Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Standalone and In-Fpga LEON3 Processors for Use in Deep Space Missions

When considering a new processor for a mission, one of the first questions that comes up is: “How does this processor compare with what we have used in the past? ” Manufacturers use benchmarks to compare normalized performance from one product to another, but often these data are incomplete and are missing key parameters such as compiler version, compile options used, memory type, and many others. In this work, we present carefully documented benchmarking results for single-core and multi-core Gaisler LEON3 processors. We evaluate both standalone (UT699, UT700, GR712RC-multi-core)and in-FPGA soft-core (single-core LEON3, quad-core LEON3)processors. In the case of multi-core processors, we use the OpenMP API with RTEMS-5.0 real-time operating system to demonstrate performance gains with automatic parallelization of computationally intensive code segments. With increasing demand for high-performance FPGAs on space missions, the option to drop in a soft-core processor into the system design should be on the table, provided there are available FPGA resources. We show that the soft-core LEON3 implementation has comparable frequency-normalized performance and is a good alternative or supplement to a dedicated standalone hard-core processor. By adding one or more soft-core processors into the FPGA design, the data systems architect may eliminate additional hardware, save on overall power, and improve net system performance by optimizing HW-to-SW data transfers. On-board processing, communications, and data management tasks can benefit from a tightly coupled processor inside an FPGA. We present various architectural design considerations that impact the performance and resource utilization of LEON3 processors inside a Microsemi RTG4 FPGA.
3D Printed Antennas Metallized Using Conductive Paint at X-Band

This paper presents performance evaluation of 3D printed antennas that operate at X-band and metallized using simple and low cost methods. The proposed 3D printed antennas operate at 10 GHz and they are metallized using different low cost conductive paint. The first antenna is metallized using conductive copper paint, while the second and the third antennas are metallized using silver paint. The performance of the proposed antennas is evaluated and the measured results of the prototypes show a good agreement with the simulation results, especially in the case of the antennas that metallized using silver paint. Measured results show descent and high gain performance of the fabricated prototypes. In addition, results show that the proposed antennas have wide impedance bandwidth of more than 10%, with radiation in the boresight direction with low side lobe level performance.
Optimal EEG Window Size for Neural Seizure Detection

In this paper, different window sizes for EEG signal segmentation are investigated in order to optimize the performance of seizure detection systems. To differentiate between epileptic and non-epileptic epochs, the time axis of the EEG signal is divided into non-overlapping windows. The window period should be long enough for the lapse to be informative but not too long for it to stay stationary. Hence, the KPSS test is used to determine signal stationarity for different window sizes, then the optimal window is chosen such that it corresponds to the smallest number of non-stationary segments in the signal of interest. The seizure detection system is then applied to the piece-wise stationary segments. Compared to the exhaustive examination, it is found that the KPSS test optimal window results in the highest sensitivity.
MIMO Architecture Modelling Utilizing Overlapped Element Antenna Arrays

Multiple Input – Multiple Output (MIMO) architectures are essential in 4G and 5G mobile communications. They are capable of providing both multiplexing gain and diversity gain depending on the use case. Indeed, they can provide high data rate by simultaneous transmission of data streams through multiple independently faded, uncorrelated, channels. On the other hand, they can be employed as phased arrays exhibiting directive multi-antenna characteristics with dynamic beam-patterns to boost capacity via higher SNRs. Those “hybrid systems” came in play to provide the advantages of both MIMO and phased array. In this path in order to increase the gain of an antenna without altering its layout, meaning that the dimensions of the radiating aperture remain the same, we need to use the overlapping element concept. At the same time we need to assess whether or not other parameters of MIMO performance are compromised. This assessment is presented in the current work through the provision of a MIMO channel model incorporating the overlapping concept.
Transmitarray Antenna with Integrated Frequency Multiplier for High-speed D-band Communications in Low-cost PCB Technology

This contribution presents the design and experimental validation of a high-gain D-band fixed beam transmitarray excited by a compact antenna-in-package. Both the planar lens and the focal source are fabricated using standard printed circuit board (PCB) technology. An eight-fold frequency multiplier is co-integrated on the source and feeds a 2×2 patch array. The transmitarray is designed using eight phase states (i.e. 3 bits of phase quantization) and comprises 1600 unit cells (40 × 40). As per simulations, the −3-dB gain bandwidth ranges from 114 GHz to 144 GHz, with a peak gain of 29.5 dBi. An excellent agreement between measured and simulated radiation patterns is observed. The proposed concept represents a cost-effective solution for future wireless networks operating beyond 100 GHz.
Bayesian Regression with Network Predictors

