Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Modeling of Charge Trapping in Molecule Based Flash Memories

To keep up with the increase in demand for storing data, flash memories have been scaled down dramatically and stacked by the semiconductor industry. Furthermore, processing large data has highlighted the limitations of the von Neumann architecture. To overcome this, different types of memory devices like Resistive Random-Access Memories (RRAMs) have also gained a lot of importance. Hence, carrier dynamics in oxides has gained significant traction in recent years. In this work, we discuss the kinetic Monte Carlo methodology as implemented in our integrated simulation environment NESS (Nano-Electronic Simulation Software) that allows us to study carrier transport in the oxide using accurate physics based models. As an example, we study the retention characteristics in a molecule based flash memory.
Generative Capability

How do firms leverage existing innovations for the next round of innovations? We develop a novel concept of generative capability in product innovation to address this question. Generative capability applies the mechanisms of iteration and rapid knowledge integration to guide innovation activities. Based on panel data from 2001 to 2009 of 56 344 manufacturing firm-year observations in China, we find that institutional complexity and internationalization are positively related to generative capability, that state-owned enterprises (SOEs) have lower levels of generative capability than do firms with other ownership structures, and that generative capability improves firms’ performance significantly. We conclude with a discussion of the implications of generative capability for theoretical development and point to a number of fruitful areas for future research.
Side-View Raman Microendoscope with Micro Stepping Motor and its Ex-Vivo Test for Real-Time Cancer Detection

This paper reports a microactuator-enabled sideview Raman microendoscope device developed for realtime, angle-resolved screening of cancers growing within narrow lumens including peripheral lung bronchi. A tubular micro stepping motor, assisted with a self-sustained ferrofluid bearing, is custom developed and integrated with a Raman spectroscopic probe via biocompatible packaging for full circumferential step scan of a probing laser beam. The performance of microfabricated prototype is assessed using reference chemicals to show the targeted function for circumferentially localized Raman data collection and analysis. Ex-vivo testing was successfully conducted to demonstrate intraluminal Raman analysis in animal bronchi with $>\ 99\%$ spectral accuracy in real time.
Fuzzy Hegselmann-Krause Opinion Dynamics with Opinion Leaders

In this paper, from the perspective of opinion dynamics theory, we investigate the intrinsic interaction principles of a group of autonomous agents and develop a fuzzy opinion dynamics model with leaders. First, this paper divides group agents into three subgroups: opinion followers, positive opinion leaders, and negative opinion leaders according to the opinion’s update manner and influence. Then, we consider the uncertainty of the agents’ opinion gaps and apply the Fuzzy Inference Machine to the effects of the leaders’ opinions on a certain follower’s opinion. The innovation of this paper is that the weight is distributed from 0 to 1 in accordance with the closeness among the opinion leaders and followers, which is closer to real life. Finally, the simulation results show that the proposed model can effectively explain the opinion interaction and evolution and conforms with the existing theoretical results in the field of opinion dynamics.
A Successfully Treated Case of Small Bowel Obstruction Caused by the Seed of a Japanese Apricot, and Leading to Acute Renal Failure

Shinsuke SATO, Takeo MAEKAWA, Koichi SATO, Hiroshi MAEKAWA, Kazutomo OUCHI, Hironobu SENGOKU, Mutsumi SAKURADA, Tomoyuki KUSHIDA, Motomi NASU, Akira TSUKADA and Yuji SUGIYAMA Department of Surgery, Juntendo University Shizuoka Hospital Small bowel obstruction caused by food impaction is uncommon and difficult to diagnose accurately, because there are no characteristic symptoms. We report here the case of small bowel obstruction caused by the seed of a Japanese apricot, and leading to acute renal failure. Surgery is the preferred treatment for most cases. We first attempted to treat the food impaction by milking the mass into the cecum. Enterotomy or small bowel resection should be performed when milking is unsuccessful. Investigating the dietary habits of the patient played an important role in diagnosing small bowel obstruction caused by food impaction.
Automated analysis of radiology images using Convolutional Neural Networks

