Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Metric Sorting for Successive Cancellation List Decoding of Polar Codes

This letter focuses on the metric sorter, which is a module that affects the overall latency in the LLR-based successive cancellation list (SCL) decoder of polar codes. We propose two improved two-step sorters, which are achieved by revising the sorters used in the pruned bitonic extractor (PBE), and selecting one of two candidate architectures based on a threshold on <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>. Moreover, we remove the first stage of the PBE in the second step sorting, which can reduce the latency in the second step and further increase the throughput of the sorting units. We first describe the architectures of the proposed sorters, and then derive the required number of the compare-and-swap units (CASUs) and the stages. The evaluation results indicate that the two proposed improved sorters outperform the state-of-the-art sorters in terms of the CASUs and the stages, which significantly lowers the overall latency and resource consumption. Compared with the PBE, the proposed sorters can reduce 57.8% of stages and 46.3% of CASUs for <inline-formula> <tex-math notation="LaTeX">$L=128$ </tex-math></inline-formula>, and 33.3% of stages and 21.1% of CASUs for <inline-formula> <tex-math notation="LaTeX">$L=8$ </tex-math></inline-formula>.
Hidden costs of ERP Implementation

It’s well known that ERP Implementation requires, above all, time and money. It can be said globally that these two elements represent a cumulative effect of many critical success factors (CSFs) of ERP implementation. Without diminishing the importance of time, the cost component of ERP implementation may be significantly increased by the hidden costs of implementation, which in the phase of supply and contracting are not seen as separate items. This paper presents the research on hidden costs of ERP implementation in small and medium-sized (SME) manufacturing companies in Croatia.
Comparison of data aggregation from a wireless network of sensors using database sharding and foreign data wrappers

In this work two built-in database technologies were compared and applied to aggregation of data from a network of sensor equipped data collection nodes in a real manufacturing environment. The methods investigated are: data sharding and foreign data wrappers. The availability and feature set differences of the methods on some popular database management systems was compared and then a detailed case study on PostgreSQL DBMS was performed. For testing the concept in more realistic environment the sensor network was implemented using a number of Raspberry Pi computers with local relational database and with wireless connectivity to main aggregation database running on a much powerful server computer. Both database methods were applied on all nodes and the server and performance was measured for different types of aggregation queries. The very same data aggregation concepts presented here could be applied to any sensor network or to any Internet of things network.
IMPROVED ENHANCEMENT LAYER VIDEO CODING

The members of the Committee appointed to examine the thesis of Rathilall Sewsunker find it satisfactory and recommend that it be accepted. Chair ii ACKNOWLEDGMENTS I would like to thank my advisor, Professor T.R. Fischer, for his guidance and assistance during my study. He is an excellent teacher and mentor, and has patiently guided me through the graduate program. Sincere appreciation also goes to Dr B. Belzer, Dr K. Sivakumar and Professor C. Hsu, both for their well-taught classes as well as their assistance in serving on my committee. Thanks to Ruby Young for all her wonderful help. I would like to thank the School of Electrical Engineering and Computer Science for the education that I have received. Thanks to my sponsor, the Africa-America Institute for providing me with a full scholarship and for their caring attitude during my study program. Particular thanks for that goes to Marks Chabedi, my Program Officer. Thanks to my employer, the University of Durban-Westville, for allowing me study leave, and especially to the Head of Electrical Engineering, Professor S. H. Mneney for his help and support. Brian Banister has helped tremendously practically from my first semester at Wash-ington State University through until the end. Brian's selfless disposition of helping and teaching will always be admired. Thanks to Esteban Rodriguez-Marek, and Ping Hou for their enthusiastic help with course-work and research alike. Thanks to my office-mate, Runar Solli, for his help and his jovial company. Thanks to my Mum and my family in South Africa for their encouragement and support, iii and especially to Kennybhai and Jaybhai, who took care of all kinds of responsibilities, not excluding sending money whenever I needed it! iv IMPROVED ENHANCEMENT LAYER VIDEO CODING Abstract Scalability techniques are increasingly applied to video coding to alleviate transmission problems such as restricted bandwidth, network congestion and data loss. Being layered, scalable video offers the receiver a choice of bit-streams based on resources and needs. The thesis focuses on SNR scalability. The conventional method for SNR scalability provides a base layer description using coarse quantization, then encodes the refinement information using a smaller step size. For the case of a two-level encoder, the refinement information is simply a difference between the original data and the base layer description. This work illustrates that picture quality can be improved by encoding the enhancement information based on the value returned by the base layer quantizer, rather …
UMGAN: Generative adversarial network for image unmosaicing using perceptual loss

