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Disasters are becoming the norm in this world, which also poses challenges to architects. Many needs to build temporary buildings may occur at the same time randomly around all over the world. Constructing an efficient and economical solution is the motivation of this research. Through a comprehensive review of all past related researches and architectural practices, “convenience” and “recyclability” have been identified as two important features of temporary buildings in the post-pandemic era. In this article, we carry on a novel research of discrete architecture. Based on the design paradigm of discrete architecture, this research uses the three main components of S/U/P to develop a Lego Architecture design method by combining with the Grasshopper plug-in under the Rhino platform to write a program that automatically generates the target plan. Then, a typical design scheme for physical construction verification and structural optimization is selected to ensure landing and safety. Originating from the thinking of productization, this research attempts to package the design methods and related knowledge into a systematic solution, relying on an open construction system framework to achieve a rapid, simple and safe construction of temporary buildings after disasters.

The inherent compliance of soft materials imbues robots, generally referred to as soft robots, with particular advantages in producing adaptive and safe interactions. However, the mainstream design paradigms of soft robots have been focused on pursuing large free motions only, usually at the expense of greatly decreased stiffness, leading to limited capability of withstanding external loads in interactive scenarios. There is a pressing need to incorporate the interaction specifications at the design stage to embody soft robots with not only proper deformability but equally importantly, considerable stiffness to perform complex tasks in practical applications. Here, inspired by the dexterity of human hands, we propose a computational design framework for soft grippers with a focus on improving their interaction performance in power grasping or precision grasping mode. The design paradigm rests on attaching a relatively stiffer skeleton layer to the parametric pneumatic networks based actuator which is widely used due to the geometric advantage, and the skeleton layout is designed for customized interaction conditions by a level set based topology optimization approach. As expected, the optimized skeleton layouts exhibit specified structural features highly relevant to the predefined concentrated loads for precision grip or distributed loads for power grip, which physically implies the compromise between deformability and stiffness. Since the interaction forces are difficult to measure in situ, we devise power and precision grasping scenarios and evaluate the critical actuation pressure of the object’s falling instead. The experiments qualitatively demonstrate the superiority of each specified design. This work represents an initial step toward the rational design for interaction in soft robots.

My goal in this paper is to present what is referred to as the life design paradigm as a new, viable approach to career design which takes into account the new contexts cursorily outlined above. Drawing on the notion of chaos as a tool for describing today’s world, I seek to show how the concept can help us conceptualise the changing systems of work organisation, people’s biographies and applicatory potentials of career counselling theories. By doing this, I to make the case for the pertinence and utility of the intervention model proposed by Mark L. Savickas and his collaborators.

Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance far from the limit. The recent breakthrough of deep learning and its positive applications to wireless communications have paved the way to tackle these challenges. This article studies the effectiveness and efficiency of deep learning in enhancing NOMA performance. Specifically, we first present the deep neural network (DNN), which is constructed via a uniform signal processing architecture, and use it as the unified multiuser receiver in both data and model-driven approaches. This enables the end-to-end optimization of NOMA transceivers due to the universal function approximation property of DNN. On the other hand, with DNN we can automatically extract the user access behaviors out of the time-series signals and optimize the transceivers to match these cross-lay-er behaviors. We further analyze the integration of non-orthogonal communication and neural computation to accomplish high-efficiency data transmission at low cost. Finally, we identify some essential future directions of deep-learning-en-hanced NOMA from the perspectives of online reconfigurability and adaptability toward the ever changing environment in future mMTC.

With the rise of Approximate Computing as an energy-efficient design paradigm that is amenable to error-tolerant applications, different Approximate Logic Synthesis (ALS) techniques have been reported in the literature to generate approximate circuits from accurate implementations automatically. One ALS technique focuses on performing structural netlist transformations. The gate-level netlist representation of a circuit is simplified to reduce the circuit area and power consumption while producing a defined error level at the output. However, currently, there is no framework or tool available to test and explore existing netlist transformation techniques for ALS. This limits the proposal of novel approaches and their corresponding comparison against state-of-the-art contributions. In this brief, we present AxLS, an open-source framework for ALS techniques based on netlist transformations. We validate our framework’s functionality by implementing a reported ALS technique and one proposed, using open-source circuit synthesis and simulation tools, and generating approximate arithmetic circuits from accurate RTL descriptions.