Radiography has outmost importance in recognizing several life threatening diseases. Nowadays, the X-ray machines can produce more examinations on a day what the physicians can analyze. In this paper, we propose a light-weight, automated, scalable method to analyze X-ray images. We designed and trained a Deep Convolutional Neural Network to recognize the presence of 16 medical conditions. We evaluated our method on publicaly available data and showed that our method is on par with state-of-the-art but has much lower computational burden.
Covert Channel Establishment Through the Dynamic Adaptation of the Sequential Probability Ratio Test to Sensor Data in IoT

Owing to their hidden natures, covert channels can be utilized such that trojan applications can communicate stealthily with each other or exchange stolen private information without being revealed. To prevent damage incurred by covert channels, researchers have preemptively scrutinized diverse covert channels that can be devised by an attacker. Although covert channels based on sensor data may interest an attacker because sensing operation is a key task in Internet of Things (IoT), we do not find any covert channel studies that adapted the Sequential Probability Ratio Test (SPRT) to sensor data except our prior study [27], where the SPRT is applied to sensor data in Android systems before an attacker’s conception; however, our previous study showed limitations owing to the static nature of the SPRT parameter settings and the method of mapping sensor data to sample types for covert channel creation. To demonstrate that these limitations can be pacified, we propose a covert channel that dynamically applies the SPRT to sensor data in IoT. In our proposed covert channel, stealthy information bit 1 (resp. 0) is encoded to and decoded from a sequence of sensor data when the SPRT with dynamic parameter settings accepts an alternate (resp. null) hypothesis. We implement our proposed covert channel in Raspberry Pi 3 Model B devices and evaluate it in terms of various metrics. Evaluation results indicate that every encoded stealthy information byte is successfully decoded in our covert channel. Furthermore, 3.513 samples and 28.105 SPRT executions at the most are required for encoding/decoding a stealthy information byte in our devised covert channel on an average, thus resulting in fast encoding/decoding in our covert channel. Finally, our developed covert channel yields a throughput ranging from 4097.5 to 9061.67 bits/sec.
A Selection Method of Ring Oscillators for An On-Chip Digital Temperature And Voltage Sensor

An on-chip digital sensor using three types of ring oscillators (ROs: Ring Oscillators) has been proposed to measure temperature and voltage of a VLSI. Each RO has inherent frequency characteristics with respect to temperature and voltage, which differ from those of the other two ROs. Measurement accuracy of the sensor depends on the combination of the ROs. This paper proposes a RO-selection method for the sensor with high accuracy. The proposed method takes particular note of temperature or voltage sensitivity as well as linearity of the RO characteristics. Evaluation experiments with SPICE simulation in 65 nm CMOS technology show that the temperature and voltage accuracies of the sensor are 2.744 °C and 3.825 mV, respectively, and the selected combination was a nearly optimal from a menu of many different ROs.
Evaluation of TMR effectiveness for soft error mitigation in SHyLoC compression IP core implemented on Zynq SoC under heavy ion radiation

This work analyses the results of applying Triple Modular Redundancy (TMR) to the SHyLoC CCSDS-121 IP, a hardware implementation of the Consultative Committee for Space Data Systems (CCSDS) 121.0-B-2 lossless compression standard, a universal compressor specifically thought for space applications. The results obtained in a radiation experiment performed at the North Area new facilities at CERN are presented. The objective is to evaluate the robustness applying TMR to the design, by comparing to the unhardened implementation of the SHyLoC CCSDS-121 IP, when it is working under Ultra High Energy radiation. Both TMR and unhardened implementations of the SHyLoC CCSDS-121 IP were implemented in a Xilinx Zynq XC7Z020 System-on-Chip and radiated with Pb ions. Compression results were compared against a golden reference, obtaining a Mean Time To Failure (MTTF) a 40% higher for the TMR design than for the unhardened one.
Boat Hunting with Semantic Segmentation for Flexible and Autonomous Manufacturing