Our focus is on prediction and inference on the association between a network-valued random variable and a response. The motivation is drawn from neuroscience studies measuring a brain connectivity network for each subject along with a response, such as an intelligence score. A recent nonparametric model for network data automatically clusters subjects into groups according to their brain network structure. We build on this model by proposing a Bayesian linear regression that allows the response conditional expectation to shift over the network clusters, facilitating inference on the association between the network and the response. A Gibbs sampler is defined for posterior computation. The approach is applied to data on human brain networks and intelligence scores.
MAXDOR: Mapping XML Document into Relational Database

The eXtensible Markup Language (XML) is used for representing and exchanging data through the Internet, but this technology needs a suitable medium for storing these data. At present, three common technologies can be used to store and retrieve XML documents, i.e., native XML database, Object oriented Database (OODB) and Relational Data- base (RDB). This paper describes a general method for mapping XML documents to relational database. The method does not need a DTD or XML schema. It uses global label approach for identifying each token in XML document. Three label are added to each token; parent labels, left sibling and right sibling; to facilitate insertion and update process and makes this cost constant, in contrast of previous approaches that need to relabelled following or descendants tokens. The method can also be used for data-centric and document-centric documents. Experiments on this method show its ability to main- tain document structure at a low cost price and building of the original document is straight forward.
Active network based management for QoS assured multicast delivered media

In this paper, we address the application specific management and monitoring of highly dynamic services. The target service is the multicasting of video streams to residential users over an ATM/ADSL access network. The high dynamicity is given by the frequent channel change operation performed in parallel by thousands of users. We approach the monitoring issue using the active network based paradigm, where code is dynamically deployed and executed in the network.
Document reconstruction by layout analysis of snippets

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. Also skew detection of scanned documents is performed to support OCR algorithms that are sensitive to skew. In this paper document analysis is applied to snippets of torn documents to calculate features for the reconstruction. Documents can either be destroyed by the intention to make the printed content unavailable (e.g. tax fraud investigation, business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, inpainting and texture synthesis techniques. In this paper the possibility of document analysis techniques of snippets to support the matching algorithm by considering additional features are shown. This implies a rotational analysis, a color analysis and a line detection. As a future work it is planned to extend the feature set with the paper type (blank, checked, lined), the type of the writing (handwritten vs. machine printed) and the text layout of a snippet (text size, line spacing). Preliminary results show that these pre-processing steps can be performed reliably on a real dataset consisting of 690 snippets.
Performance Testing: Methodologies and Tools

Performance testing is important for all types of applications and systems, especially for life critical applications in Healthcare, Medical, Biotech and Drug discovery systems, and also mission critical applications such as Automotives, Flight control, defense systems, etc. This paper presents performance testing concepts, methodologies and commonly used tools for a variety of existing and emerging applications. Scalable virtual distributed applications in the cloud pose more challenges for performance testing, for which solutions are rare, but available; one of the major providers is HP Loadrunner. Keywords: Performance testing, Application performance, Cloud computing
Video Game Values: Play as Human-Computer Interaction

Video games are a form of software and thus an obvious object of study in Human-Computer Interaction (HCI). Interaction with video games differs from the usual understanding of HCI, however, because people play video games rather than use them. In this dissertation we ask: " How can we analyse human-computer interaction in video games when the interaction in question is play? " We propose video game values, defined as sustained beliefs about preferable conduct during play, as a basis for video game HCI. In order to describe and analyse play we use activity theory, focusing on how the interface mediates players' beliefs about preferable conduct. Activity theory allows us to address the multiple levels of context and detail in play as well as the role of conflict. We employ a qualitative case study methodology to gather data about five popular video games: Civilization III, Fable, Grand Theft Auto: San Andreas, Half-Life 2, and The Sims 2. Our core data comes from observation and interview sessions with twenty-five experienced players of these games. We collected further data based on the games' interfaces, participant observation, and documentation such as manuals and walkthroughs. We make three key contributions to video game HCI: 1) We introduce video game values as a means to analyse play as a form of human-computer interaction and show how the values of PAIDIA and LUDUS influence all aspects of play; 2) we develop a video game activity framework for describing and analysing video game play at multiple levels of detail and context; and 3) we extend the video game activity framework to include contradictions and breakdowns as a means to describe and analyse the role of conflict and challenge in video game play.
Coarse grain load balance algorithm for detecting similar regions in DNA and proteins sequences

