Miracle-dz/UniFAR-FTUs huggingface dataset:
Using COTS Components for Real-Time Processing of SAR Systems

The research activities are divided into three stages, with each stage lasting approximately one month. In the first stage ', we discussed the design trade-off for real-time SAR processing. The objectives are to maximize the processing density (MFLOPS/volume, MFLOPVweight, MFLOPS/watt), to minimize the hardware cost (MFLOPS/dollar), to minimize the software development cost, and to maximize software generality. As an example, we briefly described the Mercury's RACEway multicomputer as a low cost, high-performance, embedded heterogeneous message-passing multicomputer system. In the second stage 2, we presented a procedure to divide the SAR signal processing into pipelined parallel steps that can be performed on a parallel computer. We also estimated the throughput, memory, and 1/0 bandwidth requirements using the NASA/JPL GeoSAR system. Using again the Mercury's RACEway multicomputer as an example, we showed how to configure the hardware for SAR signal processing. Parallel programming was the topic in this final stage '. First, we gave an overview of the standard multicomputing platforms and parallel programming models. We then compared and contrasted "scientific" versus "real-time" processing/programming. Next, we showed the hardwarehofiware co-design approach for SAR signal processing. Then, we discussed the current issues in parallel programming. Finally, we described a typical software architecture for parallel programming of SAR signal processing.
Electro-acupuncture could be an effective pretreatment for cerebral ischemia

Electroacupuncture, the integration of traditional Chinese acupuncture and modern electrotherapy, has been used for clinical treatment of cerebral ischemic disease in both eastern and western countries; however, the mechanism underlying its effects is still unknown. It is well known that excessive glutamate results in neuronal excitotoxicity after ischemic stroke. Previous studies have indicated that electro-acupuncture may downregulate the overactivation of glutamate after ischemia, and a recent study implied that electro-acupuncture prior to ischemia could induce brain ischemic tolerance. Based on the present information, we hypothesize that electro-acupuncture could be an effective pretreatment for cerebral ischemia by regulating the glutamatergic system.
A small-size handshake robot system for a generation of handshake approaching motion

Humans often greet one another using a handshake, a common gesture of friendship. In the case of a human and a robot, the robot can smoothly begin to communicate and coexist with humans without feelings of aversion in humans; if it generates a handshake motion that is emotionally acceptable to humans. Thus, we have proposed a handshake request motion model and a handshake respond motion model to generate a handshake approaching motion prior to actually shaking hands with the human. In the handshake request motion model, the robot stretches its hand out to a human to request a handshake. In the handshake respond motion model, the robot responds a handshake when the human requests a handshake. The effectiveness of these models is demonstrated by experiments using a handshake robot system which is fabricated based on the average size of a human arm. Therefore, in this paper, we develop a small-size handshake robot system that uses the handshake request motion model and the handshake respond motion model to generate a handshake approaching motion with a human. The effectiveness of the robot system is demonstrated by sensory evaluation. Furthermore, a handshake motion between robots is realized by using two small-size handshake robot systems which are adopted these models.
What is phenotypic plasticity and why is it important

Phenotypic plasticity, the capacity of a single genotype to exhibit variable phenotypes in different environments, is common in insects and is often highly adaptive. Virtually any abiotic or biotic factor can serve to induce plasticity, and resulting changes vary from harmful susceptibilities to highly integrated and adaptive alternative phenotypes. Numerous physiological mechanisms accomplish plasticity, including transcription, translation, enzyme, and hormonal regulation, producing local or systemic responses. The timing, specificity, and speed of plastic responses are critical to their adaptive value. Understanding plasticity requires knowing the environment, physiological mechanisms, and fitness outcomes. Plasticity is thought to be evolutionarily favored under specific conditions, yet many theoretical predictions about benefits, costs, and selection on plasticity remain untested. The ecological consequences of plasticity range from simple environmental susceptibilities to mediating interspecific interactions, and extend to structuring of ecological communities, often through indirect effects. Phenotypic plasticity, through its ecological effects, can facilitate evolutionary change and speciation. Plasticity is important because it is an encompassing model to understand life on earth, it can increase fitness, generate novelity, and facilitate evolution, it structures ecological communities, and it has numerous practical applications. As such, all biologists should understand phenotypic plasticity.
Evaluating energy mechanisms for routing in wireless sensor networks