Customized mass production of boats and other vehicles requires highly complex manufacturing processes that need a high amount of automation. To enhance the efficiency of such systems, sensing is of paramount importance to provide robots with detailed information about the working environment. In this paper, we propose the use of semantic segmentation to detect the key elements involved in production, to boost automation in the production process. Our main focus is on the sanding process of these tools by means of a robot. We demonstrate the potential of these techniques in an industrial environment featuring a lower degree of variability with respect to the domestic scenes typically considered in the literature. In the production environment, however, higher performances are required to address challenging manufacturing operations successfully. In this work, we also show that exploiting contextual cues and multiple points of view can further boost the reliability of our system, which provides useful data to the other robot modules in charge of navigation, work station recognition, and other tasks. All the methods have been thoroughly validated on the IASLAB RGB-D COROMA Dataset, that was created on purpose. It consists of 46589 RGB-D frames, whose annotation was speeded up thanks to our optimized annotation pipeline.
The Female Circumcision Controversy: An Anthropological Perspective

To the Western eye, there is something jarringly incongruous, even shocking, about the image of a six-year-old girl being held down by loving relatives so that her genitals can be cut. Yet two million girls experience this each year. Most Westerners, upon learning of the practice of female circumcision, have responded with outrage; those committed to improving the status of women have gone beyond outrage to action by creating various programs for "eradicating" the practice. But few understand the real life complexities families face in deciding whether to follow the traditional practices or to take the risk of change. In The Female Circumcision Controversy, Ellen Gruenbaum points out that Western outrage and Western efforts to stop genital mutilation often provoke a strong backlash from people in the countries where the practice is common. She looks at the validity of Western arguments against the practice. In doing so, she explores both outsider and insider perspectives on female circumcision, concentrating particularly on the complex attitudes of the individuals and groups who practice it and on indigenous efforts to end it. Gruenbaum finds that the criticisms of outsiders are frequently simplistic and fail to appreciate the diversity of cultural contexts, the complex meanings, and the conflicting responses to change. Drawing on over five years of fieldwork in Sudan, where the most severe forms of genital surgery are common, Gruenbaum shows that the practices of female circumcision are deeply embedded in Sudanese cultural traditions-in religious, moral, and aesthetic values, and in ideas about class, ethnicity, and gender. Her research illuminates both the resistance to and the acceptance of change. She shows that change is occurring as the result of economic and social developments, the influences of Islamic activists, the work of Sudanese health educators, and the efforts of educated African women. That does not mean that there is no role for outsiders, Gruenbaum asserts, and she offers suggestions for those who wish to help facilitate change. By presenting specific cultural contexts and human experiences with a deep knowledge of the tremendous variation of the practice and meaning of female circumcision, Gruenbaum provides an insightful analysis of the process of changing this complex, highly debated practice.
Scalenet: A Convolutional Network to Extract Multi-Scale and Fine-Grained Visual Features

Many convolutional neural networks have been proposed for image classification in recent years. Most tend to decrease the plane size of feature maps stage-by-stage, such that the feature maps generated within each stage show the same plane size. This concept governs the design of most classification networks. However, it can also lead to semantic deficiency of high-resolution feature maps as they are always placed in the shallow layers of a network. Here, we propose a novel network architecture, named ScaleNet, which consists of stacked convolution-deconvolution blocks and a multipath residual structure. Unlike most current networks, ScaleNet extracts image features by a cascaded deconstruction-reconstruction process. It can generate scale-variable feature maps within each block and stage, thereby realizing multiscale feature extraction at any depth of the network. Based on the CIFAR-10, CIFAR-100, and ImageNet datasets, ScaleNet demonstrated competitive classification performance compared to state-of-the-art ResNet. In addition, ScaleNet exhibited a powerful ability to capture strong semantic and fine-grained features on its high-resolution feature maps. The code is available at https://github.com/zhjpqq/scalenet.
Paediatric intussusception in Benin City, Nigeria

