Framework For Rapid Prototyping Research Articles

JustSTART: How to Find an RSA Authentication Bypass on Xilinx UltraScale(+) with Fuzzing

Fuzzing is a well-established technique in the software domain to uncover bugs and vulnerabilities. Yet, applications of fuzzing for security vulnerabilities in hardware systems are scarce, as principal reasons are requirements for design information access, i.e., HDL source code. Moreover, observation of internal hardware state during runtime is typically an ineffective information source, as its documentation is often not publicly available. In addition, such observation during runtime is also inefficient due to bandwidth-limited analysis interfaces, i.e., JTAG, and minimal introspection of hardware-internal modules.In this work, we investigate fuzzing for Xilinx 7-Series and UltraScale(+) FPGA configuration engines, the control plane governing the (secure) bitstream configuration within the FPGA. Our goal is to examine the effectiveness of fuzzing to analyze and document the opaque inner workings of FPGA configuration engines, with a primary emphasis on identifying security vulnerabilities. Using only the publicly available hardware chip and dispersed documentation, we first design and implement ConFuzz, an advanced FPGA configuration engine fuzzing and rapid prototyping framework. Based on our detailed understanding of the bitstream file format, we then systematically define 3 novel key fuzzing strategies for Xilinx FPGA configuration engines. Moreover, our strategies are executed through mutational structure-aware fuzzers and incorporate various novel custom-tailored, FPGA-specific optimizations to reduce search space. Our evaluation reveals previously undocumented behavior within the configuration engine, including critical findings such as system crashes leading to unresponsive states of the whole FPGA. In addition, our investigations not only lead to the rediscovery of the recent starbleed attack but also uncover a novel unpatchable vulnerability, denoted as JustSTART (CVE-2023-20570), capable of circumventing RSA authentication for Xilinx UltraScale(+). Note that we also discuss effective countermeasures by secure FPGA settings to prevent aforementioned attacks.

Planter: Rapid Prototyping of In-Network Machine Learning Inference

In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, and improves applications' performance. Despite its advantages, the bar to in-network machine learning research is high, requiring significant expertise in programmable data planes, in addition to knowledge of machine learning and the application area. Existing solutions are mostly one-time efforts, hard to reproduce, change, or port across platforms. In this paper, we present Planter: a modular and efficient open-source framework for rapid prototyping of in-network machine learning models across a range of platforms and pipeline architectures. By identifying general mapping methodologies for machine learning algorithms, Planter introduces new machine learning mappings and improves existing ones. It provides users with several example use cases and supports different datasets, and was already extended by users to new fields and applications. Our evaluation shows that Planter improves machine learning performance compared with previous model-tailored works, while significantly reducing resource consumption and co-existing with network functionality. Planter-supported algorithms run at line rate on unmodified commodity hardware, providing billions of inference decisions per second.

A novel open-source cloud control platform with application to tracking control under disturbance

This paper presents a novel rapid control prototyping (RCP) framework for cloud control systems (CCSs). This new architecture consists of human–computer interaction (HCI) client, a cloud server, and an edge controller. It is designed based on open-source hardware and software, with the advantages of low cost, high flexibility, and easy replication. It can offer a reference design for better and further research of CCSs. Then, a cloud-based maglev control system (CMCS) is developed, and a case study on tracking control with disturbance is performed. Furthermore, a dual-structural controller based on equivalent-input-disturbance (EID) and networked predictive control (NPC) approach is devised to enhance the disturbance rejection performance for CCSs with communication constraints. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. A discrete-time EID (DEID) estimator is introduced for one subsystem to compensate for the time delay and the exogenous disturbance. For another subsystem, a time-event-driven strategy-based NPC method is devised to increase the dynamical performance of handling packet loss. Finally, experimental results verified the effectiveness and superiority of the proposed framework and the DEID-based dual-structural control algorithm.

