Miracle-dz/UniFAR-FTUs huggingface dataset:
BER performance of turbo product LDPC codes with non-sequential decoding

This work considers the bit error rate (BER) performance of turbo product codes (TPC) constituted from low density parity check (LDPC) codes with non-sequential decoding (NSD). The BER of the TPC-LDPC is evaluated for LDPC codes with various rates and sizes using the bit flipping (BF) and sum product (SP) algorithms. Monte Carlo simulation results demonstrate that NSD can provide a remarkable additional coding gain of about 3.5 dB using the BF algorithm as compared to sequential decoding. Moreover, simulation results confirmed that the order of the turbo and LDPC decoding iterations can have a noticeable impact on the BER. Similarly, the NSD of TPC-LDPC codes using the SP algorithm offered noticeable improvement for particular codes.
Reproduction of a plane-wave sound field using planar and linear arrays of loudspeakers

In this paper, we consider the physical reproduction of plane-wave sound fields via continuous planar respectively linear distributions of secondary point sources. Our approach employs a formulation of the reproduction equation in the spatial frequency domain to explicitly solve it for the appropriate secondary source driving signals. This constitutes a generalization of the Ambisonics approach which is typically formulated for spherical and circular secondary source arrangements. For planar arrays, the alternative formulation provided by the Kirchhoff-Helmholtz integral equation is equivalent. For linear arrays, the Kirchhoff-Helmholtz formulation appears as a far-field/high-frequency approximation of the presented approach.
How Much Information Can One Get From a Wireless Ad Hoc Sensor Network Over a Correlated Random Field?

New large-deviations results that characterize the asymptotic information rates for general d-dimensional (d -D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a two-dimensional (2-D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first-order conditional autoregression and the assumption of no in-network data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density, and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate Theta(area-1/2 ) and the per-node information under fixed per-node energy also diminishes to zero with rate O(Nt -1/3) as the number Nt of network nodes increases by increasing the coverage area. As the sensor spacing dn increases, the per-node information converges to its limit D with rate D-radic(dn)e-alphadn for a given diffusion rate alpha. When the coverage area is fixed and the node density increases, the per-node information is inversely proportional to the node density. As the total energy Et consumed in the network increases, the total information obtainable from the network is given by O(logEt ) for the fixed node density and fixed coverage case and by Theta(Et 2/3 ) for the fixed per-node sensing energy and fixed density and increasing coverage case.
Work in progress: A new approach of changeable password for keystroke dynamics authentication system on smart phones

Most of subscribers use personal identification number (PIN) codes which combined with 6-8 numbers to protect their Subscriber Identity Module (SIM) cards from illegal accesses. To enhance security, many previous studies have employed keystroke dynamics to protect the PIN code, and the relative results exhibit that keystroke dynamics can indeed improve the security of a PIN code. However, the traditional keystroke dynamics-based authentication (KDA) system has to collect user's keystroke dynamics firstly and then produce a unique personal biometrics. It's inconvenient for users when changing their PIN codes is required, because the corresponding KDA systems have to be retrained. To solve the above-mentioned drawbacks, this paper proposes a novel technique by which users can change their PIN codes anytime, without any extra retraining. Conducted experiment results show that the proposed approach can effectively improve the KDA system to distinguish legitimate users and impostors even when users change their original passwords.
A novel BS power saving scheme with virtual coverage management for green heterogeneous LTE networks

Green radio technology for energy saving is one of important issues in future cellular networks. Two power saving schemes have been previously introduced to enhance the energy saving efficiency for green cellular networks, in which the cell zooming scheme and the cell wilting and blossoming scheme require organic operations between cells in the cellular networks. With the previous power saving schemes, inorganic operations of a serving cell and neighboring cells could cause coverage holes between the cells, thereby seriously degrading the power saving efficiency as well as the quality of service. In this paper, we propose a novel base station (BS) power saving scheme for green heterogeneous LTE cellular networks, in which the networks virtually adjusts the coverage of a serving macrocell (SM) and neighboring macrocells (NMs) without adjusting the transmission power of BSs when the SM is lightly loaded and turns off the SM when none of active users are associated with the SM. Simulation results show that the proposed scheme significantly improves the power saving efficiency without causing system throughput degradation. In particular, with the proposed scheme macrocells are allowed to operate independently without concurrent operation of SM and NMs for green heterogeneous cellular networks.
Support for reverse engineering and maintaining feature models