Intussusception is one of the leading causes of intestinal obstruction in children. This study reports our experience in the management of intussusception in Benin City,Nigeria. This was a 10-year retrospective review of intussusception in children at the University of Benin Teaching Hospital from January, 1997 to December, 2006. Twenty four children were admitted with intussusception (M: F = 1:1.4) within the period. Only three patients (12.5%) presented within 24 hours of onset of illness. Ten (41.6%) presented between one and four days; seven (29.2%) between five and10 days, while four (16.6%) presented between 11and14 days. Abdominal pain, irritability, blood in stools, vomiting, abdominal distension and palpablemass in various combinations were the clinical features. All the patients, except one (4%), were below the age of one year. There was much delay before presentation of infantswith intussusception,with high attendantmortality. Keywords : paediatric, intussusception. African Journal of Paediatric Surgery Vol. 4 (1) 2007: pp. 12-15
EVC-Based Power Gating Approach to Achieve Low-Power and High Performance NoC

High power consumption becomes the major bottleneck that prevents applying Network-on-Chips (NoCs) on future many-core systems. Power gating is an effective way to reduce the power consumption of a NoC. However, conventional power gating approaches cause significant packet latency increase as well as additional power consumption overhead due to the power gating mechanism. One comprehensive way to reduce these negative impacts is to bypass powered-off routers in a NoC when transferring packets. Therefore, in this paper, we propose an express virtual channel based (EVC-based) power gating approach. In our approach, packets can take pre-defined virtual bypass paths to bypass intermediate routers that can be powered-on or powered-off. Furthermore, based on our extended router structure, a certain transmission ability of the powered-off routers is kept to transfer packets going through the normal paths. Thus, even though some packets do not take a virtual bypass path, they still have less probability to be blocked by the powered-off routers. Compared with a conventional NoC without power gating, our EVC-based power gating approach causes only 2.67% performance penalty, which is less than 28.67%, 7.24%, and 5.69% penalties in related approaches. With small hardware overhead, our approach reduces on average 68.29% of the total power consumption in a NoC, which is comparable with the 72.94%, 73.56%, and 75.3% reduction of the total power consumption in related approaches.
Formation Control with Connectivity Maintenance for Double-Integrator Multi-Agent Systems

The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-integrator agent group to maintain the connectivity of communication graph until the desired formation is achieved. We also investigate the connectivity preserving formation control with a virtual leader. Providing that at least one agent has information about the leader, all agents can asymptotically reach the desired formation and attain the same velocity as the virtual leader. The asymptotic stability analysis is presented with Lyapunov-like tools including LaSalle’s invariance principle and Barbalat’s lemma. Simulation results on a group of agents with double-integrator dynamics are presented to demonstrate the effectiveness of the proposed strategies.
Modeling of Hysteresis for Piezo-driven Stages using a Rate-dependent Krasnosel’skii-Pokrovskii Model

To describe the rate-dependent hysteresis of piezo-driven stages, a modified rate-dependent Krasnosel’skii-Pokrovskii (RDKP) model is proposed in this paper. In this method, both the differential of the output displacement and rate-dependent parameters are introduced into the classical Krasnosel’skii-Pokrovskii (CKP) model. Then the interior point algorithm and bat search optimization algorithm are used to obtain the density function and rate-dependent parameters of the RDKP model respectively. Moreover, the relationship between the obtained rate-dependent parameters and input voltage frequency is produced to an exponential function. Simulation results show that the RDKP model with the rate-dependent exponential function can describe the rate-dependent hysteresis of piezo-driven stages with a higher modeling precision compared with the CKP model.
MARM-GA: Mapping Applications to Reconfigurable Mesh using Genetic Algorithm