Framework for Rapid Prototyping of PHM Analytics for Complex Systems using a Supervised Data-Driven Approach

Prognostic and Health Management (PHM) solutions are becoming increasingly popular in industries that rely on largesystems such as aircraft, spacecraft, and power plants. PHM analytic solutions are designed to monitor the health of eachsubsystem and component and apply predictive analytic to improve system reliability and safety, reduce the cost and decrease time spent on unscheduled maintenance. However, identifying correlations between different components andassociated monitors in these large systems can be challenging. To address this issue and achieve maximum utilization of available monitoring signals, a methodology is required that can identify correlations between degraded or failed components and the features engineered from the monitors and sensors. This paper introduces a framework that enables rapid prototyping of analytics, allowing users to seamlessly move from designing and discovering features to developing models for a specific event or component of interest. The framework has three main components: feature exploration, datapreparation, and model development. Feature exploration focuses on feature engineering using raw monitor data from all available monitors. Data preparation purges the data, and down-selects relevant features based on correlation defined in the feature exploration part. The data preparation step also creates a training dataset. Model development enablesquick testing and comparison of multiple supervised Machine Learning (ML) models. To demonstrate the framework, this paper presents an example of a remaining useful life model for an aircraft component. While the examples and simulations are aircraft-focused, the principles behind the framework can be applied to other large systems.

Frappé: An Ultra Lightweight Mobile UI Framework for Rapid API-based Prototyping and Environmental Deployment

QR codes have been used as an inexpensive means to connect users to digital platforms such as websites and mobile applications. However, despite their ubiquity, QR codes are limited in purpose and can only redirect users to the URL contained within it, thereby making their use heavily network dependent which can be unsuitable for use in ephemeral scenarios and areas with limited connectivity. In this paper, we introduce Frappé, a framework capable of deploying ultra lightweight UIs to mobile devices directly through QR codes, without requiring any network connectivity. This is achieved by decomposing the UI into metadata and storing it inside the QR code, while offloading the UI functionality to API calls. We also introduce enFrappé, a WYSIWYG tool for building Frappé UIs. We demonstrate the lightweight nature of our framework through a technical evaluation, whereas the usability of our UI builder tool is demonstrated through a user study.

Assessment and treatment of pectus deformities: a review of reverse engineering and 3D printing techniques

PurposeAmong thoracic malformations, pectus deformities have the highest incidence and can result in a wide range of severe and mild clinical manifestations. Recently, the treatment of pectus deformities is shifting from traditional approaches toward customized solutions. This occurs by leveraging innovative rapid prototyping tools that allow for the design and fabrication of patient-specific treatments and medical devices. This paper aims to provide a comprehensive view of the growing literature in this area to analyze the progress made in this direction.Design/methodology/approachThe search was performed on major search engines through keywords inherent to reverse engineering (RE) and additive manufacturing (AM) technologies applied to pectus deformities and related treatments, selecting 54 papers. These were analyzed according to the addressed pathology, the hardware and software tools used and/or implemented and their integration within the clinical pathway.FindingsFirst, the analysis led to analyze and divide the papers according to how RE and AM technologies are applied for surgical and non-surgical treatments, pathological assessment and preoperative simulation and planning. Second, all papers were considered within the typical rapid prototyping framework consisting of the three phases of three-dimensional (3D) scanning, 3D modelling and 3D printing.Originality/valueTo the best of the authors’ knowledge, to date, no survey has provided a comprehensive view of innovative and personalized treatment strategies for thoracic malformations; the present work fills this gap, allowing researchers in this field to have access to the most promising findings on the treatment and evaluation of pathology.

An ICT Prototyping Framework for the "Port of the Future".