Image mosaicing conceals sensitive parts of an image. The objective of this work is to recover hidden semantic structure under mosaiced parts, especially focusing on facial images. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. We present a Generative Adversarial Network (GAN) approach to image unmosaicing called UMGAN, which is an image-to-image translation method. We have found that exploiting perceptual loss together with low level $l_{1}$ loss and high level Structural SIMilarity (SSIM) loss is quite effective to attain visually plausible and semantically consistent results. We have evaluated our method on the CelebA and MIT-CBCL image datasets and achieved better perceptual results than state-of-the-art image completion methods.
Design of artificial dielectrics for anti-reflection-coatings

With the increasing interest in new artificial materials ranging from meta-materials to photonic bandgap materials there is a growing need for accurate models of artificial materials. We examine the design of a three-layer structure with an artificial dielectric as an anti-reflection-coating on both sides of a dielectric slab. We present a field-equivalent circuit theory to calculate the effective dielectric constant of the structured dielectric. The materials discussed in this paper have a lateral periodicity smaller than /spl lambda//10 and can easily be manufactured from high density polyethylene (HDPE) for frequencies up to 20 GHz. The field-analog circuit theory can also be used to calculate the polarization dependence of the artificial dielectric. Theoretical results of three geometrically different structures were compared with waveguide measurements at X-band and showed excellent agreement. Applications such as the design of new components at millimeter and submillimeter wave frequencies are discussed, considering the X-Band measurements as a scaled model.
Detecting Prostate Cancer Using A CNN-Based System Without Segmentation

A computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed in this paper. The proposed system starts by defining a region of interest that includes the prostate across the different slices of the input DWI volume. Then, the apparent diffusion coefficient (ADC) of the defined ROI is calculated, normalized and refined. Finally, the classification of prostate into either benign or malignant is achieved using a classification system of two stages. In the first stage, seven convolutional neural networks (CNNs) are used to determine initial classification probabilities for each case. Then, an SVM with Guassian kernel is fed with these probabilities to determine the ultimate diagnosis. The proposed system is new in the sense that it has the ability to detect prostate cancer with minimal prior processing (e.g., rough definition of the prostate region). Evaluation of the developed system is done using DWI datasets collected at seven different b -values from 40 patients (20 benign and 20 malignant). The acquisition of these DWI datasets is performed using two different scanners with different magnetic field strengths (1.5 Tesla and 3 Tesla). The resulting area under curve (AUC) after the second stage of classification is 0.99, which shows a high performance of our system without segmentation similar to the performance of up-to-date systems.
Maximum Signal Fraction Analysis for Enhancing Signal-to-Noise Ratio of EEG Signals in SSVEP-Based BCIs

Various improved canonical correlation analysis (CCA) methods were developed for enhancing the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Among them, the method combining CCA spatial filters from sine-cosine references and individual templates yielded the highest performance. However, the CCA aims to optimize the correlation between two sets of variables rather than the signal-to-noise ratio (SNR) of the SSVEP signals, upon which the performance of an SSVEP-based BCI depends mainly. In this paper, a novel algorithm, namely, maximum signal fraction analysis (MSFA), is proposed for creating spatial filters based on individual training data. The spatial filter for a specific stimulus target is estimated by directly maximizing the averaged SNR of the observed signals across multiple trials. An individual template is calculated for each target by averaging training signals of multiple trials. Target recognition is based on template matching between filtered template signals and a single-trial testing signal. Classification performance of the MSFA-based method was evaluated on a benchmark dataset and compared with that of the CCA-based methods. The results suggest that the proposed MSFA method significantly outperforms the CCA-based methods in terms of classification accuracy, and thus, it has great potential to be applied in the real-life SSVEP-based BCI systems.
Secrecy Performance for Integrated Satellite Terrestrial Relay Systems with Opportunistic Scheduling