In this work, we investigate a wireless power transfer technology that can unobtrusively charge wearable devices while users interact with everyday objects, such as an office desk, laptop, or car. We design and develop our solution, ShaZam, that exploits the human body as a medium to transfer Radio Frequency (RF) energy-carrier signals from minimally-instrumented daily objects to wearable devices. We focus on establishing the technical groundwork of the proposed technology by incorporating the capacitive coupling mechanism, in which the forward signal path is established through the human body, and the return path is established via capacitive coupling to the surrounding environment. To showcase the feasibility of our technology, we investigate three different use scenarios---i.e., interacting with a keyboard on a desk, a laptop, and the steering wheel of a car---to transfer power to a wrist-worn device. Using data obtained from ten healthy individuals within a setting where uncontrolled electromagnetic interference was relatively low, we demonstrate that we can transfer approximately 0.5 mW - 1 mW of DC power to the wrist-worn device. We also investigate several critical environmental and design parameters that could affect the power transfer and offer design guidelines that optimize performance. Our initial results suggest the potential for a new design paradigm towards completely charge-free wearable devices.

Critical cascades are found in many self-organizing systems. Here, we examine critical cascades as a design paradigm for logic and learning under the linear threshold model (LTM), and simple biologically inspired variants of it as sources of computational power, learning efficiency, and robustness. First, we show that the LTM can compute logic, and with a small modification, universal Boolean logic, examining its stability and cascade frequency. We then frame it formally as a binary classifier and remark on implications for accuracy. Second, we examine the LTM as a statistical learning model, studying benefits of spatial constraints and criticality to efficiency. We also discuss implications for robustness in information encoding. Our experiments show that spatial constraints can greatly increase efficiency. Theoretical investigation and initial experimental results also indicate that criticality can result in a sudden increase in accuracy.

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data rates by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

RESUMEN: La habitación colectiva de la modernidad generó un paradigma de diseño urbano-arquitectónico, que incorporaba espacios cuyo diseño fomentaba, de acuerdo con los arquitectos de la modernidad, salud e higiene mediante la circulación de aire puro, iluminación y ventilación naturales al interior de las viviendas, al igual que en los espacios compartidos y de circulación característicos de esa tipología habitacional. Esos elementos de diseño parecen ser útiles en la reducción de contagios del virus SarsCov2 y que actualmente afecta al mundo entero. Para verificar este supuesto, se realizó un trabajo de campo y en línea con habitantes del Centro Urbano Presidente Alemán (CUPA), conjunto representativo de la modernidad arquitectónica en la Ciudad de México. Mediante reconstrucciones volumétricas y cuestionarios en línea se analizaron los elementos de diseño que materializan los ideales modernos orientados a garantizar espacios exteriores e interiores sanos; se evaluó la utilidad de los equipamientos colectivos, espacios de circulación y el diseño de las cuatro tipologías de vivienda que tiene el conjunto. Los resultados del estudio y la ausencia de casos de COVID19 en el CUPA, ayudan a demostrar la validez que recobra la arquitectura moderna en la pandemia mundial, así como la importancia de las lecciones del pasado para integrar nuevos paradigmas de diseño para una arquitectura post-Covid.

Metamaterials are designed to realize exotic physical properties through the geometric arrangement of their underlying structural layout(1,2). Traditional mechanical metamaterials achieve functionalities such as a target Poisson's ratio(3) or shape transformation(4-6) through unit-cell optimization(7-9), often with spatial heterogeneity(10-12). These functionalities are programmed into the layout of the metamaterial in a way that cannot be altered. Although recent efforts have produced means of tuning such properties post-fabrication(13-19), they have not demonstrated mechanical reprogrammability analogous to that of digital devices, such as hard disk drives, in which each unit can be written to or read from in real time as required. Here we overcome this challenge by using a design framework for a tileable mechanical metamaterial with stable memory at the unit-cell level. Our design comprises an array of physical binary elements (m-bits), analogous to digital bits, with clearly delineated writing and reading phases. Each m-bit can be independently and reversibly switched between two stable states (acting as memory) using magnetic actuation to move between the equilibria of a bistable shell(20-25). Under deformation, each state is associated with a distinctly different mechanical response that is fully elastic and can be reversibly cycled until the system is reprogrammed. Encoding a set of binary instructions onto the tiled array yields markedly different mechanical properties; specifically, the stiffness and strength can be made to range over an order of magnitude. We expect that the stable memory and on-demand reprogrammability of mechanical properties in this design paradigm will facilitate the development of advanced forms of mechanical metamaterials.