Seaports are genuine, intermodal hubs connecting seaways to inland transport links, such as roads and railways. Seaports are located at the focal point of institutional, industrial, and control activities in a jungle of interconnected information systems. System integration is setting considerable challenges when a group of independent providers are asked to implement complementary software functionalities. For this reason, seaports are the ideal playground where software is highly composite and tailored to a large variety of final users (from the so-called port communities). Although the target would be that of shaping the Port Authorities to be providers of (digital) innovation services, the state-of-the-art is still that of considering them as final users, or proxies of them. For this reason, we show how a canonical cloud, virtualizing a distributed architecture, can be structured to host different, possibly overlapped, tenants, slicing the information system at the infrastructure, platform, and software layers. Resources at the infrastructure and platform layers are shared so that a variety of independent applications can make use of the local calculus and access the data stored in a Data Lake. Such a cloud is adopted by the Port of Livorno as a rapid prototyping framework for the development and deployment of ICT innovation services. In order to demonstrate the versatility of this framework, three case studies relating to as many prototype ICT services (Navigation Safety, e-Freight, and Logistics) released within three industrial tenants are here presented and discussed.

Strap-On Magnets: A Framework for Rapid Prototyping of Magnets and Beam Lines

We describe a framework to assemble permanent-magnet cubes in 3D-printed frames to construct dipole, quadrupole, and solenoid magnets, whose field, in the absence of iron, can be calculated analytically in three spatial dimensions. Rotating closely spaced dipoles and quadrupoles in opposite directions allows us to adjust the integrated strength of a multipole. The contributions of unwanted harmonics were calculated and found to be moderate. We then combined multiple magnets to construct beam-line modules: a chicane, a triplet cell, and a solenoid focusing system.

A framework for rapid virtual prototyping: a case study with the Gunnerus research vessel

ABSTRACT Virtual prototypes (VPs) are digital models that mock-up existing or conceptual systems' behaviour. In offshore operations, VPs find usages in design, proof of concept for new equipment or method, control system testing, procedure planning, and expert crew training. Moreover, VP can be used in full mission simulators with crews of maritime and offshore engineers, in which case they integrate with control systems such as handles and dynamic positioning systems. Putting the human in the loop sets high requirements for the fidelity of the visual 3D-models and the mathematical models' validity. VPs are thus time-consuming to create and difficult to validate, even based on an existing offshore system. This paper presents an innovative framework for rapid virtual prototyping of ships for hardware and human in the loop simulations and validates the results with data gathered in a sea trial performed on a research vessel, with satisfying results for position keeping. Abbreviations: CLI, Command line interpreter; DP, Dynamic positioning system: ship equipment used to maintain position and heading; DM, Damping matrix; Force FBK, Force Feedback Thruster1; HIL, Hardware in the loop; HITL, Human in the loop; JNI, Java native interface; LC, Loading condition; RAO, Response amplitude operator; RPM, Revolutions per minute; RPM FBK, RPM Feedback Thruster2/3 (Main/Azimuth); SCM, Source code management system; SOG, Speed over ground; VP, Virtual prototyping/prototype; VST, Virtual sea trial; sea trial performed in a simulation

Binary convolutional neural network acceleration framework for rapid system prototyping

Abstract The huge model size and high computational complexity make emerging convolutional neural network (CNN) models unsuitable to deploy on current embedded or edge computing devices. Recently the binary neural network (BNN) is explored to help reduce network model size and avoid complex multiplication. In this paper, a binary network acceleration framework for rapid system prototyping is proposed to promote the deployment of CNNs on embedded devices. Firstly trainable scaling factors are adopted in binary network training to improve network accuracy performance. The hardware/software co-design framework supports various compact network structures such as residual block, 1 × 1 squeeze convolution layer, and depthwise separable convolution. With flexible network binarization and efficient hardware architecture optimization, the acceleration system is able to achieve over 2 TOPS throughput performance comparable to modern desktop GPU with much higher power efficiency.

