Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.
A 100-mV–2.5-V Burst Mode Constant on-Time- Controlled Battery Charger With 92% Peak Efficiency and Integrated FOCV Technique

In this paper, a burst mode constant ON-time-controlled, energy harvesting charger is presented. The proposed boost converter system uses the burst mode control to improve the efficiency by 11%, compared to the conventional single-mode energy transfer implementation with an ultralow-power input comparator. A technique to deduce an optimum inductor energizes time across the given input range, for which the maximum converter efficiency is demonstrated. An internally triggered, capacitor-less sample-and-hold block addresses the leakage issue in the conventional fractional open-circuit voltage maximum power point tracking systems. The system is capable of charging a super-capacitor of 4.7 mF, from 1.8 to 3.3 V from an input power of $50~ {\mu }\text{W}$ to 100 mW by maintaining the maximum power at the input. The entire system is designed and fabricated in the standard 180-nm CMOS technology, and the measurement results show a peak efficiency of 92% at 98-mW input power for the output voltage of 3 V and efficiency ≥65% across the range of input voltages more than 0.3 V for the output voltage of 1.8 V.
A Novel Idea of Classification of E-commerce Products Using Deep Convolutional Neural Network

The high numbers of non-food products and categories on today E-commerce sites render validation of the data as labor intensive and expensive task. Therefore, there is a recent push to automate validation of correct placement of product in category. The French E-commerce company CDiscount has launched Kaggle competition, sharing huge dataset of over 7 million products, to solve the very problem. The goal is to classify products containing multiple images into one of 5270 categories. This paper proposes, implements and experimentally evaluates deep neural network architecture for classification of non-food E-commerce products. To tackle the complexity of the task on available hardware, hierarchical architecture of neural networks that exploits existing category taxonomy is proposed. The hierarchical architecture achieved the Top-1 accuracy of 0.61061. It has been found, that specific networks in hierarchical architecture can be successfully transferred onto similar datasets, by transferring network that learned on books onto different book dataset. The transferred model performed better than the same model pre-trained on ImageNet dataset.
High-Linearity Bottom-Plate Mixing Technique With Switch Sharing for  $N$ -path Filters/Mixers

A four-path filter/mixer for surface acoustic wave (SAW)-less frequency division duplex (FDD) radio receivers is proposed, targeting high linearity and compression requirements. A bottom-plate mixing technique improves linearity by reducing the gate-source voltage modulation of the MOSFET switches. Differential bottom-plate mixing allows for switch sharing which halves the effective switch resistance to reduce drain-source voltage modulation. The first four-path switch-RC filter stage with bottom-plate mixing and a shared switch renders 2nd-order voltage-domain RF-bandpass filtering around the LO frequency. Extra out-of-band rejection is implemented combined with <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> conversion and zero-IF frequency down-conversion in the second cross-coupled switch-RC four-path stage, which offers a low-ohmic high-linearity current path for out-of-band interferers. A prototype chip fabricated in a 28-nm CMOS technology achieves an out-of-band IIP3 of +44 dBm, IIP2 of +90 dBm and blocker 1-dB gain-compression point of +13 dBm for a blocker frequency offset of 80 MHz. At this offset frequency, the measured desensitization is only 0.6 dB for a 0-dBm blocker, and 3.5 dB for a 10-dBm blocker at 0.7-GHz LO (i.e., 6- and 9-dB blocker noise figure). The chip consumes 38–96 mW for LO-frequencies of 0.1–2 GHz and occupies an active area of 0.49 mm<sup>2</sup>.
Visual-based trash detection and classification system for smart trash bin robot

This paper presents a trash detection and classification system that will be implemented on a social-education trash bin robot. The robot is expected can be implemented in public facilities, like airport, railway station, hall and more which is there are a lot of people that potentially producing waste. We use Haar-Cascade method to first detect any objects on the floor. Then, Gray-Level Co-Occurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOG) are combined to get a set of features. Support Vector Machines (SVM) is used to classify the features into organic waste, non-organic waste, and non-waste. Offline testing of classification system using 5-fold Cross Validation method obtain 82,7% of accuracy. Online testing of detection and classification system obtain 63.5% of accuracy with the best distance gained when the camera is tilted down to −40° with minimum distance for detection is 80 cm and 200 cm for maximum detection. By using this robot, it is expected to help instill the habit of disposing of garbage in the right place. The purpose of this research is making people aware of handling their waste in the right way and hopefully, it can reduce the waste problem.
Sign Language Recognition Using Modified Convolutional Neural Network Model

