Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase Opposition Disposition PWM (POD-PWM) Strategy to Reduce WTHD Content from an NPC Inverter-Fed Electric Vehicle System

Neutral-point clamped Multilevel inverter (NPC-MLI) has transformed into a broadly accepted choice in the field of electric vehicle (EV) applications. In the traditional NPC-MLI-fed EV system, the problem regarding the higher harmonic content and the lower switching frequency still persists. In this paper, a phase opposition disposition pulse width modulation (POD-PWM) technique is proposed for a three-phase NPC-MLI, which is proficient in reducing the high-frequency weighted total harmonic distortion (WTHD) content of the inverter output voltage by employing a double Fourier integration-based harmonic mitigation algorithm. The experimental results of an FPGA-based NPC-MLI prototype hold a good resemblance with the theoretical analysis and simulated results. In addition, a comparative investigation affirms that the proposed strategy exhibits the highest efficiency and lowest WTHD content among all the previously-reported PWM schemes under study, which verifies the adaptability of the proposed work in electric vehicle applications.
Study of Dynamic Geometrical Parameter Detection System for Tramcar Track Irregularity

Despite the rapid development of the tramcar market, the study concerning security detection has been a later starter and mostly remains the human detection stage currently, resulting in low detection efficiency and precision. In this case, the paper suggests a detection system for the track irregularity of tramcars, realizing non-contact dynamic measurement over the geometric parameters for groove track longitudinal level and alignment irregularity. In light of this, it further proposes an irregularity detection algorithm for the geometric parameters of groove tracks based on the principle of laser distance measuring and the mid-chord offset (MCO) method, which overcomes the traditional asymmetrical chord offset method’s defect of being only capable of checking the irregularity of short wavelengths within lm and avoids the damage of tracks due to contact track measuring instruments. Importantly, the high detection accuracy, favorable stability, and other favorable characteristics of the detection system proposed have been proven by experimental verification, thereby providing the detection of geometric parameters for modern tramcars with a new method and a new tool.
An Approach to Prevent Road Accident using Intelligent Device

Transport is one of the pillars of the economy of any country: every year government and car/vehicle manufacturers spending a lot of money on road safety. But unfortunately, crashes on roads cause various problems for the government. The main factors that cause fatal crashes, grievous injuries, and deaths are of varied nature. The type of roads, the structure of ways, weather conditions, and behaviour of the driver are a few pertinent factors that are studied and relationship identified. This paper describes a device that is capable to locate crashes early. In case if a crash occurs, the device can recognize and send information to the nearest hospital and police station. There are two acoustic signals analysed, vehicle crash sound and human distress call (generated during crashes/accidents). For recognition of both the sounds, this paper describes an algorithm based on acoustic signal processing. The algorithm was tested in the lab and it is robust and the efficiency of the algorithm 94.7% to detect a collision.
Ultra-Fast Tunable Laser Enabling 4 ns Coherent Slot Switching Beyond 100 Gbit/s

We demonstrate ultra-fast wavelength switching of a narrow linewidth tunable laser. It enables coherent slot switching with a waiting time comprising laser switching, stabilization, channel estimation and data recovery under 4 ns (resp. 30 ns) for 32 Gbaud dual polarization QPSK (resp. 16QAM)
Design and Development of Capacitor Run Split-Phase Fan Motor for Higher Efficacy

This paper presents the design and development of the permanent capacitor split phase ceiling fan motor with higher air delivery per unit power consumption. An existing ceiling fan is taken as a case study and experimental performance is validated against finite element analysis (FEA). A parametric analysis of significant parameters such as turns in both the windings, permanent capacitor connected across the auxiliary winding, air gap length, stack length and loading, is made in detail. The copper losses and the core losses are also analyzed for the parametric variations. Moreover, the efficiency of the motor is enhanced for optimum operation. Based on the study, two new motors are designed for improvement in the performance keeping the outer diameter of the motor same as the existing motor. The overall saturation of the motor is reduced by modifying the lamination design and increasing overall volume of the motor. The designed motors are analyzed using FEA and prototypes are developed. Both the prototype results show significant improvement in the power consumption as well as the air delivery with blades of existing fan.
Visualized Dynamic Coverage Model of City-wide Carparks based on Boundary Constrained Weighted Voronoi Diagram