Music Recommender Systems (Music RS) are nowadays pivotal in shaping the listening experience of people all around the world. Partly driven by the commercial application of this technology, music recommendation research has gained increasing attention both within and outside the Music Information Retrieval (MIR) community. Thanks also to the widespread use of recommender systems in music streaming services, it has been possible to enhance several characteristics of such systems in terms of performance, design, and user experience. Nonetheless, imagining Music RS only from an application-driven perspective may generate an incomplete view of how this technology is affecting people’s habitus, from the decision-making processes to the formation of musical taste and opinions. In this overview, we address the concept of diversity in music recommendation, and taking a value-driven approach we review diversity-related methodologies proposed in the Music RS literature. Additionally, by taking as an example the wider context of Information Technology (IT), we present the elements interacting in the diversity by design paradigm. We do that to acknowledge the lack of a comprehensive framework in Music RS research to address diversity, until now mostly driven by empirical results and fragmented in different application areas. Maintaining an interdisciplinary perspective, we discuss some challenges that MIR practitioners may face when researching Music RS, going beyond the search for better performance and instead questioning the theoretical foundations on which to base future research. This work is partially supported by the European Commission under the TROMPA project (H2020 – grant agreement No. 770376). This work is also partially supported by the HUMAINT programme (Human Behaviour and Machine Intelligence), Joint Research Centre, European Commission. The project leading to these results received funding from “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/PR16/51110009.

Over the last decades, the intense need for more robust and lightweight structures, together with the dramatic improvement of computational power, had, as a result, the introduction of simulations in the traditional product development. As a simulation, it is considered any computer process that imitates a real system by generating similar responses over time. Simulations allow the designers to create virtual prototypes that can speed up the design phase and, thus, the product development time in total. This design paradigm shift is called simulation-based design (SBD) and includes several simulations and optimization techniques. The most notable of these techniques are; computer-aided design (CAD), finite element analysis (FEA), topology optimization (TO), and parametric optimization (PO). A combined SBD methodology, including these techniques, is presented here. This methodology is a two-stage optimization process. During the first stage, traditional compliance TO using the SIMP approach was conducted, while at the second, a PO with an evolutionary algorithm was applied. The presented methodology is focused on the optimization of composite laminates. In particular, an angle-ply laminated beam made by carbon fiber reinforced polymer (FRP) was used as a case study and optimized both for its topology and fibers’ direction. The results of this research are presented and tested using a commercial example. The suggested methodology resulted in a lighter and more robust design solution. These design solutions can be constructed either by conventional manufacturing processes (CMP) or by additive manufacturing (AM). Designers looking for interesting and lightweight composited structures can exploit the results found in this paper. The implemented process can easily be modified in order to cover any possible optimization of FRP products.

Where children are engaged in design, whether in the classroom or participatory design, the process often involves adults to a greater or lesser extent. Adult involvement has been shown to have a negative influence on children’s creative ability in design. This study introduces an independent child-led approach to design by allowing children to print embroidered designs onto textiles using carefully selected technology and materials that do require adult involvement and are suitable for use by children. The approach allows children the opportunity to realise their imaginations without adult impediment and create within a new design paradigm. Using focus groups, the study reveals that children welcome the opportunity to design independently from adults and that such independence leads to motivation and a sense of increased creativity

Traditional computer network design decisions are changing. There are several reasons: the technologies themselves that are used in networks have changed, the general concept for designing network is considered for a network infrastructure, in which services are considered an integral part of the network; the general approach to describing the network has changed from component to object and service, new solutions have appeared for transferring services to virtual and cloud spaces. The trends of ongoing changes are considered and real solutions are described when designing a change in the infrastructure of a computer network. Trends are considered on the example of networks with different scales. As a result, the changed network design paradigm is reduced to four areas: network design for on-premises network, network design for virtualization approaches, large network design for cloud services, and LAN design for personal space.

