Open-source Design Research Articles

PEak: A Single Source of Truth for Hardware Design and Verification

Domain-specific languages for hardware can significantly enhance designer productivity, but sometimes at the cost of ease of verification. On the other hand, ISA specification languages are too static to be used during early stage design space exploration. We present PEak, an open-source hardware design and specification language, which aims to improve both design productivity and verification capability. PEak does this by providing a single source of truth for functional models, formal specifications, and RTL. PEak has been used in several academic projects, and PEak-generated RTL has been included in three fabricated hardware accelerators. In these projects, the formal capabilities of PEak were crucial for enabling both novel design space exploration techniques and automated compiler synthesis.

Development of a device for assessment of aortic root parameters and positioning of aortic valve cusps during aortic valve-sparing root replacement

Background. A promising method of surgical treatment of aneurysms of the root and ascending aorta (AA) with unchanged aortic valve (AV) cusps is aortic valve-sparing root replacement (VSRR) with AV reimplantation (David procedure). To date, there are no clear indications to make a choice in favor of valve-replacing or valve-sparing intervention. The result of visual evaluation and the choice of treatment method based on the surgeon’s experience remains the main criteria. Objective. In the experiment to develop a prototype and method of application of the device for positioning of AV cusps, which will simplify and standardize aortic valve-sparing root replacement, improve the results and increase the reproducibility of these operations. Design and methods. Three-dimensional (3D) modeling of the device components was performed in the parametric open-source computer-aided design environment FreeCAD 0.20.1. Two-dimensional sketches were converted into three-dimensional models and exported as stl files for 3D printing. Solid components of the model were made of polylactic acid (PLA-plastic), elastic components were made of rubber-like photopolymer (Dropstil F556 10 shore A) also by 3D printing using SLA technology. Results. The device consists of 2 similar ring-sizers of variable diameter connected by three struts of variable length. In the upper part of each of the struts fastenings to the distal ring represents T-shaped cutouts for temporary locking of the suture-holders passed through the commissures of the AV cusps. For proximal locking of the aortic graft and the proximal ring-sizer, 3 U-shaped sutures are used, passed from inside to outside through the AV ring, the proximal part of the graft and taken in the tourniquets. The diameters of the ring-sizers can be varied between 25–40 mm by rotating a worm drive. A graft is placed on the inner side of the device, the suture-holders attached to the tops of the commissures are locked in the T-shaped notches in the upper parts of the struts. The device will allow changing the position of the coaptation point, coaptation square and performing hydraulic tests in different positions of the cusps, as well as variable diameters at the level of the AV ring and sinotubular junction (25–40 mm). When the target position of the coaptation point, coaptation area and satisfactory hydraulic test result are achieved, the cusps with commissures are locked inside the graft and the device is removed. Conclusion. A new device has been developed to facilitate, reproduce, and standardize VSRR. The results obtained will facilitate and accelerate the performance of VSRR, increase the reproducibility of the technique, and reduce the risks of complications.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

On the Design of a Simulation-Assisted Human-Centered Quasi-Stiffness-Based Actuator for Ankle Orthosis

Most exoskeletons designed to assist users in load-bearing tasks face a mechanical dilemma in their conception. Designers may find a compromise between stiff active actuators-based architectures which are powerful but bulky and compliant actuator-based designs which are much less assistive but less constraining for users. This article presents a new open-source simulation-based design tool and a human-centered method that lets orthosis designers explore different device configurations and evaluate some performance criteria. This framework was applied in three different young-adult subjects. The effects of design personalization on user morphology and gait were studied. First, an ankle–foot orthosis designed to support a 20 kg backpack was defined according to the user’s height, weight, and walking speed. Then, a simulation of the subjects fitted with their customized design walking at a self-selected speed on flat ground carrying this additional load was performed. First, the results showed that the designed method inspired by natural joint stiffness behavior provided viable personalized mechanisms. Second, significant reductions in peak joint torque and mean joint activity were observed when comparing muscle-generated torques while the subject was wearing the 20 kg backpack with ankle–foot orthoses on both legs or without. Finally, it shows the value of an open-access tool for exploring the coupling of passive and active actuators to generate lighter and more compliant designs.

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Turn on, Tune in, and Listen up: Maximizing Side-Channel Recovery in Cross-Platform Time-to-Digital Converters

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. Additionally, prior voltage fluctuation sensors make use of proprietary vendor intellectual property and do not provide guidance on sensor migration to other vendors. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5 \(\times\) over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2 \(\times\) . As an extension to our prior work, we show that the voltage fluctuation sensor is portable to multiple FPGA vendors, and we demonstrate implementations on both Xilinx and Intel FPGA systems.

