Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Adaptive transmit-side equalization for serial electrical interconnects at 100 Gb/s using duobinary

The ever-increasing demand for more efficient data communication calls for new, advanced techniques for high speed serial communication. Although newly developed systems are setting records, off-line determination of the optimal equalizer settings is often needed. Well-known adaptive algorithms are mainly applied for receive-side equalization. However, transmit-side equalization is desirable for its reduced linearity requirements. In this paper, an adaptive sign-sign least mean square equalizer algorithm is developed applicable for an analog transmit-side feed-forward equalizer (FFE) capable of transforming non-return-to-zero modulation to duobinary (DB) modulation at the output of the channel. In addition to the derivation of the update strategy, extra algorithms are developed to cope with the difficult transmit-receive synchronization. Using an analog six tap bit-spaced equalizer, the algorithm is capable of optimizing DB communication of 100Gb/s over 1.5-m Twin-Ax cable. Both simulations and experimental results are presented to prove the capabilities of the algorithm demonstrating automated determination of FFE parameters, such that error-free communication is obtained (BER<10(-13) using PRBS9)

Architecture, constraints, and behavior

This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Familiar and accessible case studies are used to illustrate concepts of robustness, organization, and architecture (modularity and protocols) that are central to understanding complex networks. These essential organizational features are hidden during normal function of a system but are fundamental for understanding the nature, design, and function of complex biologic and technologic systems

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Cyber-Physical Systems: A Model-Based Approach

In this concise yet comprehensive Open Access textbook, future inventors are introduced to the key concepts of Cyber-Physical Systems (CPS). Using modeling as a way to develop deeper understanding of the computational and physical components of these systems, one can express new designs in a way that facilitates their simulation, visualization, and analysis. Concepts are introduced in a cross-disciplinary way. Leveraging hybrid (continuous/discrete) systems as a unifying framework and Acumen as a modeling environment, the book bridges the conceptual gap in modeling skills needed for physical systems on the one hand and computational systems on the other. In doing so, the book gives the reader the modeling and design skills they need to build smart, IT-enabled products. Starting with a look at various examples and characteristics of Cyber-Physical Systems, the book progresses to explain how the area brings together several previously distinct ones such as Embedded Systems, Control Theory, and Mechatronics. Featuring a simulation-based project that focuses on a robotics problem (how to design a robot that can play ping-pong) as a useful example of a CPS domain, Cyber-Physical Systems: A Model-Based Approach demonstrates the intimate coupling between cyber and physical components, and how designing robots reveals several non-trivial control problems, significant embedded and real-time computation requirements, and a need to consider issues of communication and preconceptions

A cometary ion mass spectrometer

The development of flight suitable analyzer units for that part of the GIOTTO Ion Mass Spectrometer (IMS) experiment designated the High Energy Range Spectrometer (HERS) is discussed. Topics covered include: design of the total ion-optical system for the HERS analyzer; the preparation of the design of analyzing magnet; the evaluation of microchannel plate detectors and associated two-dimensional anode arrays; and the fabrication and evaluation of two flight-suitable units of the complete ion-optical analyzer system including two-dimensional imaging detectors and associated image encoding electronics

Nonlinear dynamics in neuromorphic photonic networks: physical simulation in Verilog-A

Advances in silicon photonics technology have enabled the field of neuromorphic photonics, where analog neuron-like processing elements are implemented in silicon photonics technology. Accurate and scalable simulation tools for photonic integrated circuits are critical for designing neuromorphic photonic circuits. This is especially important when designing networks with recurrent connections, where the dynamics of the system may give rise to unstable and oscillatory solutions which need to be accurately modelled. These tools must simultaneously simulate the analog electronics and the multi-channel (wavelength-division-multiplexed) photonics contained in a photonic neuron to accurately predict on-chip behaviour. In this paper, we utilize a Verilog-A model of the photonic neural network to investigate the dynamics of recurrent integrated circuits. We begin by reviewing the theory of continuous-time recurrent neural networks as dynamical systems and the relation of these dynamics to important physical features of photonic neurons such as cascadability. We then present the neural dynamics of systems of one and two neurons in the simulated Verilog-A circuit, which are compared to the expected dynamics of the abstract CTRNN model. Due to the presence of parasitic circuit elements in the Verilog-A simulation, it is seen that there is a topological equivalence, but not an exact isomorphism, between the theoretical model and the simulated model. The implications of these discrepancies for the design of neuromorphic photonic circuits are discussed. Our findings pave the way for the practical implementation of large-scale silicon photonic recurrent neural networks.Comment: 17 pages, 9 figures. Submitted to Physical Review Applie

Hybrid monolithic integration of high-power DC-DC converters in a high-voltage technology