This paper presents a new Property-Driven Design (PDD) method that starts from an abstract system model and integrates formal property checking early into a top-down design methodology for register transfer level (RTL) hardware. In PDD the role of formal verification is not limited to “bug hunting” alone. Instead, the formal techniques are applied in such a way that a formal relationship is provided between the abstract system model and its concrete implementation at the RTL. In order to avoid the high efforts associated with verification by property checking the proposed PDD approach automatically generates abstract properties from a system-level description and later refines them along the design process. The advantage of this methodology is to obtain a formally verified design at lower costs when compared to conventional design flows with property checking. The main benefit of the proposed approach results from the fact that a formally sound system model is available together with the RTL design. Specifically, LTL properties proven on the abstract model also hold on the concrete implementation. This facilitates many complex analysis and verification tasks in today’s design flows and contributes to emancipating system-level models from prototypes to golden design models. Several open-source and industrial case studies are reported that demonstrate the high potential of a PDD-based design paradigm.

Reconfigurable intelligent surface (RIS) has been proposed as a potential solution to improve the coverage and spectrum efficiency for future wireless communication. However, the privacy of users' data is often ignored in previous works, such as the user's location information during channel estimation. In this paper, we propose a privacy-preserving design paradigm combining federated learning (FL) with RIS in the mmWave communication system. Based on FL, the local models are trained and encrypted using the private data managed on each local device. Following this, a global model is generated by aggregating them at the central server. The optimal model is trained for establishing the mapping function between channel state information (CSI) and RIS' configuration matrix in order to maximize the achievable rate of the received signal. Simulation results demonstrate that the proposed scheme can effectively approach to the theoretical value generated by centralized machine learning (ML), while protecting user' privacy.

The rise of targeted therapy and immunotherapy has challenged the conventional more-is-better phase I trial design paradigm that focuses on finding the MTD. In this article, we propose a novel model-assisted phase I/II design, called checkerboard design, that considers both toxicity and efficacy. As an extension of the keyboard design, the checkerboard design models the joint distribution of toxicity and efficacy, and divides toxicity and efficacy domain into a series of equal-width intervals or keys. In light of interim data, the checkerboard design continuously updates the posterior distribution of toxicity and efficacy, and adaptively determine the optimal dose for treating the next cohort of patients based on the posterior probability of toxicity and efficacy keys. As a model-assisted design, one important advantage of the checkerboard design is that its decision rule can be pretabulated, greatly simplifying its implementation. We also extend the checkerboard design to handle continuous efficacy endpoint. Simulations study shows that the checkerboard design yields competitive performance comparable to existing model-based phase I/II designs, but is simpler and easier to implement in real applications.

The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the best-in-class, offering more than $15\times $ speedup over the contemporary GPUs. However, the rapid growth in several DNN workloads conspires to escalate the energy consumptions of the TPU-based data-centers. In order to restrict the energy consumption of TPUs, we propose GreenTPU—a low-power near-threshold (NTC) TPU design paradigm. To ensure a high inference accuracy at a low-voltage operation, GreenTPU identifies the patterns in the error-causing activation sequences in the systolic array, and prevents further timing errors from similar patterns by intermittently boosting the operating voltage of the specific multiplier-and-accumulator units in the TPU. Compared to a cutting-edge timing error mitigation technique for TPUs, GreenTPU enables $2\times $ to $3\times $ higher performance (TOPS) in an NTC TPU, with a minimal loss in the prediction accuracy.

Health care platforms rapidly shift toward the ICT-based solutions. In this context, a wide range of consumer electronics technologies come into play ranging from robotics, embedded systems, sensors, and communication infrastructures. Driven by such observations, the H2020 RADIO project set forward a service-oriented, easily expandable design paradigm that emphasizes on how heterogenous commercial off-the-shelf (COTS) ICT technologies can be used as enablers of existing and new health-care services. Such services focus on activities of daily life (ADL) monitoring algorithms, on facilitating the indoor everyday life activities of the end user emphasizing on energy multifaceted conservation and security provision. All software components produced during the RADIO project with relative guidelines are freely available through dedicated GitHub repository (https://github.com/RADIO-PROJECT-EU).

