Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and compensation of a non-ideal I/Q-modulator

Nowadays, I/Q modulators are widely utilized generating the source signals for wired and wireless communication system. Thus, a high quality of the I/Q modulator is especially crucial in order to assure high SNR at the receiver. In this work, the tests were made with a 8MHz bandpass noise signal, which has statistical properties similar to modern communications signals. We firstly did identification for the system, and then used postand pre-compensation to compensate for the non-idealities experienced in a practical I/Q modulator. The post-compensation is used at the receiver side. There are two main techniques used in the post-compensation algorithm that we developed. First, a linear Wiener filter is used to compensate for the effects of imbalance and memory in the I/Q modulator. After that, the nonlinearity is compensated by a memoryless AM/AM polynomial. On the other hand, the pre-compensation is used at the transmitter side. While knowing the compensation filters’ coefficients, pre-compensation can be applied to compensate any other signals before they are transmitted. As for the results we achieved, SNR after linear compensation rises 6 dB to 45.5 dB, comparing with 39.5 dB without any compensation. Furthermore, nonlinear polynomial estimation improves the SNR to 48 dB. Using pre-distortion, the SNR at receiver reaches 44 dB.
Opinion Mining From Text Reviews UsingMachine Learning Algorithm

Social media is one of the biggest forums to express opinions. sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes.Sentiment analysis is also known as opinion mining. Opinion Mining is to analyse and classify the user generated data like reviews, blogs, comments, articles etc. The main objective of Opinion mining is Sentiment Classification i.e. to classify the opinion into positive or negative classes. A earlier work is based on star rating of user data, most of the reviews are written in text format. The reviews are in text format which is difficult for computer system to understand. The proposed work is able to collect information from various sites and perform a sentiment analysis of a user reviews based on that information to rank a product. Manual analysis of such large number of reviews is impossible. So the automated approach of machine learning algorithm like Naive Bayesian Algorithm and Support Vector Machine is used.
Exploring neural networks for gesture recognition

Gesture recognition is the task of recognizing human gestures from video data. In this report, we discuss the ChaLearn LAP Isolated Gesture Dataset and different methods used for gesture recognition from RGB-D videos. We also propose three new methods each involving neural networks in some capacity. The first method uses a pre-trained 3D ConvNet model for feature extraction. The second method uses spatio-temporal interest points and unsupervised learning followed by an LSTM network for prediction. The third method describes the generation of composite difference images that represent the video and then uses 2D ConvNets for prediction. We discuss merits and demerits of each method.
Bringing Research and New Technology into the Undergraduate Curriculum: A Course in Computational Fluid Dynamics

As technology advances in the industries which graduating engineers wish to enter, technology in the undergraduate curriculum must also advance. A course in computational fluid dynamics was recently developed which meets the challenge of bringing advanced topics to undergraduate students. This paper addresses techniques used to enable undergraduates to enter the work force with the ability to solve and physically understand fluid dynamics problems requiring commercially available computational fluid dynamics codes and related software. Student projects involving grid generation, the solution to two-dimensional and three-dimensional problems, and the solution to multi-dimensional species flow problems are presented. Additionally, final term projects obtained from the students' cooperative employers are discussed. Introduction Advances in engineering technology has led to the development of commercially available software packages which allow for the solution of complicated engineering problems. Graduating engineers must be prepared to successfully use these tools upon entering the work force. A fundamental understanding of the mathematics, physics, and numerical considerations behind the development of these codes is often only possible when the user pursues advanced engineering degrees beyond the baccalaureate level. Undergraduate students often use software packages blindly assuming the computer will never produce incorrect solutions. Students need to understand the phenomenon modeled by the software packages and to have expectations of the problem solutions in order to detect unrealistic and incorrect results sometimes produced by inexperienced software users. Educators must also enable the students to develop a fundamental understanding of the behavior of the equations solved by the software packages without going into the details required in most graduate classes. The students must also develop the ability to run the necessary software packages including any required preprocessing and post processing software. These abilities must be developed to some degree in a single course since most undergraduate engineering curricula have few free electives. Bringing advanced topics into the undergraduate curricula has been pursued at other institutions in an effort to enable students to work in areas which, in the past, were typically reserved for individuals with advanced degrees. Some topics are reserved for graduate work simply because of the lack of time in the undergraduate curricula. Examples of these programs may be found in Goddard (1995) and Wendlandt and Harrison (1995).
Concatenated error correction coding for high-order PRML channels