Coverage model is often applied in spatial networks to evaluate site planning and robustness of networks. The visualized coverage model is helpful for people to understand and analyze these spatial data, in support for better decision. In this paper, we propose a novel dynamic coverage model for carparks around the city. Every carpark is quantified dynamically by its service capability, which is calculated in real time by the algorithm ParkingRank proposed in our previous work. Then an improved weighted Voronoi diagram featuring boundary constraint is introduced to map the quantified service capability of each carpark to the geospatial space, forming a seamless and non-overlapping coverage over the city. The best advantage of this model is the coverage scope of each carpark will dynamically adjust with its service capability varying, while the seamless and non-overlapping feature of coverage would be kept throughout from the perspective of the whole city. It is the first time that the network of carparks is modeled in a visual way, from which the city-wide parking management will get powerful decision supports in the future.
Design of a Robust Controller for a Voltage-mode Controlled Buck Converter

Linear controller-based voltage-mode control finds widespread application in the power management industry due to its ease of control. A linear controller is designed based on the average small-signal model of the converter which ignores the effect of converter's parasitics, dead-time, etc. Thus, the designed linear controller often fails to provide desired transient and steady-state performances in a practical converter. In this paper, a robust controller is designed using the average small-signal model of the synchronous buck converter. The designed robust controller can handle the impact of the system parasitics and dead-time. The performance of the proposed robust controller is compared with a linear controller using a MATLAB/Simulink model of the synchronous buck converter. Simulation results show significant improvement in the output voltage overshoot and undershoot and settling time when the robust controller is employed in the closed-loop.
Towards the implementation of an Attention-based Neural Machine Translation with artificial pronunciation for Nahuatl as a mobile application

There are great translation systems online. However, even though this technology is available for the majority of languages, it is not the case of Nahuatl [1]. For this reason, this paper outlines a master’s degree thesis proposal which is aimed to use a Neural Network for Translation with an attention mechanism and Long Short-Term Memory (LSTM) like the one used by Google [2]. In addition, it seeks to implement an artificial Text To Speech (TTS) system trained with a Neural Network with a given dataset of Mel spectrograms in [3] from a person speaking in Nahuatl and it attempts to achieve a natural voice output as a spectrogram, then process it and obtain the sound desired. Finally, once trained, these models can be prepared for being used within mobile devices, and even taking advantage of the neural engine some of them are equipped with. In this way, this technology can reach more people and help to preserve and even spread the language. The early results showed how the limited resources of this language could cause a strong bias in the outputs and also how there could be some loss of information given the morphemes Nahuatl has, given its polysynthetic nature. This also highlights the way it can be tokenized, playing an important role in how the results turn out obtaining a BLEU score of 0.34 at best. Finally, this application and research can be an interesting framework of how a polysynthetic language can be manipulated to be used for fusional languages like Spanish or English. This research work was carried out at the “Tecnológico Nacional de México” (TecNM), campus of the “Instituto Tecnológico de Apizaco” (ITA)
Full-Sphere Binaural Sound Source Localization Using Multi-task Neural Network

The accuracy of binaural sound source localization is faced with the challenge of localizing azimuth and elevation simultaneously in noisy and reverberant environments. In this work, a full-sphere binaural sound source localization system is proposed using convolutional neural network and multi-task neural network connected to learn the localization features. The log-magnitudes and interaural phase difference (IPD) of binaural signals are used as inputs to a two-branch convolutional neural network, from which interaural and monaural cues are extracted and combined. Then, the full-sphere localization is formulated as two subtasks of estimating azimuth and elevation separately using multi-task neural network. To reduce reverberation effects, the interaural coherence based pre-processing is used to select the direct-path dominated time-frequency bins for localization. The proposed system is evaluated at a variety of noise and reverberation conditions, in comparison with two baseline systems. The results indicate that the proposed system achieves better localization performance, especially for elevation estimation, at low SNR and strong reverberation conditions.
Hunting Vulnerable Smart Contracts via Graph Embedding Based Bytecode Matching

