Miracle-dz/UniFAR-FTUs huggingface dataset:
Sediment Classification of Small-Size Seabed Acoustic Images Using Convolutional Neural Networks

Seabed acoustic images are image data mosaics derived from seafloor acoustic backscattering intensity data, which is related to the type of sediment covering the seabed. Therefore, submarine sediment classification can be realized using seabed acoustic images, and has been studied extensively. Recently, deep learning has also rapidly advanced; in particular, deep convolutional neural networks (CNNs) are now being used to achieve remarkable results in the field of image processing—showing that they are well-suited for image classification tasks. Previous studies have used GoogleNet to classify large-scale side-scan sonar data, with some sediments being well-classified. However, deep learning is data-driven and, theoretically, the greater the depth, the stronger is the learning ability of the feature. It is worth noting that the dataset used for sediment classification can sometimes be small. Hitherto, no related research has analyzed the feasibility and applicability of a CNN classifier for a small-sized seabed acoustic image dataset, so we adopted two different CNN classifier models to conduct the classification experiment in this study. As the results show, the CNN classifier can be applied to the classification of sediments based on a small-sized seabed acoustic image dataset, and the classification performance of shallow CNN was found to be better than that of the deep CNN on existing side-scan sonar data. In particular, the accuracy obtained from the results of several sediment classification experiments using a shallow CNN classifier ranged between 93.4% (Sand Wave) and 87.54% (Reef).
The Bilinear Model Predictive Method-Based Motion Control System of an Underactuated Ship with an Uncertain Model in the Disturbance

Ship transportation plays an increasingly important role in and accounts for a large proportion of cargo transport. Therefore, it is necessary to improve the quality of the trajectory control system of the ship for improving the transport efficiency and ensuring maritime safety. This paper deals with the advanced control system for the three-degrees-of-freedom model of the underactuated ship in the condition of uncertain disturbance. Based on the three-degrees-of-freedom model of the underactuated ship, the authors built a bilinear model of the ship by linearizing each nonlinear model section. Then, the authors used the state estimator to compensate for uncertain components and random disturbances in the model. Finally, the authors built the output-feedback predictive controller based on the channel-separation principle combined with direct observation of the continuous model for controlling the motion of the underactuated ship in the case of uncertain disturbance and the bound control signals. The result is that the movement quality of the underactuated ship is very good in the context of uncertain disturbance and bound control signals.
A Lean Start-up approach for developing minimum viable products in an established company

ABSTRACT The minimum viable product (MVP) is a fundamental concept of the Lean Start-up approach as it enables a company to quickly start the learning process by integrating feedback from early adopters. Although the MVP concept has evolved over the years, its application is most often reported in a start-up context, even though established companies struggle to develop MVPs. This study reports on the experience and lessons learned at Texuna, an established company, where the software product innovation team created a process map for developing MVPs. This is the first study that allows the original MVP approach to be extended and applied to established organisations.
Mathematical model of human sit-to-stand and stand-to-sit motion analysis

MATHEMATICAL MODEL OF HUMAN SIT-TO-STAND AND STAND-TO-SIT MOTION ANALYSIS by Jayeshkumar Gandhi In designing a device for an amputee, it is important to find those underlying principles which determine the normal human sit-to-stand task. For this purpose we have developed a mathematical model of human sit-to-stand movement, in which it is possible to predict the minimum mechanical energy consumption to move from the sit-to-stand position. To the best of author's knowledge, this thesis represents the first time that the periodic motion of stand-to-sit and sit-to-stand movements have been mathematically modeled by a simple mechanical system. A complex model, such as the one used by Seireg and Arvikar (1973 All that contained 31 muscles per leg, is certainly impressive from a mathematical point of view alone. However, biomechanists should always reduce as much as possible the complexity in their models. The discussions of what the appropriate level of complexity to model, this biomechanical process will probably never end. Our purpose, for this thesis, is to develop a simple mathematical model of sit-tostand motion, which can be used to understand the effects of parameter changes, and to predict the human motion that minimizes energy expenditure. This knowledge can be used to design a mechanical device for this purpose. There are very few papers which explain mechanical and muscular dynamics of rising from a seated position, but unfortunately, no one has successfully constructed a model to solve the motion by forward dynamics. MATHEMATICAL MODEL OF HUMAN SIT-TO-STAND AND STAND-TO-SIT MOTION ANALYSIS by Jayeshkumar Gandhi A Thesis Submitted to the Faculty of New jersey Institute of Technology in Partial Fulfillment of the Requirement for the Degree of Master of Science in Biomedical Engineering Biomedical Engineering Committee May 1998
Vibro-acoustics substructuring : Combining simulations and experimental identification of subdomains for low frequency vehicle acoustics

