Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving a Scalable E/E-Architecture Using AUTOSAR and Virtualization

ABSTRACTToday's automotive software integration is a static process. Hardware and software form a fixed package and thushinder the integration of new electric and electronic features once the specification has been completed. Usually softwarecomponents assigned to an ECU cannot be easily transferred to other devices after they have been deployed. The mainreasons are high system configuration and integration complexity, although shifting functions from one to another ECU isa feature which is generally supported by AUTOSAR. The concept of a Virtual Functional Bus allows a strict separationbetween applications and infrastructure and avoids source code modifications. But still further tooling is needed toreconfigure the AUTOSAR Basic Software (BSW). Other challenges for AUTOSAR are mixed integrity, versioning andmulti-core support.The upcoming BMW E/E-domain oriented architecture will require all these features to be scalable across all vehiclemodel ranges. Entire vehicle E/E clusters, with heterogeneous software environments, must be encapsulated and shiftedfrom one ECU to another while still meeting all postulated requirements. Virtualization technology could be a reasonablecompromise between freedom from interference among integrated systems, scalable system architecture and efficientruntime environment.This work gives a brief vision of domain oriented architecture within the automotive domain by means of AUTOSARtechnology and discusses the requirements for such a scalable system. It is discussed, whether the requirements can be metwith AUTOSAR or if additional abstraction or separation layers are needed. Finally we outline the possible usage of ahypervisor in combination within the AUTOSAR Basic Software using multicore technology and propose a mixed systemarchitecture supporting the domain oriented architecture.CITATION: Reinhardt, D. , Kaule, D. and Kucera , M. , "Achieving a Scalable E/E-Architecture Using AUTOSAR andVirtualization," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 6(2):2013, doi:10.4271/2013-01-1399. ____________________________________
Static Analysis of x86 Executables

This dissertation is concerned with static analysis of binary executables in a theoretically well-founded, sound, yet practical way. The major challenge is the reconstruction of a correct control flow graph in presence of indirect jumps, pointer arithmetic, and untyped variables. While static program analysis for proving safety properties or finding bugs usually targets source code, in many potential analysis scenarios only a binary is available. For instance, intellectual property issues can prevent source code from being accessible to verification specialists, and some analyses, such as malware detection, are by definition required to work with executables. Moreover, binary analysis can be useful even in situations where the source code is available, e.g., when the compiler is not part of the trusted computing base. In most of the existing work, a heuristic disassembler makes a best effort attempt to generate a plain text listing of the assembly instructions in the executable and feeds it to a separate static analysis component. The heuristics render this technique inherently unsound, and the control flow graphs retrieved from such listings are usually fragmented and incomplete. Several approaches have pointed out the possibility of using results of data flow analysis to augment disassembly and control flow reconstruction, but described this connection as suffering from a "chicken and egg" problem, since data flow analysis requires a control flow graph to work on. This dissertation argues for the integration of disassembly, control flow reconstruction, and static analysis in a unified process. It introduces a framework for simultaneous control and data flow analysis on low level binary code, which overcomes the "chicken and egg" problem and is proven to yield the most precise control flow graph with respect to the precision of the data flow domain. A very precise domain that lends itself well to control flow reconstruction is introduced in Bounded Address Tracking, a combined pointer and value analysis that supports pointer arithmetic. It tracks variable valuations up to a tunable bound on the number of values per variable per program location. Its path sensitivity generally allows strong updates to memory, i.e., heap regions are uniquely identified, and equips it with context sensitivity without assuming a correct layout of procedures. These building blocks are combined into an extensible program analysis architecture, which is implemented in a novel binary analysis tool. The tool, named Jakstab, works directly on binaries and disassembles instructions on demand while exploring the program's state space, allowing it to handle low level features such as overlapping instructions, which cause difficulties for regular disassemblers. The architecture is highly configurable to allow a wide range of analyses, from sound abstract interpretation to heuristics-supported disassembly. Its practical feasibility and improvements over existing approaches are shown through case studies on device driver binaries and system executables found on a regular desktop PC.
Dependable computing techniques for reconfigurable hardware