Smart contract vulnerabilities have attracted lots of concerns due to the resultant financial losses. Matching-based detection methods extrapolating known vulnerabilities to unknown have proven to be effective in other platforms. However, directly adopting the technique to smart contracts is obstructed by two issues, i.e., diversity of bytecode generation resulting from the rapid evolution of compilers and interference of noise code easily caused by the homogeneous business logics. To address the problems, we propose contract bytecode-oriented normalization and slicing techniques to augment bytecode matching. Specifically, we conduct data- and instruction-level normalizations to uniform the bytecode generated by different compilers, and enforce contract-specific slicing by tracking data- and control-flows with simulated bytecode executions to prune the noise code as far as possible. Based on the above techniques, we design an unsupervised graph embedding algorithm to encode the code graphs into quantitatively comparable vectors. The potentially vulnerable smart contracts can be identified by measuring the similarities between their vectors and known vulnerable ones. Our evaluations have shown the efficiency (0.47 seconds per contract on average), effectiveness (160 verified true positives) and high precision (91.95% for top-ranked). It is worth noting that, we also identify dozens of honeypot contracts, further demonstrating the capability of our method.
Ransomware Detection with Semi-Supervised Learning

Today, ransomware is one of the most harmful cybersecurity threats that organizations and people face. Hence, there is a vital need for developing effective ransomware detection methods. Machine learning methods can be very useful for ransomware detection if there is sufficient labeled data for training. However, labeling data is time-consuming and expensive while a huge amount of unlabeled data exists. To cope with this problem, semi-supervised learning can be employed that exploits a few labeled data and a lot of unlabeled data for learning. To our best knowledge, there is no research investigating semi-supervised learning methods for ransomware detection. In this paper, we analyze different feature selection and semi-supervised classification methods applied to the CICAndMal 2017 dataset. Our findings suggest that the wrapper semi-supervised classification method using the random forest as a base classifier and OneR or Chi-squared as a feature selection method outperforms the other semi-supervised classification methods for ransomware detection.
A Joint-Loss Approach for Speech Enhancement via Single-channel Neural Network and MVDR Beamformer

Recent developments of noise reduction involves the use of neural beamforming. While some success is achieved, these algorithms rely solely on the gain of the beamformer to enhance the noisy signals. We propose a framework that comprises two stages where the first-stage neural network aims to achieve a good estimate of the signal and noise to the secondstage beamformer. We also introduce an objective function that reduces the distortion of the speech component in each stage. This objective function improves the accuracy of the secondstage beamformer by enhancing the first-stage output, and in the second stage, enhances the training of the network by propagating the gradient through the beamforming operation. A parameter is introduced to control the trade-off between optimizing these two stages. Simulation results on the CHiME-3 dataset at low-SNR show that the proposed algorithm is able to exploit the enhancement gains from the neural network and the beamformer with improvement over other baseline algorithms in terms of speech distortion, quality and intelligibility.
Automatic Segmented-Syllable and Deep Learning-Based Indonesian Audiovisual Speech Recognition