Exhaust noise has a significant impact on acoustic comfort and the sound identity of a vehicle brand. The present study focuses on the: “Description and quantification of the sound transmission from the exhaust outlet into the interior of a vehicle”. Physically the noise propagation from the exhaust pipe to the cabin consists of three steps: The sound waves propagate through the air from the exhaust outlet to the external skin of the vehicle (1); the external skin vibrates and transmits its vibration to the internal skin (2); the internal skin radiates sound in the passengers’ cabin (3). The Patch Transfer Functions method, which is based on the framework of dynamic substructuring, allows for the consideration of this complex problem as simpler subproblems that consist of subsystem interactions. Yet the application of the method to a full vehicle requires addressing the problem: “Characterization of Patch Transfer Functions of a subsystem by means of measurement on a coupled system”. This dissertation presents an original inverse method for the measurement of Patch Transfer Functions. In industrial structures, this in-situ characterization is generally the only possible measurement method. Yet, due to the ill posed nature of the problem, the inversion process is difficult. An original regularization method is proposed. The method is tested through numerical simulations, and is validated with an experimental setup.
Layer adaptation for transfer of expressivity in speech synthesis

Expressive speech synthesis using parametric approaches is constrained by the style of the speech corpus used. In this paper, we present the development of an expressive speech synthesis for a new speaker voice without requiring a specific recording of expressive speech by new speaker. We propose deep neural network based layer adaptation framework for transferring the expressive characteristics of speech to a new speaker's voice for which only neutral speech data is available. The focus of the work is on investigating transfer learning mechanism, which will accelerate the efforts towards exploiting existing expressive speech corpus. Experiments using expressive Caroline speech corpus and neutral Lisa speech corpus shows layer adaptation technique is able to transfer expressive characteristics while keeping the speaker's style characteristics.
Architecture to Detect, Track, and Classify Objects using LiDAR Measurements in Highway Scenarios

—Self-driving cars require a holistic perception of their environment. To achieve this requirement, a plethora of sensor technologies exists e.g. RGB-camera, ultra-sonic and radar. Those sensor technologies have different range, as well as resolution and behave differently with varying weather conditions. Another technology is Light Detection and Ranging (LiDAR) , which enables precise distance measurements. In combination with RGB-cameras, ultra-sonic, and radar, LiDAR closes the gap to enable the holistic perception of the environment. Due to limited experience with LiDAR sensors, there is a lack of understanding how to detect, track, and classify objects (e.g. cars, guardrails) using LiDAR data. In this paper, we propose an architecture to detect, track, and classify objects based on LiDAR measurements in highway scenarios. We evaluate our architecture using preliminary sensor data obtained from a setup including six Ibeo Lux sensors and additional a roof mounted Velodyne HDL-64E. Index
A Flexible BCH decoder for Flash Memory Systems using Cascaded BCH codes

NAND flash memories are widely used in consumer electronics, such as tablets, personal computers, smartphones, and gaming systems. However, unlike other standard storage devices, these flash memories suffer from various random errors. In order to address these reliability issues, various error correction codes (ECC) are employed. Bose-Chaudhuri Hocquenghem (BCH) code is the most common ECC used to address the errors in modern flash memories. Because of the limitation of the realization of the BCH codes for more extensive error correction, the modern flash memory devices use Low-density parity-check (LDPC) codes for error correction scheme. The realization of the LDPC decoders have greater complexity than BCH decoders, so these ECC decoders are implemented within the flash memory device. This thesis analyzes the limitation imposed by the state of the art implementation of BCH decoders and proposes a cascaded BCH code to address these limitations. In order to support a variety of flash memory devices, there are three main challenges to be addressed for BCH decoders. First, the latency of the BCH decoders, in the case of no error scenario, should be less than 100us. Second, there should be flexibility in supporting different ECC block size; more precisely, the solution should be able to support 256, 512, 1024, and 2048 bytes of ECC block. Third, there should be flexibility v in supporting different bit errors. A recent development with Graphical Processing Units (GPUs) has attracted many researchers to use GPUs for non-graphical implementation. These GPUs are used in many consumer electronics as part of the system on chip (SOC) configuration. In this thesis we studied the limitation imposed by different implementations (VLSI, GPU, and CPU) of BCH decoders, and we propose a cascaded BCH code implemented using a hybrid approach to overcome the limitations of the BCH codes. By splitting the implementation across VLSI and GPUs, we have shown in this thesis that this method can provide flexibility over the block size and the bit error to be corrected.
Hyperspectral Image Denoising using Dictionary Learning