Feature Models (FMs) are a popular formalism for modelling and reasoning about commonality and variability of a system. In essence, FMs aim to define a set of valid combinations of features, also called configurations. In this paper, we tackle the problem of synthesising an FM from a set of configurations. The main challenge is that numerous candidate FMs can be extracted from the same input configurations, yet only a few of them are meaningful and maintainable. We first characterise the different meanings of FMs and identify the key properties allowing to discriminate between them. We then develop a generic synthesis procedure capable of restituting the intended meanings of FMs based on inferred or user-specified knowledge. Using tool support, we show how the integration of knowledge into FM synthesis can be realized in different practical application scenarios that involve reverse engineering and maintaining FMs.
An Investigation of Cointegration and Casualty Relationships between the PIIGS’ Stock Markets

The aim of this paper is to investigate the relationship of price changes in the southern European E.U. member states through their stock markets and especially among the exchange markets of Portugal, Italy, Ireland, Greece and Spain, known also as the PIIGS countries. More specifically, it is examined whether cointegration and causality relationships exists among the PIIGS’ Stock Markets while by testing these relationships the existence of the Efficient Market Hypothesis (EMH) among these stock markets is also tested. In case of cointegration relationships between these markets it is proved that possible advantages by internationalizing portfolio diversification are limited and further attention must be given for the selection of an internationalized optimal portfolio. It is also wealth mentioning that since 2012 Europe faces a serious economic crisis which is deeper in the member states of the South, so even further attention must be given to the construction of optimal portfolios.
A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects

A high-fidelity battery model capable of accurately predicting battery performance is required for proper design and operation of battery-powered systems. However, the existing battery models have at least one of the following drawbacks: 1) requiring intensive computation due to high complexity; 2) not applicable for electrical circuit design and simulation; and 3) not capable of accurately capturing the state of charge (SOC) and predicting runtime of the battery due to neglecting the nonlinear capacity effects. This paper proposes a novel hybrid battery model, which takes the advantages of an electrical circuit battery model to accurately predicting the dynamic circuit characteristics of the battery and an analytical battery model to capturing the nonlinear capacity effects for the accurate SOC tracking and runtime prediction of the battery. The proposed battery model is validated by the simulation and experimental studies for the single-cell and multicell polymer lithium-ion batteries, as well as for a lead-acid battery. The proposed model is applicable to other types and sizes of electrochemical battery cells. The proposed battery model is computational effective for simulation, design, and real-time management of battery-powered systems.
Analysis of data center SDN controller architectures: Technology and business impacts

The typical Software Defined Networking (SDN) Data Center (DC) of the future will likely be comprised of a software overlay and a hardware underlay, i.e., a physical network. From an architectural perspective, there are two distinct possibilities for such DCs. The first is a loosely coupled model where the software overlay and the hardware underlay are kept independent and managed separately. In this model, the software overlay is transparent and virtually invisible to the hardware underlay. The second is a tightly coupled model, where the software overlay and the hardware underlay work in cooperation, with the hardware underlay having visibility into the software overlay. This paper analyzes the impact of these approaches from a technology and business standpoint. It also presents some fundamentally different ideas for maximizing the value of the underlay in a Commercial-off-the-shelf (COTS) ASICs environment.
Volatility Forecasting in Bull & Bear Markets

This thesis considers the performance of variance forecasting in bull and bear markets. Three asset indices, the DAX, the Standard & Poor’s 500 and the CurrencyShares Euro Trust, are split into bull and bear periods whereby variance forecasting is evaluated in the two states. I employ a simple moving average, an EWMA, implied volatilities from official volatility indices and three GARCH specifications; a GARCH (1,1) and EGARCH(1,1) with Student’s t errors and a GARCH (1,1) with Hansen’s skewed t errors. I compute 30 days ahead variance forecasts using daily data and the true latent variance is approximated by the intra-month realized variance. Performance is measured by the R2 from regressing the realized variance on the estimated variance, the QLIKE statistic and the MSE. I find that implied volatilities forecast best in bull markets and that the GARCH and EGARCH forecast best in bear markets. In general, the predictions’ R2 and QLIKE statistics suffer 30 % 50 % in bear markets and the MSE is as much as 15 times higher compared to bull markets. ∗Thesis for the Master of Science in Finance at University of Gothenburg †Doktor Salens Gata 15, 41322 Goteborg, Sweden. +46709700719. Email: k.o. ekvall@gmail.com
Smart-array for pipelined BTI characterization

Deeply-scaled transistors fabricated in advanced High-k/Metal Gate (HK/MG) technologies have an intrinsic variability, requiring characterization of a large number of transistors to obtain statistically relevant data. A powerful tool in the form of a Smart-array circuit is designed to serve the BTI characterization needs on an industrial scale, where time plays an important role. The Smart-array circuit is designed such that 700 pMOS and nMOS transistors can be measured using the concept of pipelining. A significant reduction of up to 88.5% in time is demonstrated by establishing a pipeline of 15 pMOS transistors and the Measure-Stress-Measure (MSM) scheme of choice.
Aberrant DNA methylation of imprinted loci in superovulated oocytes.