Reconfigurable hardware, such as Field-Programmable Gate Arrays (FPGAs), has become a very important component in various applications, including communication networking, and storage systems. With the re-programmable feature and a tremendous increase in on-chip logic and routing resources, contemporary reconfigurable hardware also opens new opportunities in designing dependable systems with fine-grained hardware redundancy. In traditional dependable systems using hardware redundancy, fault tolerance is realized by replicating functional modules at the level of a chip or a board. Such coarse-grained hardware redundancy is very expensive. In reconfigurable hardware, however, a more cost-effective method is made possible by using an alternative configuration in which the faulty parts are replaced with originally unused resources in the same device. Dependable computing using reconfigurable hardware requires three layers of support. First, Concurrent Error Detection (CED) is required in order to guarantee the correctness of outputs and detect errors. Second, transient error recovery is needed to restore normal operations from failures due to temporary environmental disturbances. Third, permanent fault recovery, including fault location techniques and reconfiguration strategies for tolerating permanent faults, is required to repair a system with physical failures in the hardware. Since these techniques are executed at run-time, it is desired to minimize their performance impact and to ensure high availability. We investigate the issues in each layer of support for dependable computing in reconfigurable hardware. For CED techniques, we present the inverse comparison scheme suitable for applications with unique inverses. For transient error recovery, we examine a memory coherence issue in the recovery process and present a dirty-bit memory coherence technique with low execution time overhead. For permanent fault recovery, we present an integrated scheme using the column-based precompiled configuration technique for repair and the blind reconfiguration approach for fault location. Our techniques ensure very fast and cost-effective fault recovery in reconfigurable, hardware
A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms.
Simultaneous Calibration of Odometry and Head-Eye Parameters for Mobile Robots with a Pan-Tilt Camera

In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to be estimated for more precise operation. However, the odometric and head-eye parameters are typically estimated separately, which is an inconvenience and requires longer calibration time. Even though several researchers have proposed simultaneous calibration methods that obtain both odometric and head-eye parameters simultaneously to reduce the calibration time, they are only applicable to a mobile robot with a fixed camera mounted, not for mobile robots equipped with a pan-tilt motorized camera systems, which is a very common configuration and widely used for wide view. Previous approaches could not provide the z-axis translation parameter between head-eye coordinate systems on mobile robots equipped with a pan-tilt camera. In this paper, we present a full simultaneous mobile robot calibration of head–eye and odometric parameters, which is appropriate for a mobile robot equipped with a camera mounted on the pan-tilt motorized device. After a set of visual features obtained from a chessboard or natural scene and the odometry measurements are synchronized and received, both odometric and head-eye parameters are iteratively adjusted until convergence prior to using a nonlinear optimization method for more accuracy.
Quantifying regional biodiversity in the tropics: A case study of freshwater fish in Trinidad and Tobago

Extinction rates are predicted to accelerate during the Anthropocene. Quantifying and mitigating these extinctions demands robust data on distributions of species and the diversity of taxa in regional biotas. However, many assemblages, particularly those in the tropics, are poorly characterized. Targeted surveys and historical museum collections are increasingly being used to meet the urgent need for accurate information, but the extent to which these contrasting data sources support meaningful inferences about biodiversity change in regional assemblages remains unclear. Here, we seek to elucidate uncertainty surrounding regional biodiversity estimates by evaluating the performance of these alternative methods in estimating the species richness and assemblage composition of the freshwater fish of Trinidad & Tobago. We compared estimates of regional species richness derived from two freshwater fish datasets: a targeted two year survey of Trinidad & Tobago rivers and historical museum collection records submitted to The University of the West Indies Zoology Museum. Richness was estimated using rarefaction and extrapolation, and assemblage composition was benchmarked against a recent literature review. Both datasets provided similar estimates of regional freshwater fish species richness (50 and 46 species, respectively), with a large overlap (85%) in species identities. Regional species richness estimates based on survey and museum data are thus comparable, and consistent in the species they include. Our results suggest that museum collection data are a viable option for setting reliable baselines in many tropical systems, thereby widening options for meaningful monitoring and evaluation of temporal trends.
An Opportunistic mHealth Architecture for Remote Patient Monitoring