Hyperspectral images are corrupted by noise during their acquisition. In this work, we propose to efficiently denoise hyperspectral images under two assumptions: (i) noiseless hyperspectral images in matrix form are low-rank, and (ii) image patches are sparse in a proper representation domain defined through a dictionary. These two assumptions have already led to state-of-the-art denoising methods using fixed Wavelet transforms. We propose to rather learn the dictionary from hyperspectral images, a task commonly known as dictionary learning. We show that the dictionary learning approach is more efficient to denoise hyperspectral images than state-of-the-art methods with fixed dictionaries, at the cost of a larger computation time.
Visualization of Crime News Sentiment in Facebook

Facebook has become a popular platform in communicating information. People can express their opinions using texts, symbols, pictures and emoticons via Facebook posts and comments. These expressions allow sentiment analysis to be performed by collecting the data to obtain the public’s opinions and emotions toward certain issues. Due to a huge amount of data obtained from Facebook, proper approaches are required to cater the texts and symbols used in the comments. There are also limited amount of dictionary on Malay texts which make it more challenging to process and classify the positive and negative words used in the comments. Thus, hybrid approach is applied during the data processing to visualize the results. In this work, a combination of lexicon-based approach and Naïve Bayes are used. This study focuses on analyzing the public’s sentiments on crime news in Facebook by using word cloud visualization. The visualization displays important words used in a form of a word cloud. Moreover, the percentage of positive and negative words existed in the comments is also shown as part of the visualization results.
Where Are You Going Big Data?

Big data applications combined with analytical tools foster prediction techniques that impact societal, economic, and political changes. After almost one decade of studies, this paper proposes to carry out a literature review on big data analytics (BDA), recognizing major debates in the topic, presenting its evolution over the past years, and identifying its research tendencies. We delimit our research to the eight top journals on information systems. Out of the 135 candidates in the initial pool, we selected 41 papers that met the selection criteria. Our finding suggests that BDA is apparently reaching a plateau, which might be confirmed by the following years of publications. Additionally, other perspectives on BDA might include a new wave of studies; and finally we conclude that new paths can be explored beyond productivity gain, but from a social perspective.
Semantically Enriched Simplification of Trajectories

Abstract. Moving objects that are equipped with GPS devices generate huge volumes of spatio-temporal data. This spatial and temporal information is used in tracing the path travelled by the object, so called trajectory. It is often difficult to handle this massive data as it contains millions of raw data points. The number of points in a trajectory is reduced by trajectory simplification techniques. While most of the simplification algorithms use the distance offset as a criterion to eliminate the redundant points, temporal dimension in trajectories should also be considered in retaining the points which convey both the spatial and temporal characteristics of the trajectory. In addition to that the simplification process may result in losing the semantics associated with the intermediate points on the original trajectories. These intermediate points can contain attributes or characteristics depending on the application domain. For example, a trajectory of a moving vessel can contain information about distance travelled, bearing, and current speed. This paper involves implementing the Synchronized Euclidean Distance (SED) based simplification to consider the temporal dimension and building the Semantically Enriched Line simpliFication(SELF) data structure to preserve the semantic attributes associated to individual points on actual trajectories. The SED based simplification technique and the SELF data structure have been implemented in PostgreSQL 9.4 with PostGIS extension using PL/pgSQL to support dynamic lines. Extended experimental work has been carried out to better understand the impact of SED based simplification over conventional Douglas-Peucker algorithm to both synthetic and real trajectories. The efficiency of SELF structure in regard to semantic preservation has been tested at different levels of simplification.
On the Enhancement of Intelligibility: Investigating the influence of different speech modifications on the intelligibility of speech in near-end noise

Several algorithms to enhance the intelligibility of speech in near-end noise were analyzed and implemented. The algorithms considered were assessed based on the intrusive instrumental intelligibility metric SIIB_Gauss. An implementation based on the direct optimization for this metric is assessed, as well as an implementation based on human induced speech modifications, including increased sound intensity, flattening of the spectral tilt, increased vowel duration and increased consonant-vowel ratio. Another implemented algorithm is the amplification of the transient component of speech. Results show that for increased vowel duration a decrease in intelligibility was found in SIIB_Gauss value as well as in informal listening tests. The other implementations did show an increase in intelligibility according to SIIB_Gauss at SNRs between -4 dB and 6 dB in both stationary and fluctuating noise, under a power constraint. Finally, the implementations were combined into a system that automatically selects the optimal algorithm to use under the given noise conditions. It is shown that this combined system is able to increase intelligibility of speech in the presence of non-fluctuating noise, fluctuating noise, speech shaped noise, and competing speaker noise.
ISO/IEC JTC1/SC29/WG11 MPEG2014/ m34661: Quality Assessment of High Dynamic Range (HDR) Video Content Using Existing Full-Reference Metrics