Data-Driven Radial Compressor Design Space Mapping

Abstract Estimates of turbomachinery performance trends inform system-level compromises during preliminary design. Existing empirical correlations for efficiency use limited experimental data, while analytical loss models require calibration to yield predictive results. From a set of 3708 radial compressor computations, this paper maps efficiency as a function of mean-line aerodynamics, and determines the governing loss mechanisms. An open-source turbomachinery design code creates annulus and blade geometry, then runs a Reynolds-averaged Navier–Stokes simulation for compressors sampled from the mean-line design space. Polynomial surface fits yield a continuous eight-dimensional representation of the design space for analysis, predicting efficiency with a root-mean-square error of 1.2% points. The results show a balance between surface dissipation in boundary layers and mixing loss due to casing separations sets optimum values for inlet Mach number, hub-to-tip ratio, de Haller number, and backsweep angle. Surface dissipation drives the effect of flow coefficient, with high surface areas at low values, and high velocities at high values. Compact compressor designs are achieved by increasing inlet Mach number, reducing hub-to-tip ratio, and minimizing the radial loading coefficient—all of which reduce efficiency approaching design space boundaries. An interactive web-based tool makes the results available to practising engineers, demonstrating large ensembles of automated designs and simulations as a higher-fidelity replacement for legacy empirical correlations in preliminary design.

Unified momentum model for rotor aerodynamics across operating regimes

Despite substantial growth in wind energy technology in recent decades, aerodynamic modeling of wind turbines relies on momentum models derived in the late 19th and early 20th centuries, which are well-known to break down under flow regimes in which wind turbines often operate. This gap in theoretical modeling for rotors that are misaligned with the inflow and also for high-thrust rotors has resulted in the development of numerous empirical corrections which are widely applied in textbooks, research articles, and open-source and industry design codes. This work reports a Unified Momentum Model to efficiently predict power production, thrust force, and wake dynamics of rotors under arbitrary inflow angles and thrust coefficients without empirical corrections. The Unified Momentum Model is additionally coupled with a blade element model to enable blade element momentum modeling predictions of wind turbines in high thrust coefficient and yaw misaligned states without using corrections for these states. This Unified Momentum Model can form a new basis for wind turbine modeling, design, and control tools from first principles and may enable further development of innovations necessary for increased wind production and reliability to respond to 21st century climate change challenges.

Implementation of microcontroller board on a sustainable and degradable PLA/flax composite substrate: a case study

In this paper, we present a novel polylactic-acid/flax-composite substrate and the implementation of a demonstrator: a microcontroller board based on commercial design. The substrate is developed for printed circuit board (PCB) applications. The pre-preg is biodegradable, reinforced, and flame-retarded. The novel material was developed to counter the increasing amount of e-waste and to improve the sustainability of the microelectronics sector. The motivation was to present a working circuit in commercial complexity that can be implemented on a rigid substrate made of natural, bio-based materials with a structure very similar to the widely used Flame Retardant Class 4 (FR4) substrate at an early technological readiness level (2–3). The circuit design is based on the Arduino Nano open-source microcontroller board design so that the demonstration could be programmable and easy to fit into education, IoT applications, and embedded designs. During the work, the design was optimized at the level of layout. The copper-clad pre-preg was then prepared and processed with subtractive printed wiring technology and through hole plating. The traditional surface mounting methodology was applied for assembly. The resulting yield of PCB production was around 50%. Signal analysis was successful with analogue data acquisition (voltage) and low-frequency (4 kHz) tests, indistinguishable from sample FR4 boards. Eventually, the samples were subjected to highly accelerated stress test (HAST). HAST tests revealed limitations compared to traditional FR4 printed circuit materials. After six cycles, the weight loss was around 30% in the case of PLA/Flax, and as three-point bending tests showed, the possible ultimate strength (25 MPa at a flexural state) was reduced by 80%. Finally, the sustainability aspect was assessed, where we found that ∼95 vol% and ∼90 wt% of the traditional substrate can be substituted, significantly easing the load of waste on the environment.

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice.

Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability, and long-term stability. This protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable, and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple reuse of probes, significantly reducing experimental costs. The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7 mm; (2) a three-dimensional (3D)-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection, and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 h), probe safety, and animal welfare. The implants had minimal impact on animals' behavioral repertoire, were easily applicable in freely moving and head-fixed contexts, and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of post-implant data collection. Infections and other surgery complications were extremely rare. As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.

