Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative blind separation of Gaussian data of unknown order

A method for blind separation of noisy jointly Gaussian multivariate signals X is presented, where X = AP + N, X is an observed set of vectors, A is the mixing matrix, P is the unknown signal matrix, and N is white noise. The objective is to estimate all matrices on the right-hand side when even their dimensions (the system order) are unknown. The algorithms developed are extensions of the Iterative Order and Noise (ION) estimation algorithm [10]. Improvements made within the iterative structure of ION to better estimate the order and noise yield ION'. The addition of a second-order blind identification algorithm (SOBI, [4]) subsequently yields ONA, which fully characterizes a data set by estimating the (O)rder, (N)oise, and mixing matrix (A). Metrics are developed to evaluate the performance of these algorithms, and their applicability is discussed. Optimum algorithm constants for ION' and ONA are derived, and their range of applicability is outlined. The algorithms are evaluated through application to three types of data: (1) simulated Gaussian data which spans the problem space, (2) a set of non-Gaussian factory data with 577 variables, and (3) a hyperspectral image with 224 channels. The ONA algorithm is extended to 2D (spatial) hyperspectral problems by exploiting spatial rather than time correlation. ONA produces a full characterization of the data with high signal-to-noise ratios for most unknown parameters in the Gaussian case, though the jointly Gaussian P is shown to be most difficult to retrieve. In all three cases, ONA reduces the noise in the data, identifies small sets of highly correlated variables, and unmixes latent signals. The spatial ONA identifies surface features in the hyperspectral image and retrieves sources significantly more independent than those retrieved by PCA. Further exploration of the applicability of these algorithms to other types of data and further algorithmic improvement is recommended. Thesis Supervisor: David H. Staelin Title: Professor of Electrical Engineering
Optimized Codebook Based Hybrid Beamforming

In this thesis we research beamforming capabilities of the UEs equipped with several antennas in a 5G scenario. We define a system model based on the use of phase codebooks that allow to change the radiation patterns of the arrays of antennas installed in the UE. Within this system model, we obtain optimized codebooks in terms of two metrics, which we consider to be of interest in this scenario, employing two different optimization approaches. We use these results to analyze, through simulations, the estimation performance in an AoA estimation scenario. We also analyze the detection performance in receiving the signal that can allow us to estimate the AoA. So as to gain more insight into how the selection of the optimum codebook affects the detection performance, we define a simpler scenario and we analyze it theoretically and draw conclusions that relate to the simulations obtained. (Less)
Work Paper : Code Optimization using Refactoring

Refactoring is a formal and mechanical process, used to modify existing code in such a way that it does indeed become 'better' while preserving the program's intended functionality. In addition to improving a program's overall design, the refactoring process tends to yield code which is far easier to maintain and extend in the long run.It is widely believed that refactoring improves software quality and developer productivity. This paper explains the benefits of refactoring by applying combinations of refactoring methods on an unoptimized code and identify which combination of refactoring methods results in better optimization of code. Further the paper discuses works done by various people on refactoring, various code optimization techniques , classification of refactoring techniques based on functionality, code optimization tools and benefits and challenges of refactoring. The paper discusses the results and conclusions are drawn on the basis of results. The paper concludes with identification of future scope of work.
A Method of Reducing Switch Count in Three-Level NPC Inverter – Analysis in Steady States

Abstract The proof of a concept for a new method of modulation has been presented which reduce switch count in a three-level neutral point clamped (3L-NPC) inverter. The method is an implementation of space vector modulation (SVM) by means of a prediction algorithm and sequences of transistors, which are not common in use. Those sequences make active use of clamping diodes of the inverter. The prediction algorithm analyzes possible sequences of transistors’ states and choose those which offers smaller switch count. Measurements of steady states were taken on prototype 3L-NPC.
Efficient Rendering of Scenes with Dynamic Lighting Using a Photons Queue and Incremental Update Algorithm

Photon mapping is a popular extension to the classic ray tracing algorithm in the ﬁeld of realistic image synthesis. Moreover, it beneﬁts from the massive parallelism computational power brought by recent developments in graphics processor hardwareand programming models. However rendering the scenes with dynamic lights stillgreatly limits the performance due to the re-construction at each rendered frame ofa kd-tree for the photons. We developed a novel approach based on the idea that storing the photons data along with the kd-tree leaf nodes data and implemented new incremental update scheme to improve the performance for dynamic lighting. The implementation is GPU-based and fully parallelized. A series of benchmarks with the prevalent existing GPU photon mapping technique is carried out to evaluate our approach. Our new technique is shown to be faster when handling scenes with dynamic lights than the existing technique while having the same image quality.
INFORMATION EXTRACTION FOR NOTES GENERATION