Sensor networks are often characterized by their large number of nodes that usually uses wireless communication, widely spread over sizeable geographical areas to monitor the environment. Considering their limited processing, memory and energy capacities, it becomes necessary for support mechanisms to ensure the rational use of their resources. It is in this context that we examine routing protocols, and show how these can be energy conscious by avoiding nodes with less spare energy. This work presents a family of protocols; we refer to as OPER (on-demand power-efficient routing), which seeks to save node energy by evaluating the energy consumption impact of a given route. The conducted simulations show that OPER outperforms other algorithms that are evaluated
Collection of Quantitative Data on Security Incidents

Quantitative data about security threats is a precondition for a precise assessment of security risks and consequently for an efficient management of information security. Currently such data is hardly available, especially for small and medium-sized organizations. In this paper we discuss different ways of gathering quantitative data and present a new approach for the collection of historical data on security incidents. We propose a platform that collects, aggregates and evaluates data on security incidents from multiple organizations. We identify basic requirements for such a platform and show approaches for satisfying them. We especially emphasize the aspects of security and fairness. Finally we introduce a prototype that shows how an implementation could look like
Incorporating Historic Knowledge into a Self-Optimizing Communication Library for High Performance Computing

Emerging computing systems have a wide variety of hardware and software components influencing the performance of parallel applications, presenting end-users with a (nearly) unique execution environment on each parallel machine. One of the big challenges of High Performance Computing is therefore to develop portable and efficient codes for any execution environment. The Abstract Data and Communication Library (ADCL) is a self-optimizing runtime communication library aiming at providing the highest possible performance for application level communication operations. The library provides for a given communication pattern a large number of implementations and incorporates a runtime selection logic. This selection aims at adaptively choosing the best performing implementation on the current platform and for the given problem. In this paper, we present a recent enhancement to the library which introduces the capability of utilizing information from previous executions in order to minimize the overhead of the runtime selection logic which mainly stems from testing underperforming implementations. We introduce the notion of similar problems by using a proximity measure for a given operation. The approach is evaluated for the n-dimensional neighborhood communication on different platforms and for a large range of different problems.
Study on a robust approach of lane departure warning algrithm

Traffic lane detection technology has attracted increasing attention in field of autonomous driving. After analyzing the characteristic of the images of the traffic lane, a robust approach was studied based on lane recognition. The adaptive threshold based on Otsu algorithm was used for different illumination demands during image preprocessing. Lanes were then detected by Hough Transformation. In lane tracking, the region of interest was established with parameters through the Kalman predictor. Then lanes were searched in the region. At last, the lane departure wanrning module was added to the algorithm according to the distance between the vehicle and the lane. Experiment results indicate that the algorithm has good robustness and efficiency.
Blind equalization of PPM signals using third-order moments

In pulse-position modulation (PPM) signaling, the time location of short-duration pulses are used to convey information over a communication channel. For successful noncoherent reception, the channel duration must be short compared to the symbol interval. This paper analyzes the use of third moments in a blind adaptive equalizer setting to limit the effective delay spread of the channel. Results detail the global convergence properties of the proposed method, showing that the parameters approach ISI-free settings under general conditions.
Research on rotary dump health monitoring expert system based on causality diagram theory

Causality diagram theory is a kind of uncertainty reasoning theory based on the belief network. It expresses the knowledge and causality relationship by diagrammatic form and direct causality intensity. Furthermore, it resolves the shortages of the belief network, and realizes a hybrid model which can process discrete and continuous variations. The theory of causality diagram model and the steps of causality diagram reasoning methodology are studied in this paper, and a model of rotary dump health monitoring expert system is proposed. In addition, this paper establishes the causality diagram of rotary dump and converts it to the causality tree. According to the causality tree of rotary dump, the causality diagram reasoning methodology composed of four steps is described. Finally, an application of rotary dump health monitoring expert system is shown, and the system performance analysis is discussed.
Management of Process Knowledge-BPM in an EA context

As a discipline, EA is practised both by IT analyst and business analysts. In some organizations, EA is primarily practised by IT people, and the results represent the detailed understanding of the existing IT infrastructure. In other organizations, EA is used for describing business architecture, with a focus on business strategy, the services provided by the business and the business processes established to deliver those services. Mature EA organizations practice holistic EA, where the business-oriented perspective is joined with the IT-oriented perspective, describing how the IT infrastructure actually supports the business.
HaptiCast: A Physically-Based 3D Game with Haptic Feedback