A modified E/sup 2/PRML (ME/sup 2/PRML) channel is optimized for high-density magnetic recording in order to reduce long error-propagation events in an ML detector. A cyclic redundancy-check error-correction code (CRCC), an innerblock ECC concatenated to the ML detector, is introduced as a new ECC-coding scheme for the high-order partial response maximum likelihood (PRML) channel. The CRCC coding can efficiently improve the BER performance with only a few redundant check-bits which intensively correct most-dominant short error-events in the ME/sup 2/PRML detector. A 16/17 coded ME/sup 2/PRML channel in conjunction with a 2.9% redundant CRCC coding can achieve a gain of more than 35 dB over a conventional 16/17 coded EPRML channel at a user density of 2.8.
Pengukuran Efisiensi Kinerja Sekolah Dasar Lewat Model Data Envelopment Analysis

Abstrak: Penelitian ini bertujuan untuk mengukur efisiensi kinerja Sekolah Dasar di DKI Jakarta khususnya kabupaten Jakarta Pusat dengan menggunakan pendekatan data envelopment analysis (DEA). DEA adalah suatu metode nonparametrik untuk mengukur efisiensi suatu unit pengambilan keputusan Decision Making Units (DMU). DEA membandingkan beberapa DMU yang bersifat homogen berdasarkan sejumlah input untuk menghasilkan suatu output yang diharapkan. Penelitian ini menggunakan metode deskriptif dengan menggunakan DMU sebanyak 103 SD Negeridi Jakarta Pusat yang terakreditasi A dengan tiga input dan empat output. Data dianalisis menggunakan aplikasi DEAP versi 2.1 dengan membandingkan model Constant Returns to Scale (CRS) dan model Variable Returns to Scale (VRS). Hasil penelitian menunjukkan bahwa: 1) pada model CRS, terdapat 8 SDN (7,77%) yang memiliki kinerja efisien, sedangkan pada model VRS terdapat 14 SDN (13,59%) yang memiliki kinerja efisien; 2) model VRS lebih baik dibanding model CRS dalam mengukur efisiensi kinerja SD. Kata Kunci: DEA, Efisiensi Kinerja Sekolah, CRS, VRS DATA ENVELOPMENT ANALYSIS (DEA): MEASUREMENT OF THE PERFORMANCE EFFICIENCY OF ELEMENTARY SCHOOLS Abstract: This study measures the performance efficiency of elementary schools in Special Capital Region of Jakarta, especially Central Jakarta district by using data envelopment analysis (DEA) approach. DEA is a non-parametric method to measure efficiency of decision making units (DMUs). DEA compares several homogeneous DMUs based on a number of inputs to produce the expected outputs. This study deployed descriptive method by using DMU as many as 103 public elementary schools that were A-accredited with three inputs and four outputs. Data was analyzed using DEAP version 2.1 application by comparing CRS (Constant Returns to Scale) model and VRS (Variable Returns to Scale) model. Results show that: 1) in CRS model, there are 8 public elementary schools (7.77 percent) which have efficient performances while in VRS model there are 14 public elementary schools (13.59 percent) which have efficient performances; 2) VRS model is better than CRS model in measuring the efficiency performance of public elementary schools. Key Words: DEA, Efficiency of School Performance, CRS, VRS
Heuristic Approach for Specially Structured Two Stage Flow Shop Scheduling to Minimize the Rental Cost, Processing Time, Set Up Time Are Associated with Their Probabilities Including Transportation Time and Job Weightage

The present paper is an attempt to develop a new heuristic algorithm, find the optimal sequence to minimize the utilization time of the machines and hence their rental cost for two stage specially structured flow shop scheduling under specified rental policy in which processing times and set up time are associated with their respective probabilities including transportation time. Further jobs are attached with weights to indicate their relative importance. The proposed method is very simple and easy to understand and also provide an important tool for the decision maker. Algorithm is justified by numerical illustration.
Robust Traction Control and Path Planning Algorithms for Planetary Micro-rover Swarms

There is extensive research into the collaborative operation of terrestrial mobile robots inspired by biological swarms. Often these studies consider only point-mass models that do not capture the actual dynamics of the robots. As a result, the adaptation of these nature-inspired technologies for planetary surface exploration rovers is extremely challenging. This study will further advance the state-of-the-art of path planning and control of micro-rovers swarms for future planetary exploration missions. This paper presents an innovative robust traction controller and path planning methodology to enable future micro-rover swarm missions. A new traction control algorithm has been developed incorporating the dynamic model of swarms using sliding mode control. Further, the artificial potential field method is used for designing the trajectory controller cascaded with the traction controller. Furthermore, the problem of obstacle avoidance and agent-to-agent collision avoidance is presented using a navigation function that is suitable for individual robot trajectory control from the initial positions to the final positions.
Human Object Interaction Recognition