The tremendous quantity and quality of data obtained by conformations of DNA and protein sequences makes their analysis very time consuming, complex, expensive and impractical. Therefore, the feasible way to identify new sequences is to compare them with well-known sequences available in established genetic databank. Comparing sequences may reveal functional, structural, and evolutionary analogies between sequences. Needleman and Wunsch (NW), and Smith and Waterman (SW) algorithms are pioneers in dynamic programming matrix for comparing two sequences with gap penalty function. However, for long sequences both methods contribute toward time and space complexity. FASTA and BLAST are heuristic method based on hitting techniques for fast detection of similar region; unfortunately they produced results with no sensitivity. There remains a need for an efficient method(s) that can detect similar regions in two sequences with accurate results and reasonable time. In this paper, we extend an existing approach to develop an efficient parallel algorithm for pairwise local sequence alignment. Our method is based on load-balancing algorithm with the CPU scheduling technique in order to accelerate the calculation of data-dependency problem in sequences alignment. Using X86-based PC with eight logical processors we able to apply 4 MBP on the proposed algorithm with a speedup of 33% increase compared to the original SW algorithm.
ANALISA KINERJA SISTEM TRANSMIT DIVERSITY DALAM MENTRANSMISIKAN DATA CITRA DIGITAL PADA KANAL RAYLEIGH DAN RICIAN FADING

Pengiriman data atau informasi saat ini tidak lagi hanya dalam bentuk teks, tetapi juga dapat berupa gambar (citra). Citra sebagai suatu komponen multimedia memegang peranan penting dalam sistem komunikasi khususnya informasi visual. Dalam proses pengiriman informasi sering mengalami gangguan salah satunya adalah fading. Untuk mengurangi efek dari fading dapat dilakukan dengan teknik diversitas atau sering disebut diversity. Analisa dilakukan dengan memperhatikan kinerja sistem transmit diversity dalam mentransmisikan data berupa citra digital pada kanal Rayleigh dan Rician fading . Selain itu juga dilakukan perbandingan kinerja sistem transmit diversity 2 pemancar dan 2 penerima dengan SISO, serta variasi antenna 1 pemancar 2 penerima dan 2 pemancar 1 penerima. Parameter jaringan yang diujikan adalah parameter SNR, BER, dan PSNR dimana dalam penelitian menggunakan perangkat lunak Matlab. Dari hasil analisa diperoleh bahwa jika di tinjau berdasarkan perbandingan nilai BER dan SNR maka sistem transmit diversity 2Tx 2Rx memiki kinerja yang lebih baik dibandingkan dengan teknik SISO maupun dengan sistem dengan 1Tx 2Rx dan 2Tx 1Rx. Sedangkan dari kondisi kanal yang digunakan, maka sistem transmit diversity 2Tx 2Rx pada kanal Rician dengan K=15 dB memiliki kinerja yang lebih baik dibandingkan Rayleigh dan Rician fading dengan K=-35dB. Untuk nilai PSNR, sistem transmit diversity 2Tx 2Rx menghasilkan citra digital yang terekonstruksi dengan kategori excellent saat SNR=15 dB
Two-level blackout probabilistic risk analysis: application to a test system

Blackouts in power systems are due to cascading outages. Based on the analysis of past events, the typical development of such a cascading failure can be split in two phases. In an initial slow cascade phase, an initiating contingency triggers a thermal transient which increases significantly the likelihood of additional contingencies. The loss of additional elements can then trigger an electrical instability. This is at the origin of the subsequent fast cascade, where a rapid succession of events can possibly lead the system to blackout. Once a blackout occurred, the recovery period can be viewed as an additional (and last) phase. The blackout PRA can be decomposed in three levels, according to these three phases. Levels I and II were previously developed and applied, but separately. The aim of this paper is to apply a two-level blackout PRA to a small test system in order to study in a coupled way the two phases of a cascading failure.
Neural networks for speech and sequence recognition

Connectionist models Learning theory The back-propagation algorithm Introduction to back-propagation Formal description Heuristics to improve convergence and generalization Extensions Integrating domain knowledge and learning from examples Automatic speech recognition Importance of pre-processing input data Input coding. Input invariances Importance of architecture constraints on the network Modularization Output coding Sequence analysis Introduction Time delay neural networks Recurrent networks BPS Supervision of a recurrent network does not need to be everywhere Problems with training of recurrent networks Dynamic programming post-processors Hidden Markov models Integrating ANNs with other systems Advantages and disadvantages of current algorithms for ANNs Modularization and joint optimization Radial basis functions and local representation Radial basis funtions networks Neurobiological plausibility Relation to vector quantization, clustering and semi-continuous HMMs Methodology Experiments on phoneme recognition with RBFs Density estimation with a neural network Relation between input PDF and output PDF Density estimation Conclusion Post-processors based on dynamic programming ANN/DP hybrids ANN/HMM Hybrids ANN/HMM Hybrid: Phoneme recognition experiments ANN/HMM hybrid: online handwriting recognition experiments.
A modular approach to analyzing biological networks