BACKGROUND There is an increased incidence of rare imprinting disorders associated with assisted reproduction technologies (ARTs). The sex-specific epigenetic modifications that are imposed during gametogenesis act as a primary imprint to distinguish maternal and paternal alleles. The most likely candidate for the gametic mark is DNA methylation. However, the timing of DNA methylation acquisition in adult oocytogenesis and the effects of superovulation are unknown. METHODS We examined the maternal methylation of PEG1(MEST), LIT1(KCNQ1OT1) and ZAC(PLAGL1) and the paternal methylation of H19 in adult growing oocytes of humans and mice and compared them with the methylation status of mouse neonatal growing oocytes by using bisulphite sequencing. Furthermore, we examined the effects of superovulation in the human and mouse. RESULTS Maternal methylation of these genes has already been initiated to some extent in adult human and mouse non-growing oocytes but not in mouse neonates. In addition, the methylation dynamics during adult human and mouse oocyte development changed more gradually than those during neonatal oocyte development. Furthermore, we found the demethylation of PEG1 in growing oocytes from some ART-treated infertile women and a gain in the methylation of H19. We also detected methylation changes in superovulated mice. CONCLUSION Our studies in the human and mouse suggest that superovulation can lead to the production of oocytes without their correct primary imprint and highlight the need for more research into ARTs.
A Counter-Example to the Mismatched Decoding Converse for Binary-Input Discrete Memoryless Channels

This paper studies the mismatched decoding problem for binary-input discrete memoryless channels. An example is provided for which an achievable rate based on superposition coding exceeds the Csiszár-Körner-Hui rate, thus providing a counter-example to a previously reported converse result. Both numerical evaluations and theoretical results are used in establishing this claim.
Role of the Functional Toll-Like Receptor-9 Promoter Polymorphism (-1237T/C) in Increased Risk of End-Stage Renal Disease: A Case-Control Study

Inflammation induced by infectious and noninfectious triggers in the kidney may lead to end stage renal disease (ESRD). Toll-like receptor 9 (TLR-9) a receptor for CpG DNA is involved in activation of immune cells in renal disease and may contribute to chronic inflammatory disease progression through an interleukin-6 (IL-6) dependent pathway. Previous studies indicate that -1237T/C confers regulatory effects on TLR-9 transcription. To date the effect of TLR-9 polymorphisms on ESRD remains unknown. We performed a case-control study and genotyped 630 ESRD patients and 415 controls for -1237T/C, -1486T/C and 1635G/A by real-time PCR assays and assessed plasma concentration of IL-6 by ELISA. Haplotype association analysis was performed using the Haploview package. A luciferase reporter assay and real-time PCR were used to test the function of the -1237T/C promoter polymorphism. A significant association between -1237T/C in TLR-9 and ESRD was identified. The TCA, TTA and CCA haplotype of TLR-9 were associated with ESRD. ESRD patients carrying -1237TC had a higher mean plasma IL-6 level when compared with -1237TT. The TLR-9 transcriptional activity of the variant -1237CC allele is higher than the -1237TT allele. The results indicate that in a Han Chinese population the presence of the C allele of -1237T/C in the TLR-9 gene increases susceptibility towards development of ESRD. In vitro studies demonstrate that -1237T/C may be involved in the development of ESRD through transcriptional modulation of TLR-9.
Machine learning techniques for FPS in Q3

This paper presents a First Person Shooter Artificial Intelligence system that makes use of machine learning capabilities to achieve more human-like behavior and strategies. The AI is trained with a supervised learning paradigm using example recorded during the observation of expert human players. The Machine Learning section of the AI is based on various Feed Forward Multi-layer Neural Networks trained by Genetic Algorithms. The AI system is developed and tested in the Quake 3 Arena game engine. The system is able to learn certain behaviors but still lack on some others. The results are evaluated and possible improvements are proposed.
Operating System Controlled Processor-Memory Bus Encryption