Modern haptic technology allows users to receive realistic kinesthetic and tactile cues in a computer generated environment. When applied to video games, it gives players a higher sense of immersion as well as new and interesting ways to interact with the game environment. In this paper, we present a game which acts as an experimental framework for assessing haptic effects in 3D games. In HaptiCast , players assume the role of a wizard with an arsenal of haptically-enabled wands which they may use to interact with the game world. We discuss the integration of haptic feedback in this first-person shooter style video game that uses a “vanilla” 3D game engine. The haptic rendering algorithms and effects used in our approach are presented, along with some non-haptic features which enhance this modality.
Concurrency Control in Distributed MRA Index Structure

Answering aggregate queries like sum, count, min, max over regions containing moving objects is often needed for virtual world applications, real-time monitoring systems, etc. Since the data set is usually very large and some queries require significant processing resources, quite often such data is stored in a distributed system wherein each system handles a partition of the whole space and manages all objects in that partition. Objects keep switching from one system to another as they change their location. Currently there are no known efficient techniques for getting aggregates over moving objects while ensuring that their position updates remains atomic to the read. We introduce an efficient technique for finding aggregates over mobile objects with data stored in a distributed system by extending the multi-resolution aggregate trees to work in a distributed system and over mobile objects. We propose a multi-phase update algorithm that is highly concurrent with respect to read queries and at the same time makes modifications to the aggregate tree atomic with respect to read queries. The analysis and experimental results prove the efficacy of this update algorithm as compared to the naive update technique.
Dynamic pricing for efficient workload colocation

Pricing models for virtualized (cloud) resources are meant to reflect the operational costs and profit margins for providers to deliver specific resources or services to customers subject to an underlying Service Level Agreements (SLAs). While the operational costs incurred by cloud providers are dynamic – they vary over time, depending on factors such as energy cost, cooling strategies, and overall utilization – the pricing models extended to customers are typically fixed – they are static over time and independent of aggregate demand. This disconnect between the cost incurred by a provider and the price paid by a customer results in an inefficient marketplace. In particular, it does not provide incentives for customers to express workload scheduling flexibilities that may benefit them as well as cloud providers. In this paper, we propose a new dynamic pricing model that aims to address this marketplace inefficiency by giving customers the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. We present the architecture and algorithmic blueprints of a framework for workload colocation, which provides customers with the ability to formally express workload scheduling flexibilities using Directed Acyclic Graphs (DAGs), optimizes the use of cloud resources to collocate clients’ workloads, and utilizes Shapley valuation to rationally – and thus fairly in a game-theoretic sense – attribute costs to customer workloads. In a thorough experimental evaluation we show the practical utility of our dynamic pricing mechanism and the efficacy of the resulting marketplace in terms of cost savings.
Blurred Image Enhancement Using Contrast Stretching , Local Edge Detection and Blind Deconvolution

Blurring of image is common problem while taking picture of an object in motion or due to shooting situations. Various methods have been proposed to enhance the blurred image. Here contrast stretching is used for obtaining deblurred image. In the proposed method local edge detection is applied on original as well as contrast stretched image. The set of edges obtained from both the images are fussed in order to get sharper edges. The original image and contrast stretched image is converted into gray scale image from RGB image before applying local edge detection to avoid detection of false edges. Since image distortion information is unknown, so on the obtained fussed image blind deconvolution is applied to get deblurred image.
Mechanism of non‐spliceosomal mRNA splicing in the unfolded protein response pathway

The unfolded protein response is an intracellular signaling pathway that, in response to accumulation of misfolded proteins in the lumen of the endoplasmic reticulum (ER), upregulates transcription of ER resident chaperones. A key step in this pathway is the non‐conventional, regulated splicing of the mRNA encoding the positive transcriptional regulator Hac1p. In the yeast Saccharomyces cerevisiae, the bifunctional transmembrane kinase/endoribonuclease Ire1p cleaves HAC1 mRNA at both splice junctions and tRNA ligase joins the two exons together. We have reconstituted HAC1 mRNA splicing in an efficient in vitro reaction and show that, in many ways, the mechanism of HAC1 mRNA splicing resembles that of pre‐tRNA splicing. In particular, Ire1p endonucleolytic cleavage leaves 2′,3′‐cyclic phosphates, the excised exons remain associated by base pairing, and exon ligation by tRNA ligase follows the same chemical steps as for pre‐tRNA splicing. To date, this mechanism of RNA processing is unprecedented for a messenger RNA. In contrast to the striking similarities to tRNA splicing, the structural features of the splice junctions recognized by Ire1p differ from those recognized by tRNA endonuclease. We show that small stem–loop structures predicted to form at both splice junctions of HAC1 mRNA are required and sufficient for Ire1p cleavage.
Integrating industrial middleware in Linked Data collaboration networks