Author(s): Sivakumar, Hari | Advisor(s): Hespanha, Joao P | Abstract: This monograph addresses the decomposition of biochemical networks into functional modules that preserve their dynamic properties upon interconnection with other modules, which permits the inference of network behavior from the properties of its constituent modules. The modular decomposition method developed here also has the property that any changes in the parameters of a chemical reaction only affect the dynamics of a single module. To illustrate our results, we define and analyze a few key biological modules that arise in gene regulation, enzymatic networks, and signaling pathways. We also provide a collection of examples that demonstrate how the behavior of a biological network can be deduced from the properties of its constituent modules, based on results from control systems theory. We then use this modular decomposition method to analyze the p53 core regulation network, which plays a key role in tumor suppression in many organisms. By decomposing the network into modules, we study the evolution of the p53 core regulation network and conduct a formal analysis of the different network configurations that emerge in the evolutionary path to complexity from putative primordial organisms to more evolved vertebrates. We develop an algorithm to solve the system of equations that describe the network behavior by interconnecting the network modules systematically, as these equations are typically difficult to solve using standard numerical solvers. In the process, we qualitatively compare the distinct types of switching behaviors that each network can exhibit. We demonstrate how our novel model for the core regulation network matches experimentally observed phenomena, and contrast this with the plausible behaviors that primordial network configurations can admit. Specifically, we show that the complexity of the p53 network in humans and evolved vertebrates permits a wide range of behaviors that can bring about distinct cell fate decisions, but that this is not the case for primordial organisms.
On List Recovery of High-Rate Tensor Codes

We continue the study of list recovery properties of high-rate tensor codes, initiated by Hemenway, Ron-Zewi, and Wootters (FOCS’17). In that work it was shown that the tensor product of an efficient (poly-time) high-rate globally list recoverable code is <italic>approximately</italic> locally list recoverable, as well as globally list recoverable in <italic>probabilistic</italic> near-linear time. This was used in turn to give the first capacity-achieving list decodable codes with (1) local list decoding algorithms, and with (2) <italic>probabilistic</italic> near-linear time global list decoding algorithms. This also yielded constant-rate codes approaching the Gilbert-Varshamov bound with <italic>probabilistic</italic> near-linear time global unique decoding algorithms. In the current work we obtain the following results: 1) The tensor product of an efficient (poly-time) high-rate globally list recoverable code is globally list recoverable in <italic>deterministic</italic> near-linear time. This yields in turn the first capacity-achieving list decodable codes with <italic>deterministic</italic> near-linear time global list decoding algorithms. It also gives constant-rate codes approaching the Gilbert-Varshamov bound with <italic>deterministic</italic> near-linear time global unique decoding algorithms. 2) If the base code is additionally locally correctable, then the tensor product is (genuinely) locally list recoverable. This yields in turn (non-explicit) constant-rate codes approaching the Gilbert-Varshamov bound that are <italic>locally correctable</italic> with query complexity and running time <inline-formula> <tex-math notation="LaTeX">$N^{o(1)}$ </tex-math></inline-formula>. This improves over prior work by Gopi et. al. (SODA’17; IEEE Transactions on Information Theory’18) that only gave query complexity <inline-formula> <tex-math notation="LaTeX">$N^{ \varepsilon }$ </tex-math></inline-formula> with rate that is exponentially small in <inline-formula> <tex-math notation="LaTeX">$1/ \varepsilon $ </tex-math></inline-formula>. 3) A nearly-tight combinatorial lower bound on output list size for list recovering high-rate tensor codes. This bound implies in turn a nearly-tight lower bound of <inline-formula> <tex-math notation="LaTeX">$N^{\Omega (1/\log \log N)}$ </tex-math></inline-formula> on the product of query complexity and output list size for locally list recovering high-rate tensor codes.
History from K-4: What Can We Really Do?

The short answer is “quite a bit. ” In this article, the author makes the case for a K-4 history program that is ambitious in scope while varied and energetic in style. She argues that in the past educators have underestimated the interests and abilities of young children. She maintains that history is the riveting story of the human past, and that no one loves a good story more than a young child. The article is both theoretical and practical. Analyzing the Massachusetts History and Social Curriculum Framework as well as the Core Knowledge Sequence, as they apply to elementary instruction, the author delineates the scope of a good K–4 program and describes and evaluates principles and methods of teaching history to young children.