This paper investigates the impact of opportunistic user scheduling scheme on the secure transmission for the integrated satellite terrestrial relay systems (ISTRS), where multiple legitimate terrestrial users and multiple eavesdroppers are concerned in the system. In order to obtain the best secrecy performance, the terrestrial user with the optimal signal-to-noise ratio (SNR) is selected by employing opportunistic scheduling scheme to receive the signal. In addition, this paper considers two representative eavesdropping cases, i.e., Case I which is shown as the colluding case; the eavesdroppers work together and overhear the main channel, and Case II, which is called by non-colluding case: the eavesdropper with the largest SNR will be selected to overhear the legitimate users' channel. Furthermore, we obtain the closed-form expression for the secrecy outage probability (SOP) relied on the given scheduling scheme between two representative wiretapping cases. In order to obtain further results in high SNR regime, approximate SOP expressions under both cases are also derived. At last, some meaningful Monte Carlo (MC) simulation results are given to show the correctness of the theoretical results.
First-Order Nelsonian Paraconsistent Quantum Logic

In this paper, a single-antecedent/succedent sequent calculus NL for first-order Nelsonian paraconsistent quantum logic is investigated. The logic under consideration is regarded as a combination of both Nelson's paraconsistent four-valued logic and Dalla Chiara and Giuntini's paraconsistent quantum logic. The duality and cut-elimination theorems for NL are proved. Decidability, some constructive properties, some constructible falsity properties, and Craig interpolation property are shown for NL. An extend NL with some naive comprehension rules in the naive set theory is also investigated.
SAMBA-bus: A high performance bus architecture for system-on-chips

A high performance communication architecture, SAMBA-bus, is proposed in this paper. In SAMBA-bus, multiple compatible bus transactions can be performed simultaneously with only a single bus access grant from the bus arbiter. Experimental results show that, compared with a traditional bus architecture, the SAMBA-bus architecture can have up to 3.5 times improvement in the effective bandwidth, and up to 15 times reduction in the average communication latency. In addition, the performance of SAMBA-bus architecture is affected only slightly by arbitration latency, because bus transactions can be performed without waiting for the bus access grant from the arbiter. This feature is desirable in SoC designs with large numbers of modules and long communication delay between modules and the bus arbiter.
Transmission Tower Tilt Angle On-Line Prognosis by Using Solar-Powered LoRa Sensor Node and Sliding XGBoost Predictor

This paper presents a transmission tower tilt angle prognosis method based on solar-powered LoRa sensor node and sliding XGBoost predictor. The proposed LoRa sensor node, which consists of solar panel, LoRa radio frequency chip, super-capacitors, MCU, accelerometer, and gyroscope, can measure the initial tilt angle of the transmission tower and the angular rate of the transmission tower. Then, the measuring signals of transmission tower were wirelessly transmitted to the LoRa gateway and were processed online. First, the noise of the raw angular rate is reduced by using PCA (principal components analysis) method and the tilt angle of the transmission tower can be calculated by integrating the angular rate. Second, a sliding XGBoost predictor is proposed for tilt angle prognosis, which collects the training data and trains the regression model dynamically. Third, a novel parameter optimization algorithm named DCCPSO (double chain chaos particle swarm optimization) and its execution strategy are proposed to determine the values of hyper-parameters. Finally, the experimental system and the corresponding experimental results are demonstrated and discussed in detail, which shows that the proposed method is effective in transmission tower to tilt angel on-line prognosis.
Yield-driven design of direct-coupled waveguide filters with minimum use of full wave EM solvers

To reduce costly prototyping and allow for short design cycles in the design of tuningfree waveguide filters, it is important to be able to perform yield analysis in short time frames. For many filter topologies, this is infeasible by direct application of full wave EM solvers. In this paper, a simple and fast method for yield-driven design of direct-coupled waveguide filters with minimum use of full wave EM solvers is presented. The method is based on training a circuit model by including the sensitivities of each coupling to changes in dimensions and frequency. It is shown that this model gives accurate results for dimensional changes within typical production tolerances for waveguide components, and it is thus possible to perform fast tolerance analysis with high accuracy for yield-driven design. Examples of tolerance analysis for some filter topologies are shown. Also included is a comparison with measurements.
Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews

With few exceptions, most research in automated assessment of depression has considered only the patient’s behavior to the exclusion of the therapist’s behavior. We investigated the interpersonal coordination (synchrony) of head movement during patient-therapist clinical interviews. Our sample consisted of patients diagnosed with major depressive disorder. They were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at 7-week intervals over a period of 21 weeks. For each session, patient and therapist 3D head movement was tracked from 2D videos. Head angles in the horizontal (pitch) and vertical (yaw) axes were used to measure head movement. Interpersonal coordination of head movement between patients and therapists was measured using windowed cross-correlation. Patterns of coordination in head movement were investigated using the peak picking algorithm. Changes in head movement coordination over the course of treatment were measured using a hierarchical linear model (HLM). The results indicated a strong effect for patient-therapist head movement synchrony. Within-dyad variability in head movement coordination was found to be higher than between-dyad variability, meaning that differences over time in a dyad were higher as compared to the differences between dyads. Head movement synchrony did not change over the course of treatment with change in depression severity. To the best of our knowledge, this study is the first attempt to analyze the mutual influence of patient-therapist head movement in relation to depression severity.
Developing a Hybrid Expert/Data-Driven Health Index for Railway Axleboxes Using Auto-Encoder Neural Networks