Within the Semantic Web field, Linked Data is an emerging technology offering advantages in terms of flexibility and openness also for industrial environments. It provides a powerful information model with simple extension mechanisms and a cross-domain character, which makes it possible to span flexible information spaces across enterprise boundaries. Thus, it is able to represent all facets of a digital plant, like devices and processes. Although developed with the focus on static data, it is also capable of handling dynamic data if the latter is adapted appropriately. This article describes these requirements and presents a concept for integrating process data into the Linked Data information space. A separation between a semantic description of the access to process data and the service providing exactly this information allows efficient and integration-ready access to data. The necessary vocabulary is described as well as the information model. A prototypical implementation of OPC UA integration into Linked Data is presented. The article further discusses the current status of the approach and provides future research challenges.
Real-time observation of DNA looping dynamics of Type IIE restriction enzymes NaeI and NarI

Many restriction enzymes require binding of two copies of a recognition sequence for DNA cleavage, thereby introducing a loop in the DNA. We investigated looping dynamics of Type IIE restriction enzymes NaeI and NarI by tracking the Brownian motion of single tethered DNA molecules. DNA containing two endonuclease recognition sites spaced a few 100 bp apart connect small polystyrene beads to a glass surface. The position of a bead is tracked through video microscopy. Protein-mediated looping and unlooping is then observed as a sudden specific change in Brownian motion of the bead. With this method we are able to directly follow DNA looping kinetics of single protein–DNA complexes to obtain loop stability and loop formation times. We show that, in the absence of divalent cations, NaeI induces DNA loops of specific size. In contrast, under these conditions NarI mainly creates non-specific loops, resulting in effective DNA compaction for higher enzyme concentrations. Addition of Ca2+ increases the NaeI-DNA loop lifetime by two orders of magnitude and stimulates specific binding by NarI. Finally, for both enzymes we observe exponentially distributed loop formation times, indicating that looping is dominated by (re)binding the second recognition site.
Impulsiveness without discounting: the ecological rationality hypothesis

Observed animal impulsiveness challenges ideas from foraging theory about the fitness value of food rewards, and may play a role in important behavioural phenomena such as cooperation and addiction. Behavioural ecologists usually invoke temporal discounting to explain the evolution of animal impulsiveness. According to the discounting hypothesis, delay reduces the fitness value of the delayed food. We develop an alternative model for the evolution of impulsiveness that does not require discounting. We show that impulsive or short–sighted rules can maximize long–term rates of food intake. The advantages of impulsive rules come from two sources. First, naturally occurring choices have a foreground–background structure that reduces the long–term cost of impulsiveness. Second, impulsive rules have a discrimination advantage because they tend to compare smaller quantities. Discounting contributes little to this result. Although we find that impulsive rules are optimal in a simple foreground–background choice situation in the absence of discounting, in contrast we do not find comparable impulsiveness in binary choice situations even when there is strong discounting.
Climate Policies and Induced Technological Change: Impacts and Timing of Technology Subsidies

We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning-by-doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are introduction or expansion constraints for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry and thight carbon constraints.
Binarization by Local K-means Clustering for Korean Text Extraction

Text information in a natural scene is very useful and important for understanding images. Detection and extraction of text information in such natural images have been used in many applications. Many conditions of natural scene make the problem of text segmentation quite intractable. In this paper, an effective method for the segmentation and binarization of Korean texts from signboard images is proposed, which is robust in blurred images, uneven illumination and strong boundary text images. The proposed approach is based on local K-means clustering on separate words in text region. Firstly, detected text region is divided into local areas with relatively uniform illumination, and then using 3-means clustering with Euclidean distance has been applied to segment text from the background. By dividing the region of interest into local areas, the effect of uneven lighting has been minimized. The comparison with Otsu's method and 2-means clustering based on intensity will be representation in some metrics. Natural images from the test database, collected from mobile devices, are used in the experiment and the results show the performance of the proposed method.