Group signatures allow members of a group to anonymously sign on behalf of the group. Membership is administered by a designated group manager. The group manager can also reveal the identity of a signer if and when needed to enforce accountability and deter abuse. For group signatures to be applicable in practice, they need to support fully dynamic groups, i.e., users may join and leave at any time. Existing security definitions for fully dynamic group signatures are informal, have shortcomings, and are mutually incompatible. We fill the gap by providing a formal rigorous security model for fully dynamic group signatures. Our model is general and is not tailored toward a specific design paradigm and can therefore, as we show, be used to argue about the security of different existing constructions following different design paradigms. Our definitions are stringent and when possible incorporate protection against maliciously chosen keys. We consider both the case where the group management and tracing signatures are administered by the same authority, i.e., a single group manager, and also the case where those roles are administered by two separate authorities, i.e., a group manager and an opening authority. We also show that a specialization of our model captures existing models for static and partially dynamic schemes. In the process, we identify a subtle gap in the security achieved by group signatures using revocation lists. We show that in such schemes new members achieve a slightly weaker notion of traceability. The flexibility of our security model allows to capture such relaxation of traceability.

Several applications such as signal processing, multimedia and big data analysis exhibit computational error tolerance. This tolerance can be exploited to achieve efficient designs by sacrificing accuracy. Therefore, approximate computing presents a new design paradigm that smashes the traditional belief of error-free computations and provides efficient design with quality metrics specific to an application. The multiplication operation significantly determines the performance of the core due to the compute intensive operation. Therefore, this paper proposes a novel leading one bit based approximate (LoBA) multiplier architecture that selects k-bits from n-bit inputs (k ≤ n/4) based on leading one bit (LOB) and then computes approximate product based of these small input. The accuracy is further improved by selecting next k-bits based on LOB position and considering the partial product for computing final product. Four imprecise LoBA multipliers are presented that provide trade-off between accuracy and performance. Finally, the effectiveness of the proposed architectures is shown over the existing multipliers as standalone arithmetic unit and in the application by implementing Gaussian smoothing filters. The proposed 16-bit LoBA0 and LoBA1 designs reduce power consumption by 64.2% and 32.9%, respectively over the existing multiplier architecture.

The rapid development of additive manufacturing of cementitious materials has enabled the emergence of a new design paradigm, namely functional grading of material properties by location. Target performance parameters could be material weight and insulation value or (particularly important) ductility. A generic concept to achieve this, is through the selective addition of fibres or aggregates. In 3D concrete printing (3DCP), this concept can be developed into two strategies: by adding particles (i) to the bulk mixture through a second stage mixing process at the printer head (Simultaneous Process, SP), or (ii) in between the layers of deposited cementitious filament (Repetitive Sequential Process, RSP). The present paper presents the development of specific equipment required to obtain on–demand functional grading of the printed material. Subsequently, the application of these systems in print trials is shown. The current study focussed on ductility by creating fibre–reinforced 3D printed concrete through both strategies. The mechanical performance of the obtained material has been established through compressive, flexural, and crack–mouth opening displacement tests. To underline the generic nature of the strategies, a trial with lightweight aggregates has also been performed. It was shown that particularly the SP is capable of achieving improvements in ductility and self–weight.

A recent design paradigm seeks to overcome the challenges associated with broadly exploring a design space requiring computationally expensive model evaluations by formally viewing design as a sequential decision process (SDP). With the SDP, a set of computational models of increasing fidelity are used to sequentially evaluate and systematically eliminate inefficient design alternatives from further consideration. Key to the SDP are concept models that are of lower fidelity than the true function and are constructed in such a way that when used to evaluate a given design, they return two-sided limits that bound the precise value of the decision criteria, hence referred to as bounding models. Efficiency in the SDP is achieved by using such low-fidelity, inexpensive models, early in the design process to eliminate inefficient design alternatives from consideration after which a higher fidelity, more computationally expensive model, is executed, but only on those design alternatives that appear promising. In general, low-fidelity models trade off discriminatory power for computational complexity; however, it can be demonstrated that knowledge of the underlying physics and/or mathematics can be used to increase the discriminatory power of the lower fidelity models for a given computational cost. Increasing the discriminatory power of the bounding models directly translates into an increase in the efficiency of the SDP. This paper discusses and demonstrates how knowledge of the underlying physics and/or mathematics, otherwise referred to as “problem-specific knowledge,” such as monotonicity and concavity can be used to increase the discriminatory power of the bounding models in the context of the SDP and for engineering designs characterized by demand and capacity relationships. Furthermore, the concept of constructing the bounding models to systematically defer decisions on a subset of design variables, for example for a subsystem, is demonstrated, while retaining the desirable convergence guarantees to the optimal set. The utility of leveraging knowledge to increase discriminatory power and systematically deferring decisions through bounding models in the context of the SDP is demonstrated through two design problems: (1) the notional design of an engine-propeller combination to minimize takeoff distance for a light civil aircraft, and (2) the design of a building’s seismic force resisting structural-foundation system where the performance is evaluated on the basis of minimizing drift and total system cost.