Sign Language is an interesting topic and similar to Action Recognition. Especially along with the great development of Deep Learning. Video-based Sign Language Recognition is our concern because we want to recognize a sign not only by the shape but also by the action the signer does. The problem is sign language is very complex and vary. The variation of sign language is making the system harder to recognize all the words accurately. Many researchers have been researching Sign Language Recognition for a long time. So many methods had been used to find out which one is the best method. Because of similarity between Sign Language Recognition and Action Recognition, we are trying to implement one of the top-tier models in Action Recognition which is i3d inception this model is also a new Action Recognition model with very high accuracy. So we can know is it possible to adopt Action Recognition behavior into Sign Language Recognition. The goal of this paper is to implement the i3d inception model to Sign Language Recognition with transfer learning method. From the test we've been done, we got 100% accuracy on training with 10 words and 10 signers with 100 classes but the validation accuracy is pretty low. This model is too overfit.
Accuracy Improvement of fNIRS based Motor Imagery Movement Classification by Standardized Common Spatial Pattern

Motor imagery event classification from functional near-infrared spectroscopy (fNIRS) is one of the most interesting problems of the current brain-computer interfaces (BCIs) challenges. Feature extraction from multiple channel fNIRS signal is always challenging due to its high dimensionality. To reduce the feature dimension, common spatial pattern (CSP) is an effective method. The present research work proposes an algorithm named by standardized common spatial pattern (SCSP) based filtering method for fNIRS based motor imagery classification. This work used both the conventional CSP method and the proposed SCSP method to extract the features from the fNIRS signal through spatial filtering. The results revealed that the proposed SCSP algorithm outperformed the conventional CSP method and channel-wise methods for classifying the two motor imagery event classifications. In addition, it can improve the classification accuracy up to 17% (in average) and 7%(in average) with respect to the channel-wise feature extraction method and conventional CSP algorithm, respectively.
Heuristic Based Learning of Parameters for Dictionaries in Sparse Representations

Sparse representation has attracted attention recently by successful applications in the computer vision domain. The success of these methods depends on the learned dictionary as it represents the latent feature space of the data. Different parameters affect the dictionary learning process like the number of atoms and sparsity limit. Generally, these parameters are learned through trial and error experimentation which requires a lot of time. In the literature, no approach is seen that attempts to relate these dictionary parameters to the data. In this paper, we propose heuristics for this problem. These heuristics use statistical properties of the data to estimate dictionary parameters. The proposed heuristics are applied to several datasets.
A Review of Computational Intelligence for StarCraft AI

After artificial intelligent (AI) scientists have conquered Go game, StarCraft has been the next biggest challenge. A highly intelligent AI system that is able to beat human professional players is expected. In this paper, we review the recent development of computational intelligence (CI) in the field of StarCraft AI. Successful applications of CI techniques are analyzed and compared from different levels of AI functionality. It should be noted that current StarCraft AI is highly dependent on human experience and is far away from a completely intelligent one. New frameworks and techniques are still expected to improve the intelligence.
Performance Bounds of Secondary User's Transmission Under Fading Channel

Cognitive radio (CR) is a promising technology to improve the spectrum utilization in that it allows secondary users (SUs) to transmit over the spectrum holes by spectrum sensing. Though the SUs are with low priority to access to the channel, the QoS guarantees are critical for their transmissions. In this paper, we construct a cross-layer based model to analyze the performance bounds for SUs, by which we investigate how different factors jointly influence the transmission of SUs, including PU activity, number of SUs and channel fading, etc. To this end, the stochastic network calculus is employed as a basic tool, where impairment process is introduced to construct the service process for SUs. Then, the delay bound and throughput bound are derived for given arrival process. Numerical results demonstrate that PU activity has a dominant impact on the performance of the transmission for SUs.
High-Entropy STT-MTJ-Based TRNG

Hardware true random number generators (TRNGs) yield random numbers from physical processes. Traditionally, such devices are based on statistically random events such as thermal noise or other quantum phenomena. In this brief, we propose a novel TRNG design using a spin-transfer torque magnetic tunnel junction (MTJ) device. Our solution exploits the stochastic nature of the MTJ switching, and the behavior of an XOR gate dealing with probabilistic signals. We show that by using multiple MTJ devices, the proposed TRNG succeeds in filtering the negative effect of environmental changes as well as fabrication-induced variability and generates random sequences with high-entropy under any conditions.
Co-expression Network Analysis Identifies Four Hub Genes Associated With Prognosis in Soft Tissue Sarcoma