For a specific topic we find many reference books and textbooks to refer and the content is much more than one needs, therefore at the time of revision or quick study we need the summary and short version of the whole content for revision, especially at the time of examinations. Therefore we aim to develop a semi-automated technique to generate notes from English text documents like Reference Books and Text books. The technique discussed is considered to be a pioneering attempt in the field of NLP (Natural Language Processing). This technique has a wide scope in the educational domain. The technique when implemented as an application can be used by both faculty members and students.
Increasing junior students’ learning outcome using systematic approach to problem solving

A learning program not only aims to make students understand and master what and how things happen, but also provide an understanding of why it happened. Thus, a lesson that emphasizes problem-solving becomes very important to teach. One form of learning that emphasizes the problem solving is to apply systematic approach to problem solving. This is a guide to perform an action that serves to assist a person in solving a problem. Problem solving steps based on systematic approach to problem solving consists of four stages, namely problem analysis, problem solving process planning, calculation operations, and checking answers and interpretation of results. This study is a classroom action research that aims to see the inceasing of student learning outcomes after applied systematic approach to problem solving. The subject of this research is 25 students of class VIIIA MTs Salafiyah Syafi'iyah Tebuireng Jombang. Instruments in this study is a matter of student learning outcomes on the material wake up space. This research was conducted in two cycles because in the second cycle has reached the indicator of success that students achieve the minimal clarity of at least 75%. The results showed that student learning outcomes in the first cycle reached 36% classical completeness, and in the second cycle of classical completeness of 84%. This shows that the application of systematic approach to problem solving can increase student learning outcomes.
Generation of fixed‐length search codes from INSPEC words using techniques based on variety generation and on division‐hashing

The use of variety generation techniques in the production of fixed‐length degenerate representations for search terms is compared with methods based on division‐hashing. For files of words taken from INSPEC data, the latter perform better, almost certainly because of dependence between assignments of symbol sets. Attempts to overcome the problem produced no real improvement, possibly because the decrease in dependence was accompanied by an increased skewness in the distribution of symbols.
PRELIMINARY EVALUATION OF A COMMERCIAL 360 MULTI-CAMERA RIG FOR PHOTOGRAMMETRIC PURPOSES

Abstract. The research presented in this paper is focused on a preliminary evaluation of a 360 multi-camera rig: the possibilities to use the images acquired by the system in a photogrammetric workflow and for the creation of spherical images are investigated and different tests and analyses are reported. Particular attention is dedicated to different operative approaches for the estimation of the interior orientation parameters of the cameras, both from an operative and theoretical point of view. The consistency of the six cameras that compose the 360 system was in depth analysed adopting a self-calibration approach in a commercial photogrammetric software solution. A 3D calibration field was projected and created, and several topographic measurements were performed in order to have a set of control points to enhance and control the photogrammetric process. The influence of the interior parameters of the six cameras were analyse both in the different phases of the photogrammetric workflow (reprojection errors on the single tie point, dense cloud generation, geometrical description of the surveyed object, etc.), both in the stitching of the different images into a single spherical panorama (some consideration on the influence of the camera parameters on the overall quality of the spherical image are reported also in these section).
Sensor Fusion with Out-of-Sequence Measurements - Localization in an Agricultural Robot Using Visual Odometry

Timing is an important aspect when working with visual odometry (VO). The time of processing and data transfer of image based measurements are significantly longer than IMU, wheel encoders, and GPS measurements. This introduces an arrival latency, causing the VO measurements to require delay fusion, a subcategory of out-of-sequence measurement (OOSM) fusion. In cooperation with Adigo AS this thesis has focused on the Asterix project, where an agricultural robot uses a downward facing camera for visual-inertial odometry to aid in localization. The main focus in this work is with the delay fusion problem. By approaching the OOSM fusion with a Bayesian framework, the theory chapter presents a method of fusing delayed displacement measurements. This can be considered a generalization of the stochastic cloning approach. A byproduct of the investigation is an unscented multiple-point smoother capable of defining fixed-points to be smoothed on demand. Simulations and experiments showed that the OOSM fusion methods worked, but model inconsistencies and inaccuracies in the VO measurements negatively affected the results.
Applications of evolutionary computation in electric power systems