Rapid prototyping of cognitive agent simulations using C-BML transformations

Simulating battle management is an essential technique used in planning and mission rehearsal as well as training. Simulation development costs tend to be high due to the complexity of cognitive system architectures in such applications. Due to this complexity, it takes significant effort for a simulation developer to comprehend the problem domain enough to capture accurately in a simulation code. Domain-specific languages (DSL) play an important role in narrowing the communication gap between the domain user and the developer and hence facilitate rapid development. In command and control (C2) applications, the coalition battle management language (C-BML) serves as a DSL for exchanging battle information among C2 systems, simulations, and autonomous elements. In this article, we use a rapid prototyping framework for cognitive agents and demonstrate deployment of agent systems by adopting the model driven engineering approach. To this end, we extend the use of C-BML and automatically transform it in a cognitive agent model, which is then used for adaptive decision making at runtime. As a result, during a simulation run, it is possible to initialize and modify an agent’s goal reasoning model. The cognitive agent models are based on the deliberative coherence theory, which provides a goal reasoning system in terms of coherence-driven agents.

Synthetic Gene Circuits Enable Systems-Level Biosensor Trigger Discovery at the Host-Microbe Interface.

Engineering synthetic circuits into intestinal bacteria to sense, record, and respond to in vivo signals is a promising new approach for the diagnosis, treatment, and prevention of disease. However, because the design of disease-responsive circuits is limited by a relatively small pool of known biosensors, there is a need for expanding the capacity of engineered bacteria to sense and respond to the host environment. Here, we apply a robust genetic memory circuit in Escherichia coli to identify new bacterial biosensor triggers responding in the healthy and diseased mammalian gut, which may be used to construct diagnostic or therapeutic circuits. We developed a pipeline for rapid systems-level library construction and screening, using next-generation sequencing and computational analysis, which demonstrates remarkably reliable identification of responsive biosensor triggers from pooled libraries. By testing libraries of potential triggers-each consisting of a promoter and ribosome binding site (RBS)-and using RBS variation to augment the range of trigger sensitivity, we identify and validate triggers that selectively activate our synthetic memory circuit during transit through the gut. We further identify biosensor triggers with increased response in the inflamed gut through comparative screening of one of our libraries in healthy mice and those with intestinal inflammation. Our results demonstrate the power of systems-level screening for the identification of novel biosensor triggers in the gut and provide a platform for disease-specific screening that is capable of contributing to both the understanding and clinical management of intestinal illness.IMPORTANCE The gut is a largely obscure and inaccessible environment. The use of live, engineered probiotics to detect and respond to disease signals in vivo represents a new frontier in the management of gut diseases. Engineered probiotics have also shown promise as a novel mechanism for drug delivery. However, the design and construction of effective strains that respond to the in vivo environment is hindered by our limited understanding of bacterial behavior in the gut. Our work expands the pool of environmentally responsive synthetic circuits for the healthy and diseased gut, providing insight into host-microbe interactions and enabling future development of increasingly complex biosensors. This method also provides a framework for rapid prototyping of engineered systems and for application across bacterial strains and disease models, representing a practical step toward the construction of clinically useful synthetic tools.

SnakeSIM: a ROS-based control and simulation framework for perception-driven obstacle-aided locomotion of snake robots

Biological snakes are capable of exploiting roughness in the terrain for locomotion. This feature allows them to adapt to different types of environments. Snake robots that can mimic this behaviour could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. Snake robot locomotion in a cluttered environment where the snake robot utilises a sensory–perceptual system to perceive the surrounding operational environment for means of propulsion can be defined as perception-driven obstacle-aided locomotion (POAL). The initial testing of new control methods for POAL in a physical environment using a real snake robot imposes challenging requirements on both the robot and the test environment in terms of robustness and predictability. This paper introduces SnakeSIM, a virtual rapid-prototyping framework that allows researchers for the design and simulation of POAL more safely, rapidly and efficiently. SnakeSIM is based on the robot operating system (ROS) and it allows for simulating the snake robot model in a virtual environment cluttered with obstacles. The simulated robot can be equipped with different sensors. Tactile perception can be achieved using contact sensors to retrieve forces, torques, contact positions and contact normals. A depth camera can be attached to the snake robot head for visual perception purposes. Furthermore, SnakeSIM allows for exploiting the large variety of robotics sensors that are supported by ROS. The framework can be transparently integrated with a real robot. To demonstrate the potential of SnakeSIM, a possible control approach for POAL is considered as a case study.

Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data.

Objective We have created an open-source application and framework for rapid GPU-accelerated prototyping, targeting image analysis, including volumetric images such as CT or MRI data. Methods A visual graph editor enables the design of processing pipelines without programming. Run-time compiled compute shaders enable prototyping of complex operations in a matter of minutes. Results GPU-acceleration increases processing the speed by at least an order of magnitude when compared to traditional multithreaded CPU-based implementations, while offering the flexibility of scripted implementations. Conclusion Our framework enables real-time, intuition-guided accelerated algorithm and method development, supported by built-in scriptable visualization. Significance This is, to our knowledge, the first tool for medical data analysis that provides both high performance and rapid prototyping. As such, it has the potential to act as a force multiplier for further research, enabling handling of high-resolution datasets while providing quasi-instant feedback and visualization of results.

TOPPE: A framework for rapid prototyping of MR pulse sequences.

To introduce a framework for rapid prototyping of MR pulse sequences. We propose a simple file format, called "TOPPE", for specifying all details of an MR imaging experiment, such as gradient and radiofrequency waveforms and the complete scan loop. In addition, we provide a TOPPE file "interpreter" for GE scanners, which is a binary executable that loads TOPPE files and executes the sequence on the scanner. We also provide MATLAB scripts for reading and writing TOPPE files and previewing the sequence prior to hardware execution. With this setup, the task of the pulse sequence programmer is reduced to creating TOPPE files, eliminating the need for hardware-specific programming. No sequence-specific compilation is necessary; the interpreter only needs to be compiled once (for every scanner software upgrade). We demonstrate TOPPE in three different applications: k-space mapping, non-Cartesian PRESTO whole-brain dynamic imaging, and myelin mapping in the brain using inhomogeneous magnetization transfer. We successfully implemented and executed the three example sequences. By simply changing the various TOPPE sequence files, a single binary executable (interpreter) was used to execute several different sequences. The TOPPE file format is a complete specification of an MR imaging experiment, based on arbitrary sequences of a (typically small) number of unique modules. Along with the GE interpreter, TOPPE comprises a modular and flexible platform for rapid prototyping of new pulse sequences. Magn Reson Med 79:3128-3134, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The development and feasibility of a personal health-optimization system for people with bipolar disorder

BackgroundPeople with bipolar disorder often experience ill health and have considerably reduced life expectancies. Suboptimal treatment is common and includes a lack of effective medicines, overtreatment, and non-adherence to medical interventions and lifestyle measures. E- and m-health applications support patients in optimizing their treatment but often exhibit conceptual and technical shortcomings. The objective of this work was to develop and test the usability of a system targeting suboptimal treatment and compare the service to other genres and strategies.MethodsBased on the frameworks of shared decision-making, multi-criteria decision analysis, and single-subject research design, we interviewed potential users, reviewed research and current approaches, and created a first version using a rapid prototyping framework. We then iteratively improved and expanded the service based on formative usability testing with patients, healthcare providers, and laypeople from Norway, the UK, and Ukraine. The evidence-based health-optimization system was developed using systematic methods. The System Usability Scale and a questionnaire were administered in formative and summative tests. A comparison of the system to current standards for clinical practice guidelines and patient decision aids was performed.ResultsSeventy-eight potential users identified 82 issues. Driven by user feedback, the limited first version was developed into a more comprehensive system. The current version encompasses 21 integrated core features, supporting 6 health-optimization strategies. One crucial feature enables patients and clinicians to explore the likely value of treatments based on mathematical integration of self-reported and research data and the patient’s preferences. The mean ± SD (median) system usability score of the patient-oriented subsystem was 71 ± 18 (73). The mean ± SD (median) system usability score in the summative usability testing was 78 ± 18 (75), well above the norm score of 68. Feedback from the questionnaire was generally positive. Eighteen out of 23 components in the system are not required in international standards for patient decision aids and clinical practice guidelines.ConclusionWe have developed the first evidence-based health-optimization system enabling patients, clinicians, and caregivers to collaborate in optimizing the patient’s health on a shared platform. User tests indicate that the feasibility of the system is acceptable.