Background: Soft tissue sarcomas (STS) are heterogeneous tumors derived from mesenchymal cells that differentiate into soft tissues. The prognosis of patients who present with an STS is influenced by the regulation of a complex gene network. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with STS (Samples = 156). Results: Among the 11 modules identified, the black and blue modules were highly correlated with STS. However, using preservation analysis, the black module demonstrated low preservation, therefore the blue module was chosen as the module of interest. Furthermore, a total of 20 network hub genes were identified in the blue module, 12 of which were also hub nodes in the protein-protein interaction network of the module genes. Following additional verification, 4 of 12 genes (RRM2, BUB1B, CENPF, and KIF20A) demonstrated poorer overall survival and disease-free survival rate in the test datasets. In addition, gene set enrichment analysis (GSEA) demonstrated that samples with a high level of blue module eigengene (ME) were enriched in cell cycle and metabolism associated signaling pathways. Conclusion: In summary, co-expression network analysis identified four hub genes associated with prognosis for STS, which may diminish the prognosis by influencing cell cycle and metabolism associated signaling pathways.
Using Constrained Model Predictive Control to Control Two Quadrotors Transporting a Cable-Suspended Payload

This paper applies a constrained MPC controller to control two quadrotors which carry a cable-suspended payload together. The system dynamics is derived from the Euler-Lagrange equation. Given the dynamics complexity, a linear MPC controller is employed for a control task, which is to make the payload to track a desired trajectory while stabilising the two quadrotors. The constraints on quadrotor control signals and payload positions are taken into consideration. The constrained controller could be useful for practical control systems. The simulation results are provided to evaluate the control performance against an LQR controller.
Channel Modelling for 60GHz mmWave Communication Inside Bus

The 5G vision acknowledges intravehicular communication as a means to enable passenger connectivity on the move. The capacity demand in a public transport vehicle is multi-fold compared to personal cars as there are more people on-board. In order to meet the demand, 5G standardization bodies prescribe moving the spectrum up to the millimetre wave (mmWave) regime. In this paper, we focus on buses as they are the most pervasive form of public transportation, and provide a wideband wireless channel model for 60GHz mmWave propagation inside bus. The model characterizes power delay profile (PDP) of the wireless intravehicular channel and is derived from about a thousand measured datasets within a bus. The proposed analytical model is further translated to a simple simulation algorithm which generates in-vehicle channel PDPs. The simulated PDPs are in good agreement with the measured data.
Distributed Control of A Class of Nonlinear Multi-Agent Systems with Switching Topologies*

This paper presents a cyclic-small-gain approach to distributed control of non linear multi-agent systems with switching topologies for output agreement. Each agent is assumed to be in the strict output-feedback form. By introducing a new dynamic mechanism, a dynamic compensator, a local state-observer and a state transformation, the multi-agent system is transformed into a dynamic network having switching topology and consisting of input-to-output stable and unboundedness observable subsystems. The output agreement is verified to be realized by guaranteeing the stability of the dynamic network with the help of the cyclic-small-gain theorem.
Toward High-Performance Implementation of 5G SCMA Algorithms

The recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as sparse code multiple access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offers improved bit error rate performance and higher spectral efficiency than other comparable techniques, but these improvements come at the cost of complex decoders. There are many challenges in designing near-optimum high throughput SCMA decoders. This paper explores means to enhance the performance of SCMA decoders. To achieve this goal, various improvements to the MPA algorithms are proposed. They notably aim at adapting SCMA decoding to the single instruction multiple data paradigm. Approximate modeling of noise is performed to reduce the complexity of floating-point calculations. The effects of forwarding error corrections such as polar, turbo, and LDPC codes, as well as different ways of accessing memory and improving power efficiency of modified MPAs are investigated. The results show that the throughput of an SCMA decoder can be increased by 3.1 to 21 times when compared to the original MPA on different computing platforms using the suggested improvements.
Long Short-Term Memory Based Language Model for Indonesian Spontaneous Speech Recognition