Many studies proved that the audiovisual speech recognition system was better than the audio-only or visual-only ones. Nevertheless, three crucial issues should be carefully designed: the combination of both audio and visual, the acoustic model, and the feature extraction. In this paper, a deep learning-based Indonesian audiovisual speech recognition (INAVSR) system is developed. It is a combination of two models: Indonesian audio speech recognition (INASR) and Indonesian visual speech recognition (INVSR). The INASR is built using the Mel frequency cepstral coefficient (MFCC) as well as Mozilla DeepSpeech (MDS) and Kaituoxu Speech-Transformer (KST). Whereas the INVSR is implemented using the LipNet. A simple procedure of automatic syllable segmentation in the visual data is proposed. It functions to solve the out of vocabulary (OOV) words problem in recognizing speech in a sentence-level video. Evaluation of a small dataset shows that the developed deep speech-based INASR produces a relatively low word error rate (WER) of 22.0%. Meanwhile, the developed LipNet-based INVSR gives a bit higher WER of 30.8%. The proposed automatic syllable segmentation is able to tackle the problem of OOV words. Finally, an evaluation of the dataset of videos informs that the INAVSR system is capable of recognizing audiovisual speech in a sentence-level video. Compared to the INASR, the INAVSR provides slightly higher performance. It is able to give an absolute reduction of the WER by up to 2.0%.
A 345 nW Power Continuous Time Delta Sigma Modulator for Biomedical Applications

We present an ultra low power continuous time delta sigma modulator (CTDSM) for biomedical applications. The design was fabricated in a 180 nm CMOS mixed-mode technology at Semiconductor Lab (SCL), Chandigarh. This makes the complete chain of CTDSM design, fabrication and testing entirely in India. In this paper, we provide the design details and the various architectural level choices that enable the low power design of such a CTDSM. Measurement results of the fabricated chip show that the CTDSM consumes a power of only 345 nW while achieving a SNDR and dynamic range of 67.36 dB and 69.7 dB, respectively while operating at 1V supply voltage, over a 1 kHz bandwidth. This translates to an energy efficiency of 90.46 fJ/conv. The design is also very compact, occupying a total area of only 0.07 mm2.
The effect of urea treated cassava peels as supplement to West African Dwarf (WAD) goats grazing natural pasture

The performance of West African Dwarf (WAD) goats grazing natural pasture and supplemented with different levels of urea-treated cassava peels (UTCP) was evaluated. The urea treatments were 0% UTCP, 4% UTCP (40g urea/kg), 6% UTCP (60g urea/kg) and 8% UTCP (80g urea/kg). Twenty WAD bucks aged between 6 and 9 months and weighing on the average 7.7±1.2kg were randomly allocated to five treatment diets consisting of either natural herbage alone (NH); NH + 0% UTCP (0% UTCP,) NH + 4% UTCP (4% UTCP), NH + 6% UTCP (6% UTCP) and NH + 8% UTCP (8% UTCP) respectively in a completely randomized design experiment lasting 84 days to evaluate the potentials of UTCP as a supplement to natural pasture. Total daily feed intake, daily weight gain and weight gain per gramme nitrogen intake varied significantly (P 0.05) increase in blood urea accompanied ammoniation and blood urea concentration did not exceed the toxic level of 10mg per litre. Keywords : effect, urea treated cassava peels, goats Nigerian Veterinary Journal Vol. 26(1) 2005: 1-9
Beat and Downbeat Tracking of Symbolic Music Data Using Deep Recurrent Neural Networks

Musical beat tracking is one of the most investigated tasks in music information retrieval (MIR). Research endeavors on this task have mostly been focused on the modeling of audio data representations. In contrast, beat tracking of symbolic music contents (e.g., MIDI, score sheets) has been relatively overlooked in the past years. In this paper, we revisit the task of symbolic music beat tracking and present to solve this task with modern deep learning approaches. To the extent of our knowledge, it is the first time that utilizing deep learning approaches to track beats and downbeats of symbolic music data. The proposed symbolic beat tracking framework performs joint beat and downbeat tracking in a multi-task learning (MTL) manner, and we investigate various types of networks which are based on the recurrent neural networks (RNN), such as bidirectional long short-term memory (BLSTM) network, hierarchical multi-scale (HM) LSTM, and BLSTM with the attention mechanism. In the experiments, a systematic comparison of these networks and state-of-art audio beat tracking methods are performed on the MusicNet dataset. Experiment results show that the BLSTM model trained specifically on symbolic data outperforms the state-of-the-art beat tracking methods utilized on synthesized audio. Such a comparison of performance also indicates the technical challenges in symbolic music beat tracking.
An Efficient Algorithm to Count the Relations in a Range of Binary Relations Represented by a k²-Tree