In this thesis, we have investigated the human object interaction recognition by using the skeleton data and local depth information provided by RGB-D sensors. There are two main applications we address in this thesis: human object interaction recognition and abnormal activity recognition. First, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. In the spatial modeling of human object interactions, we propose low-level feature and object related distance feature which adopted on on-line human object interaction recognition and abnormal gait detection. Then, we propose object feature, a rough description of the object shape and size as new features to model human-object interactions. This object feature is fused with the low-level feature for online human object interaction recognition. In the temporal modeling of human object interactions, we proposed a shape analysis framework based on low-level feature and object related distance feature for full sequence-based off-line recognition. Experiments carried out on two representative benchmarks demonstrate the proposed method are effective and discriminative for human object interaction analysis. Second, we extend the study to abnormal gait detection by using the on-line framework of human object interaction classification. The experiments conducted following state-of-the-art settings on the benchmark shows the effectiveness of proposed method. Finally, we collected a multi-view human object interaction dataset involving abnormal and normal human behaviors by RGB-D sensors. We test our model on the new dataset and evaluate the potential of the proposed approach.
Efficient & Lock-Free Modified Skip List in Concurrent Environment

In this era the trend of increasing software demands continues consistently, the traditional approach of faster processes comes to an end, forcing major processor manufactures to turn to multi-threading and multi-core architectures, in what is called the concurrency revolution. At the heart of many concurrent applications lie concurrent data structures. Concurrent data structures coordinate access to shared resources; implementing them is hard. The main goal of this paper is to provide an efficient and practical lock-free implementation of modified skip list data structure. That is suitable for both fully concurrent (large multi-processor) systems as well as pre-emptive (multi-process) systems. The algorithms for concurrent MSL based on mutual exclusion, Causes blocking which has several drawbacks and degrades the system’s overall performance. Non-blocking algorithms avoid blocking, and are either lock-free or wait-free.
Kinematic and dynamic analysis of the functional reach test in elderly subjects using force-plate measurements

Presents the results of a research activity aimed at the evaluation of the motor performance in the elderly, by means of objective, quantitative measures of balance and mobility, and at the study of the related motor control. A particular motor task, the functional reach (FR) test, has been adopted. Literature has shown, statistically, that this test has predictive value in identifying elderly subjects at risk of recurrent falls. The FR test has been analysed from a biomechanical point of view in order to put in evidence differences between the young and elderly populations. The experimental protocol is based on the use of low-cost instrumentation (one dynamometric platform) and a simple experimental test procedure, allowing its application not only in specialised movement analysis laboratories but also in ambulatory environment. A 2D, two joint, three segment model has been adopted. An optimisation algorithm for the estimation of the kinematic quantities of the mechanical model has been developed. The results of the estimates have been compared with the measures obtained by an optoelectronic stereometric system for validation purpose. The results refer to 24 healthy elderly and 15 young subjects. They evidenced significative differences in the motor performances and in the kinematic patterns adopted. As expected, the elderly population resulted characterised by a worse performance, by a more "cautious" attitude and by a less efficient motor control.
Recent advancements of nurse schedulingmodels and a potential path

Nurse scheduling problem (NSP) has been a subject that is being studied broadly by both academician and practitioners, over the years. Nurse scheduling concepts are varied due to sophisticated and challenging real world scenarios in nurse management system. Moreover, the importance of solving nurse scheduling problems has triggered various approaches or techniques to solve these scheduling problems.In order to develop a good nurse scheduling model, it is crucial to comprehend the problem and the characteristics of the potential techniques so as to cope with the complex scheduling problem.Hence, the main purpose of this paper is to provide a rich literature on the diverse recent solution techniques in the NSP. Subsequently, an evolutionary trend of nurse scheduling models is identified as fruitful and thus, future directions of the scheduling technique are discussed. Finally, a potential algorithm is proposed to solve a nurse scheduling problem
Designing Tabletop and Surface Applications Using Interactive Prototypes

ii Acknowledgments. iii List of Figures . ix List of Abreviations . xii List of Tables . xiii PUBLICATIONS . xiv Chapter One : Introduction . 1 1.1 Research Goals . 3 1.2 Contributions . 4 1.3 Thesis Structure . 6 Chapter Two : Background . 7 2.1 Application domain and technical aspects . 7 2.2 Gestures . 8 2.2.1 Tangibles . 9 2.3 Sketching and prototyping . 9 2.4 Types of prototypes . 12 2.4. 1 Mixed fidelity. 13 2.4. 2 The Anatomy of Prototypes . 14 Chapter Three : Related Work . 16
The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Naïve Bayes Classifier for The Classification of the Ratio of Inpatients