The development of architecture along with the development of situations and conditions that exist in the community. In this postmodernism era, architectural design is not only based on one paradigm, but is more free to choose the architectural design figure that will be achieved. The development of this infinite design can be studied or appreciated through a communication approach between an architectural object and an observer. The architectural design of this era can have different meanings among observers. This proves that the communication system does not guarantee the meaning of a building. This system only runs that the building can be interpreted based on a communication system in the form of a sign like the one in Semiotics. Using qualitative and quantitative descriptive methods in seeing the current development of architecture. This gives the conclusion that architectural design not only has a meaning but many meanings in accordance with the design paradigm used.

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

DNA strand displacement reactions (SDRs) provide a set of intelligent toolboxes for developing molecular computation. Whereas SDR-based logic gate circuits have achieved a high level of complexity, the scale-up for practical achievable computational tasks remains a hurdle. Switching circuits that were originally proposed by Shannon in 1938 and nowadays widely used in telecommunication represent an alternative and efficient means to realize fast-speed and high-bandwidth communication. Here we develop SDR-based DNA switching circuits (DSCs) for implementing digital computing. Using a routing strategy on a programmable DNA switch canvas, we show that arbitrary Boolean functions can be represented by DSCs and implemented with molecular switches with high computing speed. We further demonstrate the implementation of full-adder and square-rooting functions using DSCs, which only uses down to 1/4 DNA strands as compared with a dual-rail logic expression-based design. We expect that DSCs provide a design paradigm for digital computation with biomolecules., DNA strand displacement reactions can be difficult to scale up for computational tasks. Here the authors develop DNA switching circuits that achieve high-speed computing with fewer molecules.

Design productivity remains an important aspect in the analog integrated circuit design industry, as growing competition and shorter design cycles pressure the traditional flow that involves time-consuming manual iterations in a circuit simulator. This paper describes innovations within an alternative framework that uses precomputed look-up tables (LUTs) to enable fast and accurate evaluation of circuit sizing scenarios without a simulator in the loop. It lets the designer explore and understand the design space boundaries in a systematic setting, thus supporting informed decision making and architectural innovation that is difficult to attain with fully automated, black-box sizing tools. Our discussion begins with an overview of the LUT-based design paradigm and its two primary variants: inverse design (finding design parameters that meet the specifications) and forward evaluation (sweeping design parameters to search the design space). In support of the latter, the core of our work focuses on improving the accuracy and speed of LUT access, enabling millions of queries within seconds on a standard computer. Large improvements over prior art are enabled using enhanced interpolation methods, which allow for a relatively large LUT grid spacing (hence small memory footprint) and yet accurate parameter lookup. We evaluate the efficacy of the proposed methods using two classical analog circuits, a bandgap reference and a folded cascode amplifier. In the bandgap example, we observe less than 1 ppm error between the LUT-predicted temperature coefficient and circuit simulation. In the folded-cascode example, one million design points are generated in only 4 seconds, providing the designer with useful maps that delineate the reachability of certain target specifications.

This paper studies the robust output regulation problem of general linear continuous-time systems with periodically sampled measurements, consisting of both the regulation errors and the extra measurements. With some standard conditions, we propose a novel robust implementable regulator design paradigm, that is comprised of a generalized zero-order hold device, a discrete-time compensator, a discrete-time washout filter and a discrete-time stabilizer.