The railway axlebox is a critical safety component in rolling stock. It bears the weight of the train, and minimises the friction with the rotating axle. Its failure in service might cause derailment. Therefore, its maintenance is decisive. This article presents a methodology to create a standard Health Index that blends the hard threshold-based expert criteria developed at the workshop, with a record of actual vibration data, in order to increase the flexibility of its diagnostic and provide a soft granular feedback. The proposed method makes use of an auto-encoder neural network to accommodate two different approaches: one based on the principal component of a set of vibration features, and the other based on an anomaly detection scheme operating directly at the vibration signal level. The results of the analysis show that the former strategy provides a smooth uncertain index, while the latter yields a sharp sensitive index. Such different diagnosis designs are regarded as an opportunity to better fit the vision of the maintenance course of action.
Association And Classification Models For Web Mining

We present methods for the search of association rules in the analysis of web clickstream data. We first show association rules, and show how to draw data mining conclusions on the basis of them. We then compare association rules, which are local models, with tree-based models, and show how association rules can be deduced from them. Our analysis have been conducted on a real ecommerce dataset.
Soft-Switching Losses in GaN and SiC Power Transistors Based on New Calorimetric Measurements

The use of modern GaN and SiC power semiconductors in soft-switching power electronic applications makes it possible to minimize switching losses and to achieve a significant increase in power density. However, the loss mechanisms in soft-switching operation of power semiconductors are poorly under-stood and electrical measurements of these losses are affected by a large error. Therefore calorimetric measuring methods are becoming state of the art for determining soft-switching losses. This paper for the first time presents empirical power dissipation data for high voltage GaN and SiC power semiconductors of low power class at application-oriented switching frequencies for zero voltage switching (ZVS) operation. The losses were measured using a transient calorimetric measurement setup with the goal of determining also low power dissipation with high accuracy while maintaining the application-oriented commutation cell layout.
QoE Driven Resource Allocation in Massive IoT: A Deep Reinforcement Learning Approach

Dense deployment of various Internet of Things (IoT) equipment in cellular will lead to a sharp shortage of frequency resources. Consequently, how to make efficient utilization of radio resources in massive IoT scene is a brand-new challenge. In this paper, we present a novel deep reinforcement learning approach for massive IoT networks that performs centralized joint resource allocation to satisfy the link interference constraint and to maximize the quality of user experience (QoE) which is measured through utility based Mean Opinion Score (MOS). Different from the traditional resource allocation algorithm, we model the problem as a joint optimization resource allocation problem and use a neural network embedded reinforcement learning algorithm to obtain the optimal solution. In addition, we utilize a new reinforcement framework called actor-critic (AC) to overcome the problem of continuous power allocation that cannot be accomplished in traditional reinforcement learning algorithms. The simulation results show that our proposed algorithm has good convergence under different conditions. Furthermore, QoE is consistent with the optimal value under our algorithm, and the average QoE can reach a higher level.
The Internet of Things as a Facilitator of Smart Building Services

To successfully perform the necessary migration towards a sustainable civilization, it is required we exploit both consolidated and emerging technologies. Within this context, the emerging smart grid has numerous components, and is closely intertwined with the concept of the internet of things (IoT). Both are necessary for the transition to a high renewable penetration electricity generation matrix, by making use of improved data acquisition and processing to optimize systems’ operations. In this work, we focus on energy use in buildings, as they are one of the biggest end energy usage sectors. In particular, we identify what energy resources are available within buildings, and what power system services they may provide, whilst exploring how IoT facilitates or enables each of these.
System level design of embedded controllers: knock detection, a case study in the automotive domain

We present a case study in the design of automotive engine controllers: the development of a knock detection algorithm and its implementation in an optimized platform. The design problem is complicated by the need of using heterogeneous models of computation and different design environments. The use of different design environments, one for functional design and one for architectural design space exploration, requires to transform a model of computation into another. We describe how we solved this problem and we present the ﬁnal design with the trade-offs explored.