Open-source design of low-cost sensorised elastic actuators for collaborative prosthetics and orthotics

Collaborative robots, or cobots, have become popular due to their ability to safely operate alongside humans in shared environments. These robots use compliant actuators as a key design element to prevent damage during unintended collisions. In prosthetic and orthotic applications, compliant actuators are crucial for ensuring user safety and comfort. However, most compliant cobots for these applications are excessively expensive and complex to construct. Our study introduces an innovative, cost-effective, and sensorised elastic actuator design tailored for prosthetics and orthotics. The design uses a modular approach and leverages 3D printing technology for rapid customisation, enabling efficient and affordable fabrication. Both hardware and software components are open-source, facilitating unrestricted access for students, researchers, and practitioners. Our design supports impedance and admittance control techniques, enhancing the system’s capabilities. Validation results show a standard deviation of 9.67 Nm between calculated and measured torque in impedance control and 0.2563 radians between calculated and measured angles in admittance control. This allows for improved adaptability to varying operational requirements in prosthetics and orthotics. By introducing this educational framework encompassing a low-cost, sensorised elastic actuator design, we aim to address the need for accessible solutions in the field of collaborative robotics for prosthetics and orthotics.

The role of 3- D printing technologies

Digital fabrication technology, often known as 3D printing or additive manufacturing, constructs tangible items by incrementally adding materials based on a geometric model. 3D printing technology is a rapidly advancing technology. Currently, 3D printing finds extensive use worldwide. The agricultural, healthcare, automobile, locomotive, and aviation sectors are increasingly utilising 3D printing technology for mass customisation and manufacturing of various open-source designs. 3D printing technology enables the sequential addition of material layers to create an item directly from a computer-assisted design (CAD) model. This article provides an overview of several kinds of 3D printing methods, their applications, and the materials used in the manufacturing business.Additive manufacturing, 3D Printing, Manufacturing industry

Open-Source Design of Infiltration Trenches for Sustainable Soil and Water Conservation in Rural Areas of Central Chile

Specific algorithms are developed to solve the equations that define the physical dimensions under various conditions. In this sense, the storm index method was incorporated for the variable precipitation intensity, expanding the number of rainfall stations with the intensity duration frequency (IDF) curves from 9 to 31 within the considered territory (the Biobio Region of Central Chile). Likewise, the infiltration values and runoff coefficients necessary for calculating the dimensions of the trenches were obtained using the Python programming language. The results show that an open-source Python solution allows high reliability and efficiency based on the tests developed. For this reason, this prototype is expected to add new mathematical expressions that may arise to better account for an efficient design of soil and water conservation works or infiltration trenches. In this way, it is concluded that it is possible to develop simulation models for the efficient design of trenches based on well-defined and limited theoretical modeling, adding to computer language tools. This allows for a virtuous synergy that can help address efficient public policies to conserve soil and water in Chile and elsewhere.

Cost-effective, open-source light shutters with Arduino control

In optical experiments, shutters are devices that open or close a path of light. They are often used to limit the duration of light exposure onto a target or onto a detector to reduce possible light-induced damage. Many commercial shutters are available for different applications – some provide very fast opening and closing times, some can handle large optical powers, and others allow for fail-safe operation. Many of these devices are costly and offer limited control options. Here we provide an open-source design for a low-cost, general purpose shutter system based on ubiquitous actuators (servo motors or solenoids) that are connected to an Arduino-based controller. Several shutters can be controlled by one controller, further reducing system cost. The state of the shutters can be controlled via a display built into the controller, by serial commands via USB, or by electrical control lines. The use of a microcontroller makes the shutter controller adaptable – only control options that are used need to be included, and the design accommodates a selection of display and actuator options. We provide designs for all required components, including 3D print files for the actuator holders and cases, the Arduino code, libraries for serial communication (C and python), and example graphical user interfaces for testing.

OpenSTED: open-source dynamic intensity minimum system for stimulated emission depletion microscopy.

Stimulated emission depletion (STED) is a powerful super-resolution microscopy technique that can be used for imaging live cells. However, the high STED laser powers can cause significant photobleaching and sample damage in sensitive biological samples. The dynamic intensity minimum (DyMIN) technique turns on the STED laser only in regions of the sample where there is fluorescence signal, thus saving significant sample photobleaching. The reduction in photobleaching allows higher resolution images to be obtained and longer time-lapse imaging of live samples. A stand-alone module to perform DyMIN is not available commercially. In this work, we developed an open-source design to implement three-step DyMIN on a STED microscope and demonstrated reduced photobleaching for timelapse imaging of beads, cells, and tissue. The DyMIN system uses a fast multiplexer circuit and inexpensive field-programmable gate array controlled by Labview software that operates as a stand-alone module for a STED microscope. All software and circuit diagrams are freely available. We compared time-lapse images of bead samples using our custom DyMIN system to conventional STED and recorded a higher signal when using DyMIN after a 50-image sequence. We further demonstrated the DyMIN system for time-lapse STED imaging of live cells and brain tissue slices. Our open-source DyMIN system is an inexpensive add-on to a conventional STED microscope that can reduce photobleaching. The system can significantly improve signal to noise for dynamic time-lapse STED imaging of live samples.