The widespread use and recent advancements in mobile technology have spurred improvements in access to healthcare through remote patient monitoring. As a result, chronically-ill patients can be remotely monitored using noninvasive or invasive body sensors connected to mobile phones. This has led to a decrease in mortality rates and improved quality of life for patients who are able to utilize such technology [5]. Ideally, remote patient monitoring (RPM) should benefit patients regardless of geographical and financial barriers. However, limited or nonexistent access to broadband connectivity has restricted the benefits of connected health services for many people in rural areas [4]. Additionally, the cost of deploying broadband or updating traditional technology has proved to be a large financial burden. Furthermore, the prevalence of chronic disease is approximately 20% higher in rural areas than other areas [3]. Consequently, the need for cost-effective patient monitoring technologies that can operate in the midst of intermittent connectivity is essential in rural and developing areas. A promising solution to this lies in the use of delay tolerant networks (DTN) [2]. Hence, this abstract discusses the design and viability of an architecture that leverages the ubiquity of mobile devices, DTNs and intermittent broadband connectivity to facilitate the transmission of medical information through a remote patient monitoring iOS application.
Paddy crop and weed classification using color features for computer vision based precision agriculture

Weed detection in paddy fields using robotic vision is still a challenging task. The main reason for this being lack of dataset. In this re-search work, the creation of paddy crop and weeds image dataset has been described. The images were acquired using a digital camera under natu-ral lighting conditions. For every image in the dataset, its annotated image was created using manual annotation. Annotation was carried out after plant segmentation from the soil background. Dataset consists of 300 images. In addition, classification of paddy crop and two types of weeds namely, sedges and grass-type weeds has been done using only color features. Usually, crop and weed dis-crimination is done using texture, shape and color features. However, using all three features may result in a computationally intensive system. Color features are extracted using a novel approach based on Speeded-up Robust Features (SURF). Random Forest, K-Nearest Neighbors (K-NN) and Least Squares Support Vector Machine (LSSVM) classifiers have been used for the classification of the paddy crop and two types of weeds. An accuracy around 86% was obtained by all the classifiers. This indicates color features can be relied upon in discrimination between paddy crop and sedges and grass-type weeds.
Non-equilibrium Phase Transitions in Interacting Diffusions

The theory of thermodynamic phase transitions has played a central role both in theoretical physics and in dynamical systems for several decades. One of its fundamental results is the classification of various physical models into equivalence classes with respect to the scaling behavior of solutions near the critical manifold. From that point of view, systems characterized by the same set of critical exponents are equivalent, regardless of how different the original physical models might be. For non equilibrium phase transitions, the current theoretical framework is much less developed. In particular, an equivalent classification criterion is not available, thus requiring a specific analysis of each model individually. In this thesis, we propose a potential classification method for time-dependent dynamical systems, namely comparing the possible deformations of the original problem, and identifying dynamical systems which share the same deformation space. The specific model on which this procedure is developed is the Kuramoto model for interacting, disordered oscillators. Studied in the mean-field limit by a variety of methods, its associated synchronization phase transition appears as an appropriate model for cooperative phenomena ranging from coupled Josephson junctions to self-ordering patterns in biological and social systems. We investigate the geometric deformation of the dynamical system into the space of univalent maps of the unit disk, related to the Douady-Earle extension and the Denjoy-Wolff theory, and separately the algebraic deformation into the space of nonlinear sigma models for unitary operators. The results indicate that the Kuramoto model is representative for a large class of non equilibrium synchronization models, with a rich phase-space diagram.
Lightweight Authentication Protocol for M2M Communications of Resource-Constrained Devices in Industrial Internet of Things