The main focus of this document is to evaluate the performance of the existing LDR and HDR metrics on HDR video content which in turn will allow for a better understanding of how well each of these metrics work and if they can be applied in capturing, compressing, transmitting process of HDR data. To this end a series of subjective tests is performed to evaluate the quality of DML-HDR video database [1], when several different representing types of artifacts are present using a HDR display. Then, the correlation between the results from the existing LDR and HDR quality metrics and those from subjective tests is measured to determine the most effective exiting quality metric for HDR.
Factors influencing people's continuous watching intention and consumption intention in live streaming

Purpose The purpose of this paper is to investigate what factors can affect people’s continuous watching and consumption intentions in live streaming. Design/methodology/approach This research conducted a mixed-methods study. The semi-structured interview was deployed to develop a research model and a live streaming typology. A survey was then used for quantitative assessment of the research model. Survey data were analyzed using partial least squares-structural equation modeling. Findings The results suggest that sex and humor appeals, social status display and interactivity play considerable roles in the viewer’s behavioral intentions in live streaming and their effects vary across different live streaming types. Research limitations/implications This research is conducted in the Chinese context. Future research can test the research model in other cultural contexts. This study can also be extended by incorporating the roles of viewer gender and price sensitivity in the future. Practical implications This study provides managerial insights into how live streaming platforms and streamers can improve their popularity and profitability. Originality/value The paper introduces a novel form of social media and a new business model. It illustrates what will affect people’s behavioral intentions in such a new context.
Review on Security Based Vehicular Ad-Hoc Network

VANET a vehicular ad-hoc network provides the security to vehicles. To provide security in VANET, we are using Elliptical curve cryptography. In this paper we are going to discuss various techniques of cryptography, the techniques such are Advanced Encryption Standard, Data Encryption Standard, and Triple Data Encryption Standard. These techniques are part of symmetric and asymmetric cryptography. In addition to this there are some attacks which we are discussing in this paper like, masquerade attack, replay attack, insider attack, mutual authentication attack, and parallel session attack. These attacks are on different layer. At the end we are comparing these attacks with each other and compare their quality of services.
Convolutional neural network-based registration for mosaicing of microscopic images

Abstract. To obtain wider field of view through optical microscopes, we proposed an image mosaicing method based on the convolutional neural network (CNN). The CNN was designed to discriminate the corresponding patches in a pair of images. To train the CNN, a training set that contained image patches and their matching labels was created from an open-access database. The pretrained CNN was then fine-tuned by self-learning from the target image and its transformed images. With the proposed self-learning CNN, the corresponding feature points were detected for the registration in mosaicing. The proposed method was compared with the scale-invariant feature transform detector-based and speed-up robust feature detector-based methods for mosaicing of 30 pairs of microscopic images. The proposed method outperformed the two traditional methods in terms of both visual quality and objective assessment. Results demonstrate that using the self-learning CNN can improve the accuracy of registration in image mosaicing.
A Novel Model Predictive Control via Optimized Vector Selection Method for Common-Mode Voltage Reduction of Three-Phase Inverters

In this paper, a novel model predictive control (MPC) via a double vector optimized selection method is proposed. The main idea of this method is for decreasing the high common-mode voltage of two-level inverter under balance situation, improve the tracking performance of the output current and decrease the high output current harmonic distortion rate. To solve the problem of increasing the value of total harmonic distortion (THD) due to the reduction of the available voltage vector, this method controls two voltage vectors in each sampling period for getting a better tracking performance of the output current. Simultaneously, compared with the adding virtual vector MPC method, which results in excessive switching losses, the proposed method defining the selection range of the second voltage vector. Only two voltage vectors adjacent to the first voltage vector can be selected so that the switching frequency can be greatly reduced. An experimental control platform based on Simulink-Real-Time system is presented for the further verification of the effectiveness of the proposed control method. The simulation and experimental results showed that the proposed method could effectively reduce the output common-mode voltage of the inverter. Simultaneously, the THD value and the switching loss are greatly reduced.