Rapidly decreasing size of the CMOS transistors allowed us to place more components on a single chip than ever before. In order to meet the performance demands of these dense designs, designers introduced an efficient communication paradigm, Network-on-Chip (NoC), instead of traditional wiring-based methods. Although mesh topology is most commonly used topology for NoC design, it has several problems such as network congestion and energy consumption. Reconfigurable mesh topology is a good alternative to traditional mesh since it gives more mapping and routing options for reducing network congestion. However, design automation tools still lack efficient mapping and routing algorithms for reconfigurable meshes. In this study, we propose a genetic algorithm (GA) based method that simultaneously maps the application nodes on 2D reconfigurable mesh structure and determines the routing paths between communicating pairs with the objective of energy minimization. We have applied our method on four benchmarks and compared our results against two heuristic strategies. Simulation results show the superiority of our proposed method over the existing ones in terms of energy consumption.
Prediction of Packet Inter-Reception Time for Platooning using Conditional Exponential Distribution

We present a conditional exponential distribution model for the prediction of the packet inter-reception time. This value is our objective in terms of quality of service prediction for a high-density platooning application. This application indeed requires the exchange of information with low latency and high reliability. The prediction of the packet inter-reception time is therefore of high interest for this scenario.We extend our discrete kernel density estimation model to a continuous model based on the parametrization of an exponential distribution. In this model, the rate of the distribution is a function of our predictors, the inter-antenna distance and the spatial distribution of the surrounding communicating vehicles. We approach this function using a multi-layer perceptron regressor for different granularity values of our spatial model.Based on simulation data obtained with a combination of ns-3 and SUMO, we show that our model performs better than our previous model. This performance, combined with the advantages of being continuous and being able to be trained on-line, is promising for a further deployment and test on other scenarios. We present further results on the influence of the interaction between inter-antenna distance and spatial distribution of the communicating vehicles. Finally, we show a successful example of on-line instantaneous prediction of the packet inter-reception time along with the observed values of the predictors and the experienced packet inter-reception time.
Synthesis of an Optimized Control Signal for an Improved EMC Switching Behavior of MOSFETs Using a System Characterization Approach

Active gate control of MOSFETs is a common strategy to improve efficiency and/or electromagnetic compatibility (EMC) of power electronic systems. Finding an appropriate control method for specific requirements is no trivial task, and often there is a trade-off between efficiency and EMC. In this paper, a novel method is proposed that utilizes synthesized control signals. With signal synthesis methods, that can be realized affordable in future integrated circuits, signal shapes can be adjusted more or less arbitrarily. Various requirements, e.g. switching waveforms, overall system’s EMC or efficiency, can be aimed at this way. Here, a basic MOSFET circuit is investigated which should generate a specific switching waveform. This waveform shall improve the system’s EMC without significantly affecting the efficiency. The system is developed, characterized and a mathematical approximation is derived that respects the important temperature dependency. In this first demonstration, very low switching frequencies are considered that make the reactive elements of the MOSFET negligible. From the requirements and the system model, a suitable control signal can be derived. Measurement and simulation results show the good performance of the proposed method.
Delay Tolerant Networks in Industrial Applications

The control of mobile robots in industrial applications is mostly shared between local control by the robot and remote control by a central control system. The rising autonomy of robots in industrial settings affect the type of messages communicated between the central control system and the mobile robot. Single messages become bigger in size, but more tolerant towards transmission delays. This work examines the possibility of using delay tolerant networking in an industrial application for mobile robots. After defining the emerging requirements, the network is modelled statistically to check the theoretical applicability. An implementation of a delay tolerant network in an industrial application is used to verify the model and evaluate the observed performance. According to the observed performance DTNs are applicable for a wide variety of use cases in industrial applications. The work is concluded by giving a brief overview over future research and the methods that will be prioritized in this future research.