On supporting rapid prototyping of embedded systems with reconfigurable architectures

Reducing time-to-market while improving product quality is a big challenge. This paper proposes a software-supported framework for rapid prototyping that offers a concurrent fast hardware/software system-level design. The introduced framework enables the constant evaluation and verification of the prototype under development, while it provides automatic functionality mapping to hardware via High-Level Synthesis techniques. We evaluate our framework and its software instantiation with a computer vision algorithm. Based on our experimentation, we show that our approach reduces the development time by almost 64×, it prunes the hardware design space by 34×, while maintaining designs that trade-off high Quality-of-Report on the Pareto frontier.

RunStream

We present RunStream, a rapid prototyping framework for realizing stream cipher implementations based on algorithmic specifications and architectural customizations desired by the users. In the dynamic world of cryptography where newer recommendations are frequently proposed, the need of such tools is imperative. It carries out design validation and generates an optimized software implementation and a synthesizable Register Transfer Level Verilog description. Our framework enables speedy benchmarking against critical resources like area, throughput, power, and latency and allows exploration of alternatives. Using RunStream, we successfully implemented various stream ciphers and benchmarked the quality of results to be at par with published hand-optimized implementations.

A cell-free framework for rapid biosynthetic pathway prototyping and enzyme discovery

Speeding up design-build-test (DBT) cycles is a fundamental challenge facing biochemical engineering. To address this challenge, we report a new cell-free protein synthesis driven metabolic engineering (CFPS-ME) framework for rapid biosynthetic pathway prototyping. In our framework, cell-free cocktails for synthesizing target small molecules are assembled in a mix-and-match fashion from crude cell lysates either containing selectively enriched pathway enzymes from heterologous overexpression or directly producing pathway enzymes in lysates by CFPS. As a model, we apply our approach to n-butanol biosynthesis showing that Escherichia coli lysates support a highly active 17-step CoA-dependent n-butanol pathway in vitro. The elevated degree of flexibility in the cell-free environment allows us to manipulate physiochemical conditions, access enzymatic nodes, discover new enzymes, and prototype enzyme sets with linear DNA templates to study pathway performance. We anticipate that CFPS-ME will facilitate efforts to define, manipulate, and understand metabolic pathways for accelerated DBT cycles without the need to reengineer organisms.

RunFein: a rapid prototyping framework for Feistel and SPN-based block ciphers

Block ciphers are the most prominent symmetric-key cryptography kernels, serving as fundamental building blocks to many other cryptographic functions. This work presents RunFein, a tool for rapid prototyping of a major class of block ciphers, namely product ciphers (including Feistel network and Substitution permutation network-based block ciphers). RunFein accepts the algorithmic configuration of an existing/new block cipher from the user through a GUI to generate a customized software implementation. The user may choose from various micro-architectural templates (unrolled, pipelined, sub-pipelined) to generate an HDL description of the cipher. Various modes of operation and the NIST test suite may also be included. This high-level design approach eliminates the laborious and repetitive development efforts for VLSI realizations of block ciphers. It allows a quick design exploration, consequently enabling fast benchmarking in terms of critical resource estimation of various versions/configurations of a cipher that varies in terms of security, complexity and performance. Using RunFein, we have successfully implemented some well-known product ciphers and benchmarked their performance without significant degradation against their published hand-crafted implementations in literature.