJ/bit under NRZ modulation. For PAM-4 modulation, higher achievable data rate can be reached by the over-damped VCSEL transmitter compared with the under-damped one, for which trade-offs of choosing the appropriate driver parameters are required for high-speed operation. The optimized over-damped VCSEL transmitter can have 16.13% higher achievable data rate and around 4.12% of energy efficiency improvement compared with the under-damped counterpart, but the signal quality is more sensitive to driver parameters. The best energy efficiencies are 365.1 fJ/bit and 380.8 fJ/bit for the over- and under-damped VCSEL transmitter under PAM-4 modulation. This co-design framework can provide a guidance in the VCSEL enabled co-packaging design.
Widely Tunable, 25-mW Power, Ti:sapphire Crystal-Fiber Laser

Using a glass-clad Ti:sapphire crystal fiber (CF) as the gain medium, we demonstrate for the first time a diode-pumped, 25-mW tunable laser with a 180-nm tuning range. Active medium is a 2.4-cm long, a-cut Ti:sapphire CF we have drawn with an elliptical core of 13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter in the [0001] axis and 15.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter in the [<inline-formula> <tex-math notation="LaTeX">$1\bar {1}00$ </tex-math></inline-formula>] axis. The CF is cladded with silica and has high efficiency (88%) of coupling to the pump, a diode laser emitting 1-W at 520-nm. A blazed grating provides a continuous smooth tuning on the wavelength range from 700 to 880 nm.
Graph based Unsupervised Learning Methods for Edge and Node Anomaly Detection in Social Network

In the last decade online social networks analysis has become an interesting area of research for researchers, to study and analyze the activities of users using which the user interaction pattern can be identified and capture any anomalies within an user community. Detecting such users can help in identifying malicious individuals such as automated bots, fake accounts, spammers, sexual predators, and fraudsters. An anomaly (outliers, deviant patterns, exceptions, abnormal data points, malicious user) is an important task in social network analysis. The major hurdle in social networks anomaly detection is to identify irregular patterns in data that behaves significantly different from regular patterns. The focus of this paper is to propose graph based unsupervised machine learning methods for edge anomaly and node anomaly detection in social network data.
Optimal Sizing and Cost Estimation of Sugar Cane Industry

Due to environmental and energy policy concerns Distributed generation is popular due to its better utilization of power generation technologies. In this paper a hybrid power plant is incorporated upon the sugar cane power plant, sugar cane plant is optimum for such distributed generation as a byproduct of a sugar plant is used for power generation however the working period is small therefore to extend the period other renewable resources such as CHP and solar are incorporated. The capital cost of the plant is always an issue for the company, which mainly depends upon the capacity of components. Therefore, choices are to be made based on the economy. Cost and Benefit analysis is one such approach to maximize financial benefit. In this Paper optimal size of all components to be incorporated in sugar, the plant is determined using Yalmip Toolbox in Matlab.
The Development of Dual-Band Antenna for Applying MIMO Systems

This paper presents the slot antenna I-shaped and the rectangular slot etching technique for Multiple-Input Multiple-Output (MIMO) systems of dual-band at 2.45 GHz and 5.2 GHz. The characteristics of the proposed antenna and operational parameters were determined by using the CST Microwave studio and experimental technique. The prototype antenna arranges 1x4 at distance $\lambda /2$ for matching impedance 50 $\Omega$. The prototype antenna was fabricated on FR4 printed circuit broad (PCB) with dielectric constant of 4.3 and thickness of 0.764. The simulated and measured results indicated that the return loss (S11) of the proposed antenna was less than -10dB. The measured bandwidth of 10.35% (2.38 - 2.64 GHz) for low bands and 18.31% (4.81 - 5.78 GHz) for high bands. The average gain of 2.57 dBi and 3.34 dBi, respectively. The isolation (S21) was less than -15 dB.
Wear Particle Texture Analysis

Wear particle analysis is used for health monitoring of a machine to identify possible failure modes resulting from various machine components. One of the initial phases of analysis typically involves a microscopic examination of particles extracted from the lubrication system. Experts in the field (Tribologists) utilize this important information from these examinations to monitor the operation of the machine and ensure efficiency, safety, and economy of the operation. This extracted information has been characterized by six morphological features of particle shape, edge curvature, size, color, thickness ratio, and surface texture. These features are the building blocks in the creation of image analysis systems to supplement the wear judgments made by experts who use traditional methods of diagnostics. The paper investigates one of the six morphological features related to the surface texture classification of wear particles. Image processing procedures of line/edge detection and Artificial Neural Networks are used to recognize four texture types of smooth, pitted, striations, and rough. An accuracy classification rate of approximately 98.9% has been achieved and is shown by a confusion matrix.
Abnormality Detection in Human based on Hybrid Model - NB3 AkMGA

Remote Health Monitoring for elderly people has attracted a lot of attention in the recent years. The existing healthcare systems provide only a generalized method of detecting abnormalities without considering individual’s personalized optimal health parameter value. The normal range of vital health parameter values for a person may vary over a period of time as a result of ageing, gender, pre/post-surgery conditions, etc. Hence, there is a need for an adaptive health monitoring system to provide the necessary personalization in the vital health parameter values. This paper proposed a hybrid model NB3 AkMGA for a personalized health care using machine learning methods and genetic algorithm. Also, cloud-based health monitoring provides anytime, anywhere access to the health parameters of the person for their respective doctors. The scheme is implemented using PHP scripting and Java, deployed and tested in a private cloud environment. After implementation, the results prove that the proposed model accurately determines the severity levels of the vital health parameter values which reflect in the abnormality detection of a person in real time.