Unencrypted data appearing on the processor- memory bus can result in security violations, e.g., allowing attackers to gather keys to financial accounts and personal data. Although on-chip bus encryption hardware can solve this problem, it requires hardware redesign or increases processor cost. Application redesign to prevent sensitive data from appearing on the processor-memory bus is extremely difficult. We propose and evaluate a processor-memory bus encryption technique for embedded systems that requires no changes to applications or hardware. This technique exploits cache locking or scratchpad memory, features present in many embedded processors, permitting the operating system (OS) virtual memory infrastructure to automatically encrypt data belonging to protected processes as they are written to off-chip memory. Pages belonging to unprotected processes are stored unencrypted to prevent performance and energy consumption penalties. We evaluate the proposed bus encryption technique using full system simulation. Experimental results indicate that it is possible to prevent the working data sets of processes from appearing on the processor-memory bus in plaintext, without using dedicated hardware and without changing applications. The OS based technique results in 1.37times slowdown for protected processes for processors with 512 KB of L2 cache and 1.78times slowdown for processors with 256 KB of L2 cache. There are negligible performance penalties for unprotected processes.
Knowledge-Based Deriven Manufacturing System (KDMS) for Injection Molding

The global manufacturing development faces the new challenges including waste of resources, short delivery time, labor cost increase, short of labor, etc. As a result, the overall industry environment of plastic materials manufacturing turns to focus on automatic production that enables manufacturing industry entering into the next phase so that production efficiency, man-made errors, and labor shortage can be improved. In this study, a cloud based platform of knowledge-driven manufacturing system integrates science bases and practical experience of injection molding with remote control technology that can enhance traditional production efficiency in several aspects such as production guidance in design/tooling/molding phases, production scheduling for all working members and equipment and processing monitoring via internet. More important is that during each working stage, sufficient knowledge are provided and guiding/driving the design/manufacturing work. The new knowledge/data are accumulated, preserved, reused. The system has been test in Jabil Green Point and Techmation, the efficiency is more than 50% improvement as compared with traditional human operation. Keywords—Intelligent, Knowledge-driven, Cloud
3D based head movement tracking for incorporation in facial expression system

Summary Head movement tracking is a necessary system in our attempt to establish the positioning of the head in an instance of the time. In computer graphics, head positioning sequence must be done in a proper manner so that the rendering will appear realistic. The head role becomes more important when a facial expression is being depicted. As a true facial expression must be accompanied with some motion of the head, rendering the facial expression without any proper description regarding head movement will make the head less realistic. This paper proposed a dual-pivot 3D-based head movement tracking system (DPHT) that enables modeler to capture the movement of the head. By having two pivots in the system, the movement of the neck can be modeled together with the yaw, roll and pitch of the head. This movement of the neck is an integral part of the facial expression depiction as can be attested by someone who 'pulls' his neck in manifestation of disgust. The results in this paper show that having a dual-pivot tracking system, head positioning can be better established hence producing more realistic head movement model.
Are Capital Controls Effective? The Case of the Republic of Korea

Capital controls have recently attracted interest as capital surges in emerging market economies threaten to bring about economic instability and heighten difficulties in implementing macroeconomic policies. While an option that can be taken to deal with huge capital inflows involves the use of capital controls, there is no consensus on their effectiveness. Against this background, our paper aims to investigate the effectiveness of capital controls in the Republic of Korea. This paper first reviews the history of capital account policy, which can be divided into five stages: (i) gradual liberalization during the 1980s and early 1990s, (ii) acceleration of liberalization during the early and mid-1990s, (iii) the big-bang approach to liberalization during the Asian financial crisis, (iv) liberalization for facilitation of capital market development beginning the late 1990s, and (v) the conservative approach during the global financial crisis. To quantify the trends, this paper constructs measures of capital account control/liberalization based on the official record of government policies. In the second part, it discusses the effects of capital account control/liberalization in four ways. First, the behavior of key macro variables in the Republic of Korea is reviewed by comparing the periods before and after serious capital account liberalization. Second, the effects of shocks to the capital account control/liberalization indexes on capital flows are examined using a VAR model. Third, the effects of the United States (US) monetary policy shocks on capital flows and the interest rate of the Republic of Korea are examined for the period of capital controls and the period of capital account liberalization. Fourth, a simple event study is conducted of recent capital control measures. The empirical results based on VAR models show that shocks to capital account controls do not have significant effects on capital flows in most cases. However, capital flows, the current account, and the exchange rate were far more volatile in the period of a liberalized capital account. It is also interesting that during the latter period, the Republic of Korea did not gain monetary autonomy despite adopting a freely floating exchange rate. This result may be related to volatile capital flows under a liberalized capital account. Finally, the results of the event study tend to support the effectiveness of capital controls in altering the composition of capital flows.