A robust recognition performance in daily or spontaneous conversation becomes necessary for a speech recognizer when deployed in real world applications. Meanwhile, the Indonesian speech recognition system (ASR) still has poor performance compared to dictated speech. In this work, we used deep neural networks approach, focused primarily on using long short-term memory (LSTM) to improve the language model performance as it has been successfully applied to many long context-dependent problems including language modeling. We tried different architectures and parameters to get the optimal combination, including deep LSTMs and LSTM with projection layer (LSTMP). Thereafter, different type of corpus was employed to enrich the language model linguistically. All our LSTM language models achieved significant improvement in terms of perplexity and word error rate (%WER) compared to n-gram as the baseline. The perplexity improvement was up to 50.6% and best WER reduction was 3.61% as evaluated with Triphone GMM- HMM acoustic model. The optimal architecture combination we got is deep LSTMP with L2 regularization.
Texture Pattern Classification via Weibull Distribution-based Image Statistical Modeling*

A novel image texture pattern classification (ITPC) method based on statistical modeling of image spatial structures (ISSs) is presented due to the perceptual perspective that the visual appearance of image texture pattern (ITP) visually depends on the random organization of local homogeneous fragments. Firstly, the oriented Gaussian derivative filters (OGDFs)-based ISS characteristics expression method is introduced. Then, the Weibull distribution (WD) behavior of ISSs, boiled down to the randomly-distributed local homogeneous fragments, is explicated by the sequential fragmentation theory (SFT). Finally, the WD-based ISS feature parameters, directly related to the human vision perception(HVP) system, are extracted and used to ITPC by a partial least squares-discriminant analysis (PLS-DA)-based classifier. The effectiveness of the proposed ISS-based ITPC method is verified by extensive experiments on three different texture image databases and compared with some classic statistics-based ITP identification methods.
Learning TWSVM using Privilege Information

Expert’s knowledge can be used to improve classification performance of the algorithm or to reduce the requirement of data for training the algorithm. However, in the field of machine learning, the knowledge offered by the expert is rarely used. Recently, Qi et al. [1] proposed a fast learning model for TWSVM using privilege information termed as FTWSVMPI where privilege information is acquired by Oracle function. Oracle function needs to solve two additional TWSVM based Quadratic Programming Problems (QPPs) which leads to higher computational cost. Therefore, to avoid to solve two additional TWSVM based QPPs, in this paper, we propose a novel method to extract privilege information from the dataset itself. Using this privilege information, we further introduce an improved version of Twin Support Vector Machine termed as I-TWSVMPI. The proposed I-TWSVMPI incorporates privilege information using correcting function so as to obtain two nonparallel hyperplanes. We also perform experiments for pedestrian detection as an application to proposed I-TWSVMPI. The experimental results on several benchmark UCI datasets and pedestrian detection prove the efficacy of our proposed formulation to that of other state-of-the-art classification algorithms with comparatively lesser computational time.
A Framework of Ultrasounds Image Slice Positioning and Orientation in 3D Augmented Reality Environment using Hybrid Tracking Method

Augmented reality (AR) in ultrasound image guided able to improve the understanding of medical personnel to the scanned anatomy. The issues in ultrasound image-guided are the operator needs to mentally transforming a series of 2D ultrasound images to become 3D object in real environment. The current 3D ultrasound machines are expensive and bulky. Therefore, this paper proposes to investigate and solve the ultrasound image position in 3D environment of AR using hybrid tracking method and game controller. This project involves five phases. The first is to investigate and analyze the issues in implementing the orientation ultrasound images in AR environment. The second phase is to propose and design AR system using hybrid tracking technique in order to overcome the problems in matching ultrasound images orientation on baby phantom in AR environment. Finally, the proposed hybrid tracking technique of AR on the output generated from the algorithm are evaluated. The aim of this project is to match the position of ultrasound image on baby phantom using hybrid tracking method.
Using DMAIC in Designing Capstone Courses to Enhance Essential Skills in Undergraduate Students

The role of the university is to provide society with holistic professionals: not only with domain of technical areas, but also with essential skills to support their performance at the organizations. This paper describes the use of DMAIC as the backbone of the senior design course at Escuela Superior Politecnica del Litoral (ESPOL) in Guayaquil, Ecuador. Senior design projects are developed in real companies with multidisciplinary teams following the different stages of DMAIC. In this document, we explain the faculty guided process, from the project selection and its definition to the solution and control plan. Also, we analyze the evolution of student outcomes -related to essential skills-as a consequence of improvements in the capstone process. We measured the changes in: written communication skills in native language, verbal communication in English -as a second language-, ability to function on multidisciplinary teams and awareness of the impact of the engineering profession. The results show significant improvements in each student outcome through the DMAIC stages. We conclude that senior design course; successfully supports the university in the emphasis of essential skills in engineering students. At the same time, this scheme enables students to effectively solve the problem of the firms.