This survey covers the broad area of evolutionary computation applications to optimization, model identification, and control in power systems. Due to space limitation, all reviewed papers have been selected since 1996, from IEEE Transactions only. A total of 85 articles are listed in this survey. It shows the development of the area and identifies current trends. The following techniques are considered under the scope of evolutionary computation: evolutionary algorithms (e.g., genetic algorithms, evolution strategies, evolutionary programming, and genetic programming), simulated annealing, tabu search, and particle swarm optimization.
Airborne Kinematic Positioning and Attitude Determination Without Base Stations

One of the limitations of using integrated inertial/GPS systems to Directly Georeference airborne sensor data (c.f., Mostafa and Hutton, 2001), is the necessity of using a GPS base station or stations in order to obtain the positional accuracy required to meet the accuracy standards of certain mapping products. In some precise large-scale aerial survey applications having to place a number of GPS base stations in a remote or inaccessible location becomes quite problematic. In some other occasions, such as in a real production environment, the GPS base station data is lost. In this paper, the potential of using GPS with no base station for aerial survey applications is discussed. The NGS CORS network stations are processed in conjunction with the airborne GPS raw observables to determine the aircraft position which is then used to aid the inertial data processing in a closed loop fashion to end up with a full resolution of the trajectory parameters, namely position, velocity and attitude, which are then used to generate Exterior Orientation data to support aerial mapping. Flight test data were collected in January 2001 using the Applanix POS/AV system (S/A is off). Test results and analysis are presented in some detail.
Reflections on pitching research: Do it your own way

This pitch letter provides a reflection of a student’s personal experience of completing Faff (2017)’s reverse engineered pitch. The reflection describes Faff’s pitching template as a learning and self-developmental tool. As a learning tool, the pitching template focuses on building technical skills as a researcher. However, as a self-developmental tool, the pitching template creates opportunities for discovering who the pitcher is and aspires to be as a researcher. The underlying message of this reflection depicts that no one pitch is the same. Hence, pitching research enables the pitcher to ‘do it their own way’, from their idiosyncratic perspective of the research process.
Scattered simultaneous and autonomous access to scrambled cloud databases

Cloud data environments square measure unpleasantly tempting for the preparing of tremendous scale applications owing to their amazingly ascendible and offered foundation. Data as a Service (DBaaS) model is utilized to oversee databases in cloud setting. Secure DBaaS standard gives data classification to distributed storage. Secure DBaaS is expected to allow various and independent buyers to append to the cloud while not middle server. Documents, data structures and data square measure encoded before exchange to the cloud. Different cryptography methods square measure won't to change over plain content into scrambled data. Table names and their section names likewise are scrambled inside of the cloud data wellbeing subject. The framework underpins topographically conveyed buyers to append on to A scrambled cloud data. Amid this paper we tend to quadrangular measure proposing new plan that coordinate distributed storage administration with data protection and have a component of flogging co-happening operations on scrambled data and together with the topographically dispersed buyers to connect on to these cloud data that is encoded and that they conjointly given to execute their operations over the cloud data. This configuration disposes of the dealers (Intermediate intermediaries) it constrains the quantifiability, flexibility, availability. High touchy data square measure encoded by RSA cryptography and standard data square measure scrambled abuse AES method so overhead on the system will be decreased
Implementation of Deep Learning in Spatial Multiplexing MIMO Communication

Research in Multiple Input Multiple Output (MIMO) communication system has been developed rapidly in order to improve the effectiveness of communication among users. However, trade-off phenomenon between performance and computational complexity always become the hugest dilemma suffered by researchers. As an alternative solution, this paper proposes an optimization in 3x3 spatial multiplexing MIMO communication system using end-to-end based learning, specifically, it adapts autoencoder based model with the knowledge of Channel State Information (CSI) in the receiver side, make it fairly compared with the baseline method. The proposed models were evaluated in one of the most common channel impairment which is fast Rayleigh fading with additional Additive White Gaussian Noise (AWGN). By appropriately determining hyperparameters and the help of PReLU (Parametric Rectified Linear Unit), the results show that this autoencoder based MIMO communication system results in very promising results by exceeding the baseline methods (methods widely used in conventional MIMO communication) by reaching BER lower than at SNR 22.5 dB.
An efficient Uniform Integrated Advection algorithm for Finite Time Lyapunov Exponent field computation on GPU and MIC

