Otoacoustic protection in biologically-inspired systems

Systems, methods and apparatus are provided through which in some embodiments an autonomic unit transmits an otoacoustic signal to counteract a potentially harmful incoming signal

Features and Cost Comparison of Biologically Inspired Vision Systems

The economic analysis of the advantages of known analogues of biologically inspired systems for unmanned aerial vehicles (UAVs), quadrocopters, etc

Digital-to-Analog Converter Interface for Computer Assisted Biologically Inspired Systems

In today\u27s integrated circuit technology, system interfaces play an important role of enabling fast, reliable data communications. A key feature of this work is the exploration and development of ultra-low power data converters. Data converters are present in some form in almost all mixed-signal systems; in particular, digital-to-analog converters present the opportunity for digitally controlled analog signal sources. Such signal sources are used in a variety of applications such as neuromorphic systems and analog signal processing. Multi-dimensional systems, such as biologically inspired neuromorphic systems, require vectors of analog signals. To use a microprocessor to control these analog systems, we must ultimately convert the digital control signal to an analog control signal and deliver it to the system. Integrating such capabilities of a converter on chip can yield significant power and chip area constraints. Special attention is paid to the power efficiency of the data converter, the data converter design discussed in this thesis yields the lowest power consumption to date. The need for a converter with these properties leads us to the concept of a scalable array of power-efficient digital-to-analog converters; the channels of which are time-domain multiplexed so that chip-area is minimized while preserving performance. To take further advantage of microprocessor capabilities, an analog-to- digital design is proposed to return the analog system\u27s outputs to the microprocessor in a digital form. A current-steering digital-to-analog converter was chosen as a candidate for the conversion process because of its natural speed and voltage-to-current translation properties. This choice is nevertheless unusual, because current-steering digital- to-analog converters have a reputation for high performance with high power consumption. A time domain multiplexing scheme is presented such that a digital data set of any size is synchronously multiplexed through a finite array of converters, minimizing the total area and power consumption. I demonstrate the suitability of current-steering digital-to-analog converters for ultra low-power operation with a proof-of-concept design in a widely available 130 nm CMOS technology. In statistical simulation, the proposed digital-to-analog converter was capable of 8-bit, 100 kSps operation while consuming 231 nW of power from a 1 V supply

Evolvable Embryonics: 2-in-1 Approach to Self-healing Systems

This paper covers the authors’ recent research in the area of evolutionary design optimisation in electronic application domain (Evolvable Hardware). This will be also presented in the context of biologically inspired systems where Evolvable Hardware is concerned with evolutionary synthesis of self-healing systems and potentially hardware capable of online adaptation to dynamically changing environment. We will also illustrate how EAs can produce novel and unintuitive design solutions, and possibly new design principles. The novelty of this research project addresses this compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired mechanism to support the design optimisation of self-healing architectures, that is Evolvable-Embryonics

The Capacity of String-Duplication Systems

It is known that the majority of the human genome consists of duplicated sequences. Furthermore, it is believed that a significant part of the rest of the genome also originated from duplicated sequences and has mutated to its current form. In this paper, we investigate the possibility of constructing an exponentially large number of sequences from a short initial sequence using simple duplication rules, including those resembling genomic-duplication processes. In other words, our goal is to find the capacity, or the expressive power, of these string-duplication systems. Our results include exact capacities, and bounds on the capacities, of four fundamental string-duplication systems. The study of these fundamental biologically inspired systems is an important step toward modeling and analyzing more complex biological processes

Biological Inspiration for Multiple Memories Implementation and Cooperation

Colloque avec actes et comité de lecture. internationale.International audienceBiological inspiration has led to the design of many connectionist models and mechanisms. Among them, memorization mechanisms are of particular importance to endow biologically inspired systems with efficient and consistent adaptive abilities. In this paper, we report recent modelling works of this kind, implementing procedural, episodic and working memories and making them cooperate for autonomous agent navigation

A Temporally Coherent Neural Algorithm for Artistic Style Transfer

Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions

Towards Biological Inspiration in the Development of Complex Systems

Greater understanding of biology in modem times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in "biologically inspired" systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be inspired by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems

Selectivity and specificity: pros and cons in sensing

Sensing using specific and selective receptors provides two very different but complementary strategies. This Sensor Issues article will discuss the merits and challenges of specific sensors, and selective sensors based on synthetic arrays. We will examine where each has been successfully applied to a sensing challenge, and then look at how a combined approach could take elements of both to provide new sensor platforms