The supply of electrical energy to home, commercial, and industrial users has become ubiquitous, and it is hard to imagine a world without the facilities provided by electrical energy. Despite the ever increasing efficiency of nearly every electrical application, the worldwide demand for electrical power continues to increase, since the number of users and applications more than compensates for these technological improvements. In order to maintain the affordability and feasibility of the total production, it is essential for the distribution of the produced electrical energy to be as efficient as possible. In other words the loss in the power distribution is to be minimized. By transporting electrical energy at the maximum safe voltage, the current in the conductors, and the associated conduction loss can remain as low as possible. In order to optimize the total efficiency, the high transportation voltage needs to be converted to the appropriate lower voltage as close as possible to the end user. Obviously, this conversion also needs to be as efficient, affordable, and compact as possible. Because of the ever increasing integration of electronic systems, where more and more functionality is combined in monolithically integrated circuits, the cost, the power consumption, and the size of these electronic systems can be greatly reduced. This thorough integration is not limited to the electronic systems that are the end users of the electrical energy, but can also be applied to the power conversion itself. In most modern applications, the voltage conversion is implemented as a switching DC-DC converter, in which electrical energy is temporarily stored in reactive elements, i.e. inductors or capacitors. High switching speeds are used to allow for a compact and efficient implementation. For low power levels, typically below 1 Watt, it is possible to monolithically implement the voltage conversion on an integrated circuit. In some cases, this is even done on the same integrated circuit that is the end user of the electrical energy to minimize the system dimensions. For higher power levels, it is no longer feasible to achieve the desired efficiency with monolithically integrated components, and some external components prove indispensable. Usually, the reactive components are the main limiting factor, and are the first components to be moved away from the integrated circuit for increasing power levels. The semiconductor components, including the power transistors, remain part of the integrated circuit. Using this hybrid approach, it is possible in modern converterapplications to process around 60 Watt, albeit limited to voltages of a few Volt. For hybrid integrated converters with an output voltage of tens of Volt, the power is limited to approximately 10 Watt. For even higher power levels, the integrated power transistors also become a limiting factor, and are replaced with discrete power devices. In these discrete converters, greatly increased power levels become possible, although the system size rapidly increases. In this work, the limits of the hybrid approach are explored when using so-called smart-power technologies. Smart-power technologies are standard lowcost submicron CMOS technologies that are complemented with a number of integrated high-voltage devices. By using an appropriate combination of smart-power technologies and circuit topologies, it is possible to improve on the current state-of-the-art converters, by optimizing the size, the cost, and the efficiency. To determine the limits of smart-power DC-DC converters, we first discuss the major contributing factors for an efficient energy distribution, and take a look at the role of voltage conversion in the energy distribution. Considering the limitations of the technologies and the potential application areas, we define two test-cases in the telecommunications sector for which we want to optimize the hybrid monolithic integration in a smart-power technology. Subsequently, we explore the specifications of an ideal converter, and the relevant properties of the affordable smart-power technologies for the implementation of DC-DC converters. Taking into account the limitations of these technologies, we define a cost function that allows to systematically evaluate the different potential converter topologies, without having to perform a full design cycle for each topology. From this cost function, we notice that the de facto default topology selection in discrete converters, which is typically based on output power, is not optimal for converters with integrated power transistors. Based on the cost function and the boundary conditions of our test-cases, we determine the optimal topology for a smart-power implementation of these applications. Then, we take another step towards the real world and evaluate the influence of parasitic elements in a smart-power implementation of switching converters. It is noticed that the voltage overshoot caused by the transformer secondary side leakage inductance is a major roadblock for an efficient implementation. Since the usual approach to this voltage overshoot in discrete converters is not applicable in smart-power converters due to technological limitations, an alternative approach is shown and implemented. The energy from the voltage overshoot is absorbed and transferred to the output of the converter. This allows for a significant reduction in the voltage overshoot, while maintaining a high efficiency, leading to an efficient, compact, and low-cost implementation. The effectiveness of this approach was tested and demonstrated in both a version using a commercially available integrated circuit, and our own implementation in a smart-power integrated circuit. Finally, we also take a look at the optimization of switching converters over the load range by exploiting the capabilities of highly integrated converters. Although the maximum output power remains one of the defining characteristics of converters, it has been shown that most converters spend a majority of their lifetime delivering significantly lower output power. Therefore, it is also desirable to optimize the efficiency of the converter at reduced output current and output power. By splitting the power transistors in multiple independent segments, which are turned on or off in function of the current, the efficiency at low currents can be significantly improved, without introducing undesirable frequency components in the output voltage, and without harming the efficiency at higher currents. These properties allow a near universal application of the optimization technique in hybrid monolithic DC-DC converter applications, without significant impact on the complexity and the cost of the system. This approach for the optimization of switching converters over the load range was demonstrated using a boost converter with discrete power transistors. The demonstration of our smart-power implementation was limited to simulations due to an issue with a digital control block. On a finishing note, we formulate the general conclusions and provide an outlook on potential future work based on this research

Dynamic Focusing of Large Arrays for Wireless Power Transfer and Beyond

We present architectures, circuits, and algorithms for dynamic 3-D lensing and focusing of electromagnetic power in radiative near- and far-field regions by arrays that can be arbitrary and nonuniform. They can benefit applications such as wireless power transfer at a distance (WPT-AD), volumetric sensing and imaging, high-throughput communications, and optical phased arrays. Theoretical limits on system performance are calculated. An adaptive algorithm focuses the power at the receiver(s) without prior knowledge of its location(s). It uses orthogonal bases to change the phases of multiple elements simultaneously to enhance the dynamic range. One class of such 2-D orthogonal and pseudo-orthogonal masks is constructed using the Hadamard and pseudo-Hadamard matrices. Generation and recovery units (GU and RU) work collaboratively to focus energy quickly and reliably with no need for factory calibration. Orthogonality enables batch processing in high-latency and low-rate communication settings. Secondary vector-based calculations allow instantaneous refocusing at different locations using element-wise calculations. An emulator enables further evaluation of the system. We demonstrate modular WPT-AD GUs of up to 400 elements utilizing arrays of 65-nm CMOS ICs to focus power on RUs that convert the RF power to dc. Each RFIC synthesizes 16 independently phase-controlled RF outputs around 10 GHz from a common single low-frequency reference. Detailed measurements demonstrate the feasibility and effectiveness of RF lensing techniques presented in this article. More than 2 W of dc power can be recovered through a wireless transfer at distances greater than 1 m. The system can dynamically project power at various angles and at distances greater than 10 m. These developments are another step toward unified wireless power, sensing, and communication solutions in the future