Finite Time Lyapunov Exponent (FTLE) is widely used in Lagrangian Coherent Structure extraction and research on unsteady flow field. FTLE is computationally expensive due to the flow map calculation on dense samples in the field. In order to improve the computation efficiency, we investigate the most time-consuming flow map and propose UIA, a Uniform Integrated Advection algorithm based on piecewise linear hypothesis. FTLE coverts multiple spatiotemporal interpolations and multi-step time advection into single matrix multiplication so as to dramatically reduce computation and be applied to any type of grids. We also design an UIA based FTLE algorithm on GUP and MIC to further reduce computation time, and present corresponding performance optimization strategies. The correctness and accuracy of algorithm are also experimentally verified by applying the algorithm to an analytical 3-dimensional field. The speedup ratio of the proposed algorithm ranges from 6 to 143 compared with FTLE execution time based on traditional RK4 flow map, which demonstrates that UIA has significantly improve the efficiency of FTLE computation and visualization.
Study of Deep Learning Algorithms for Automatic License Plate Recognition (ALPR)

: Automatic License Plate Recognition (ALPR) systems obtain a vehicle’s license plate and recognize the license plate number and other required information from the image. ALPR systems have number of significant applications: law enforcement, public safety agencies, toll gate systems, etc. The goal is to recognize the characters on the license plate with high accuracy. ALPR has been implemented using various techniques. In traditional recognition methods, features are obtained from the image manually. Unlike conventional methods, deep learning techniques automatically select features and are one of the significant technologies in the field of computer vision, automatic recognition tasks and natural language processing. In this paper we have discussed some of the character recognition methodologies like Convolutional neural network (CNN), feedforward Artificial Neural Network (ANN),
Reliability Analysis of Sealing Structure of Electromechanical System Based on Kriging Model

The sealing performance of aircraft electromechanical system has a great influence on flight safety, and the reliability of its typical seal structure is analyzed by researcher. In this paper, we regard reciprocating seal structure as a research object to study structural reliability. Having been based on the finite element numerical simulation method, the contact stress between the rubber sealing ring and the cylinder wall is calculated, and the relationship between the contact stress and the pressure of the hydraulic medium is built, and the friction force on different working conditions are compared. Through the co-simulation, the adaptive Kriging model obtained by EFF learning mechanism is used to describe the failure probability of the seal ring, so as to evaluate the reliability of the sealing structure. This article proposes a new idea of numerical evaluation for the reliability analysis of sealing structure, and also provides a theoretical basis for the optimal design of sealing structure.
Fault Tolerance for Main-memory applications in the cloud

Advances in hardware have enabled many long-running applications to execute entirely in main memory. With the emergence of cloud computing, thousands of machines could be made available to deploy such applications with lowered operational and maintenance costs. While achieving substantially better performance, these applications have encountered new challenges in achieving fault tolerance; i.e., to ensure durability in the event of a crash. In addition, many of these applications, such as massively multiplayer online games, main-memory OLTP systems, main-memory search engine and deterministic transaction processing systems, must sustain extremely high update rates – often hundreds of thousands of updates per second. They also demand extremely high throughput (e.g. scientific simulation) or low latency (e.g. massively multiplayer online games). To support these demanding requirements, these applications have increasingly turned to database techniques. In this dissertation, we propose an approach to provide fault tolerance for main-memory applications without introducing excessive overhead or latency spikes. First, we evaluate the applicability of existing checkpoint recovery techniques developed for main-memory DBMS. We use massively multiplayer online games (MMOs) as our motivating example. In particular, we show how to adapt consistent checkpointing techniques developed for main-memory databases to MMOs. Furthermore, we provide a thorough simulation model and evaluation of six recovery strategies. Based on our results, we argue that not all state-of-the-art checkpoint recovery techniques are equally suited for low-latency and high-throughput applications such as MMOs. These algorithms either use locks or large synchronous copy operations, which hurt throughput and latency, respectively. Next, we take advantage of frequent points of consistency in many of these applications to develop novel checkpoint recovery algorithms that trade additional space in main memory for significantly lower overhead and latency. Compared to previous work, our new algorithms do not require any locking or bulk copies of the application state. Our experimental evaluation shows that one of our new algorithms attains nearly constant latency and reduces overhead by more than an order of magnitude for low to medium update rates. Additionally, in a heavily loaded main-memory transaction processing system, it still reduces overhead by more than a factor of two. Finally, we present BRRL, a library for making distributed main-memory applications fault tolerant. BRRL is optimized for cloud applications with frequent points of consistency that use data-parallelism to avoid complex concurrency control mechanisms. BRRL differs from existing recovery libraries by providing a simple table abstraction and using schema information to optimize checkpointing.