Hardware Accelerated AES: RTL to GDS

This paper presents a hardware-accelerated Advanced Encryption Standard (AES) implementation featuring an 11-stage pipelined architecture and an optimized subkey generation scheme for enhanced performance. This design is implemented in Verilog and verified through waveform analysis. Operating at a maximum clock frequency of 100 MHz, our implementation achieves significant efficiency. The final design occupies an area of 1.44 mm2 and consists of 32,424 standard cells, with a power consumption of 32 mW at the typical corner. Furthermore, the entire design flow, from synthesis to layout (GDSII), is achieved using OpenLane, an open-source Electronic Design Automation (EDA) tool. This democratization of hardware design empowers a wider range of participants, particularly students and researchers, to efficiently implement their designs and gain hands-on experience without the constraints of expensive commercial tools. This fosters innovation and accelerates progress by allowing a broader community to contribute to technological advancements. Index Terms—OpenLane, Open-source , EDA, Pipelined AES, RTL to GDSII

Feature selection using metaheuristics made easy: Open source MAFESE library in Python

Artificial intelligence (AI) often relies on feature selection (FS) to recognize and highlight the most relevant and major features in a dataset. The procedure of training and optimizing AI systems with key data points is decisive for its development and efficacy. To address this challenge, the present study introduces MAFESE, an open-source Python library that employs metaheuristic algorithms for selecting the optimal set of attributes, particularly when dealing with complex and high-dimensional data. MAFESE encompasses a wide range of feature selection techniques, including unsupervised-based, filter-based, embedded-based, and wrapper-based methods. Notably, within the wrapper-based category, MAFESE offers users access to over 200 metaheuristic algorithms, empowering them to choose the most suitable algorithm based on their specific datasets and requirements. Additionally, MAFESE incorporates built-in evaluation metrics that enable efficient comparison among various algorithms. Open-source design of MAFESE encourages cooperation within the data science community, allowing for continuous upgrades. This collaborative environment promotes the sharing of ideas, proposals, and changes, resulting in a stronger and more adaptive feature selection framework. MAFESE distinguishes itself with an easy-to-use Python interface that follows object-oriented programming concepts. It supports both experienced researchers and practitioners. MAFESE offers many resources, including documentation, examples, and test cases, for a smooth user onboarding experience. The modular architecture enables users to enhance features and interface with other tools, such as scikit-learn. MAFESE can be a valuable tool for identifying meaningful features in complicated, high-dimensional datasets, making it a significant addition to feature selection. MAFESE utilizes metaheuristic algorithms to help users tackle complex feature selection problems more efficiently and accurately. Through openness and teamwork, MAFESE may become a comprehensive resource that responds to the evolving demands of the data science community. The source code and materials of MAFESE are publicly available in the official GitHub repository at https://github.com/thieu1995/mafese.

An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model.

Multichannel, infinite-conjugate optical systems easily allow implementation of multiple image paths and imaging modes into a single microscope. Traditional optical alignment methods which rely on additional hardware are not always simple to implement, particularly in compact open-source microscope designs. We present here an alignment algorithm and process to position the lenses and cameras in a microscope using only image magnification measurements. We show that the resulting positioning accuracy is comparable to the axial resolution of the microscope. Ray transfer matrix analysis is used to model the imaging paths when the optics are both correctly and incorrectly aligned. This is used to derive the corresponding image magnifications. We can then extract information about the lens positions using simple image-based measurements to determine whether there is misalignment of the objective lens to sample distance (working distance) and with what magnitude and direction the objective lens needs to be adjusted. Using the M4All open-source 3D printable microscope system in combination with the OpenFlexure microscope, we validate the alignment method and highlight its usability. We provide the model and an example implementation of the algorithm as an open-source Jupyter Notebook. This article is part of the Theo Murphy meeting issue 'Open, reproducible hardware for microscopy'.

SPONGE: Open-Source Designs of Modular Articulated Soft Robots

SPONGE: Open-Source Designs of Modular Articulated Soft Robots

Review of Advanced Methods in Hardware Acceleration for Deep Neural Networks

Abstract: Convolutional neural networks have become very efficient in performing tasks like Object Detection providing human like accuracy. However, their practical implementation needs significant hardware resources and memory bandwidth. In recent past a lot of research is being carried out for achieving higher efficiency in implementing such neural networks in hardware. We talk about FPGAs for hardware implementation due to their flexibility for customisation for such neural network architectures. In this paper we will discuss the metrics for efficient hardware accelerator and general methods available for achieving an efficient design. Further, we will discuss the actual methods used by recent research for implementation of deep neural networks particularly for object detection related applications. These methods range from actual ASIC design like TPUs [1] for on chip acceleration, state of the art open source designs like Gemini to methods like hardware reuse, re-configurable nodes and approximation in computations as a trade-off between speed and accuracy. This paper will be a valuable summary for the researchers starting in the field of hardware accelerators design for neural networks