Two sets <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>, whose elements fulfill a total order on operator <inline-formula> <tex-math notation="LaTeX">$\le $ </tex-math></inline-formula>, can have a binary relation <inline-formula> <tex-math notation="LaTeX">$R \subseteq A \times B$ </tex-math></inline-formula> represented by the k<sup>2</sup>-tree compact data structure, which greatly improves storage space. Currently, <inline-formula> <tex-math notation="LaTeX">$Count$ </tex-math></inline-formula> query is managed by either using <inline-formula> <tex-math notation="LaTeX">$Range$ </tex-math></inline-formula> query or to modify the structure to have aggregate information, implying additional time or space in order to perform the query. This article presents <italic>Compact Count</italic>, which exploits the k<sup>2</sup>-tree properties to reduce the paths to be scanned to count the numbers in a range <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>, thus ensuring an expected runtime of <inline-formula> <tex-math notation="LaTeX">$O(\log _{k} r \log _{k} n)$ </tex-math></inline-formula> and storage of <inline-formula> <tex-math notation="LaTeX">$O(\log _{k} r)$ </tex-math></inline-formula> with the k<sup>2</sup>-tree parameters <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. Our algorithm was compared through a series of experiments that consider both synthetic data with different distributions and real data, with a solution based on the <italic>Range</italic> algorithm. Experimental results show that <italic>Compact Count</italic> is 250 to 1,000 times faster than <italic>Range</italic> on synthetic and real data, respectively, with a small additional storage cost, as expected by the theoretical analysis.
Investigation of Energy Efficiency in Underlaid D2D Assisted 5G Cellular Network

Next generation wireless standard 5G is expected to meet huge spectrum demand by enabling new technology, i.e., device to device communication (D2D). This technology enhances spectrum efficiency (SE) while interference management due to device proximity is challenging by coordinating transmission power between cellular and D2D users, i.e., underlaid approach. Another key design requirement fr 5G is energy efficiency (EE). In this research, we investigate the EE of cellular network which is underlaid with D2D for multiple bands while considering several network criteria such as different bandwidths (BW), path loss exponents (PLE), transmission power required for cellular users (CU) and D2D users (DU), reference distances between CU and DU, outage threshold for DU, user density for CU and DU upon the changes. Simulation results demonstrate the truthful impact of these parameters.
Brain Tumor Segmentation and Classification using Spatial Fuzzy C mean and Quadratic Support Vector Machine

Precise detection and classification of brain tumors at a reasonable time is vital to enhance the survival of a patient. But due to the big dimensional and anatomical diversity between brain tumors, this is a difficult job. In this contribution, we are proposing an improved spatial fuzzy c-mean (SFCM) for brain tumor segmentation and a quadratic support vector machine (QSVM) for tumor type classification. When applied to the BraTS 2018 dataset, the acquired segmentation outcomes were shown to be 7.16% and 12.78% better than the conventional fuzzy c-mean (FCM) and k-mean. This method assists radiologists in understanding the medical image and gives a second opinion.
A Joint Sparse and Correlation Induced Subspace Clustering Method for Segmentation of Natural Images

Due to the presence of diverse and disparate patterns, the segmentation of natural images endures crucial as well as a challenging problem in image analysis algorithms. The proposed method addresses image segmentation as a subspace clustering of image feature vectors. Initially, an image is partitioned into superpixels and further, a feature data matrix is computed using the Local Spectral Histogram (LSH) features from individual superpixels. A single-stage optimization model is formulated which incorporates better subspace selection, excellent grouping effect and simultaneous noise robustness for the uncorrelated, correlated and corrupted data by the conjunctive venture of l1, l2 and l2,1 norm minimization. The proposed model is solved using Augmented Lagrangian technique. We compared the proposed method with state-of-the-art methods and the results demonstrate the improved performance of our proposed model over the existing counterparts.