The Industrial Internet of Things (IIoT) consists of sensors, networks, and services to connect and control production systems. Its benefits include supply chain monitoring and machine failure detection. However, it has many vulnerabilities, such as industrial espionage and sabotage. Furthermore, many IIoT devices are resource-constrained, which impedes the use of traditional security services for them. Authentication allows devices to be confident of each other’s identity, preventing some security attacks. Many authentication protocols have been proposed for IIoT; however, they have high computing requirements not viable to resource-constrained devices, or they have been found insecure. In this paper, an authentication protocol for resource-constrained IIoT devices is proposed. It is based on the lightweight operations xor, addition, and subtraction, and a hash function. Also, only four messages are exchanged between the principals to authenticate. It has a low execution-time and communication-cost. Its security was successfully assessed with the formal methods Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Burrows–Abadi–Needham (BAN) logic, together with an informal analysis of its resistance to known attacks. Its performance and security were compared with state-of-the-art protocols, resulting in a good performance for resource-constrained IIoT devices, and higher security similar to computational expensive schemes.
COPII Components Sar1b and Sar1c Play Distinct Yet Interchangeable Roles in Pollen Development1[OPEN]

The small GTPase Sar1, a component of the COPII complex that mediates vesicular trafficking from ER to Golgi, participates in sporophytic and gametophytic control of pollen development in Arabidopsis, with different isoforms playing distinct or redundant roles. The development of pollen is a prerequisite for double fertilization in angiosperms. Coat protein complex II (COPII) mediates anterograde transport of vesicles from the endoplasmic reticulum to the Golgi. Components of the COPII complex have been reported to regulate either sporophytic or gametophytic control of pollen development. The Arabidopsis (Arabidopsis thaliana) genome encodes five Sar1 isoforms, the small GTPases essential for COPII formation. By using a dominant negative approach, Sar1 isoforms were proposed to have distinct cargo specificity despite their sequence similarity. Here, we examined the functions of three Sar1 isoforms through analysis of transfer DNA insertion mutants and CRISPR/Cas9-generated mutants. We report that functional loss of Sar1b caused malfunction of tapetum, leading to male sterility. Ectopic expression of Sar1c could compensate for Sar1b loss of function in sporophytic control of pollen development, suggesting that they are interchangeable. Functional distinction between Sar1b and Sar1c may have resulted from their different gene transcription levels based on expression analyses. On the other hand, Sar1b and Sar1c redundantly mediate male gametophytic development such that the sar1b;sar1c microspores aborted at anther developmental stage 10. This study uncovers the role of Sar1 isoforms in both sporophytic and gametophytic control of pollen development. It also suggests that distinct functions of Sar1 isoforms may be caused by their distinct transcription programs.
Reducing Acidity of Tropical Acid Soil to Improve Phosphorus Availability and Zea mays L. Productivity through Efficient Use of Chicken Litter Biochar and Triple Superphosphate

Phosphorus is a macronutrient which plays an important role in plant metabolism, growth, and development. However, in tropical acid soils, P fixation is high because of significant amounts of Al and Fe ions. Al and Fe ions can reduce diffusion of P into plant roots. Low absorption of P at initial growth of most plants causes stunting and slow growth of plant leaves. This process reduces photosynthesis. Chicken litter biochar (CLB) had been used on tropical acid soils to improve total P, available P, organic P, and inorganic fractions of P. Moreover, CLB is able to reduce exchangeable acidity, Fe, and Al ions in mineral acid soils because of the reactive surfaces of this organic amendment. However, there is dearth of information on the effects of the right combination of CLB and triple superphosphate (TSP) on the aforementioned soil chemical properties and crop productivity. To this end, the objectives of this study were to improve P: (i) Availability in a mineral acid soil and (ii) uptake, agronomic efficiency, and dry matter yield of Zea mays L. using the right amounts of TSP and CLB. Combinations of 75%, 50%, and 25% CLB (based on recommended 5 t ha−1) and TSP (based on recommended P fertilization for maize) were evaluated in a pot study. Selected soil chemical properties, maize plants nutrient uptake, growth variables, and dry matter yield were determined using standard measures. Results showed that 25% and 50% biochar of 5 t ha−1 with 75% TSP can increase soil P availability, recovery, agronomic use efficiency, and dry matter yield of maize plants. These optimum rates can also reduce P fixation by Al and Fe ions. Therefore, soil and maize productivity can be improved by using CLB (25% and 50% of 5 t ha−1) and TSP (75% of conventional rate) to increase nutrients availability especially P.