Diabetes can lead to mortality and disability, so patients should be inpatient again to undergo treatment again to be saved. On previous research about feature selection with greedy stepwise forward fail to predict classification ratio inpatient of patient with the result of recall and precision 0 on data training 60%, 75%, 80%, and 90% and there is suggestion to handle unbalanced class data problem by comparison of data readmitted 6293 and the otherwise 64141. The research purposed to know the effect of choosing the best model using best first instead of greedy stepwise forward and data sampling with spreadsubsample to resolve unbalanced class data problem. The data used was patient data from 130 American Hospital in 1999 until 2008 with 70434 data. The method that used was best first search and spreadsubsample. The result of this research are precision found 0.4 and 0.333 on training dataset 75% and 90% with best first method, while spreadsubsample method found that value of precision and recall is more significantly increased. Spreadsubsample has more effect with the result of precision and recall rather than using best first method.
Prevention of SQL Injection Attacks having XML Database

XML-based Web applications are broadly utilized in computer world, whose main applications are remote operation performance and bring arbitrary data. It is recently used in cloud interfaces, E-Government, Service Oriented Architectures etc. Due to abundant acceptance of this technology large attacks are raised like Denial of Service attacks, attacks on XML Encryption, and XML Signature Wrapping attacks. To stop these types of attacks different techniques were proposed however they're not enough to stop all varieties of attacks. The existing SQLIA prevention techniques can validate the client side data, one by one. It complicates the developer's task to write different validation codes for each data receiving page in the server. This paper proposed an idea of XML based SQLIA prevention technique which can validate the entire client side data by one single call of the dedicated validation function. In this process, the client will submit data in XML format and the server will verify the entire incoming XML file, based on some pr-decided rules called data-rules.
An Enhanced Load Balancing Mechanism in Cloud computing

: Cloud computing is the means of accessing a shared pool of configurable computing resources (including hardware, software, networks, servers, storage applications and services) that can be rapidly provided, used and released with minimal effort on the part of users or service providers. But it has some of the main concerns like load management and fault tolerance. In this paper we have put forward a load balancing approach in cloud computing. Load balancing is helped to distribute the workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources . It also improves the performance of the system.
An Introduction of Shadow Mapping Rendering Algorithms in Realistic Computer Graphics

Shadow is always being one of the most attractive subjects because of its important role in the field of computer realistic graphics. As one mainstream technology of the real-time soft shadow rendering methods, shadow mapping is extensively used in various graphical applications due to their generality and efficiency. In past years, there are many variations of the algorithms proposed to improve the original method in order to alleviate aliasing, enhance efficiency, etc. In this paper, we give a complete survey of shadow mapping algorithms. Basic idea, implementation, and various improvements of the algorithms are discussed in details.
Facial expression recognition based on improved deep belief networks

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.
Realization of Programmable BPSK Demodulator-Bit Synchronizer using Multirate Processing

This paper presents the design and implementation of programmable BPSK demodulator and bit synchronizer. The demodulator is based on the Costas loop design whereas the bit synchronizer is based on Gardner timing error detector. The advantage of this design is that it offers programmability using multi-rate processing and does not rely on computation of filter coefficients, NCO angle input for each specific data rate and thus avoids computational complexities. The algorithm and its application were verified on Matlab-Simulink and was implemented on ALTERA platform. A 32 kHz BPSK demodulator–bit synchronizer pair catering for data rates from 1 kbps to 8 kbps was implemented. DOI: http://dx.doi. org/10.11591/ijece.v4i3. 5561
Performance Evaluation of PSVM Using Various Combination of Kernel Function for Intrusion Detection System

The internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continuously, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, intrusion detection technique is a valuable technology to protect target systems and networks against malicious activities. But this system doesn't provide the required accuracy. Thus to meet this requirement, this paper proposes an intrusion detection system as a model based on Proximal Support Vector Machines (PSVMs) implemented with various combination of basic kernel functions. PSVM is a light and simple modification of support vector machine. We have implemented PSVM for binary classification of intrusion detection data. For experimental training and testing NSL- KDD dataset is preprocessed using Principle Component Analysis technique. Using proposed classification model, we have achieved up to 79% classification accuracy.