Achieving Balance Through Fundamentals of Digital Librarianship

In efforts to strengthen its digitization program, the University Libraries of University of Guam have assembled members from its Robert F. Kennedy Memorial Library and Micronesian Area Research Center to identify standards and frameworks to facilitate sustainable, long-term access and preservation of its indigenous and historic collections. Following the 2019 Pacific Islands Association of Libraries Annual Conference hosted at the University of Guam RFK Library and the unveiling of the Para Hulo' Strategic Plan, a greater institutional emphasis has been placed on the need for digitally accessible archives and remote access. University Libraries investigates the need to balance utilitarian functions of traditional librarianship with the democratic ideals inherent in the profession, as expressed through revisiting a range of literature to articulate the connections between digital librarianship, traditional librarianship, and analog to digital conversion.

Influence of Analog-To-Digital Conversion and Decimation Parameters on the Signal-To-Noise Ratio in the Radar Path

Introduction. The digital representation of received radar signals has provided a wide range of opportunities for their processing. However, the used hardware and software impose some limits on the number of bits and sampling rate of the signal at all conversion and processing stages. These limitations lead to a decrease in the signal-to-interference ratio due to quantization noise introduced by powerful components comprising the received signal (interfering reflections; active noise interference), as well as the attenuation of a low-power reflected signal represented by a limited number of bits. In practice, the amplitude of interfering reflections can exceed that of the signal reflected from the target by a factor of thousands.Aim. In this connection, it is essential to take into account the effect of quantization noise on the signal-tointerference ratio.Materials and methods. The article presents expressions for calculating the power and power spectral density (PSD) of quantization noise, which take into account the value of the least significant bit of an analog-to-digital converter (ADC) and the signal sampling rate. These expressions are verified by simulating 4-, 8- and 16-bit ADCs in the Mathcad environment.Results. Expressions are derived for calculating the quantization noise PSD of interfering reflections, which allows the PSD to be taken into account in the signal-to-interference ratio at the output of the processing chain. In addition, a comparison of decimation options (by discarding and averaging samples) is performed drawing on the estimates of the noise PSD and the signal-to-noise ratio.Conclusion. Recommendations regarding the ADC bit depth and sampling rate for the radar receiver are presented.

Systematization and Comparison of the Binary Successive Approximation Variants

This paper presents a systematization and a comparison of the binary successive approximation (SA) variants. Three different variants are distinguished and all of them are applied in the analog-to-digital conversion. Regardless of an analog-to-digital converter circuit solution, the adoption of the specific SA variant imposes a particular character of the conversion process and related parameters. One of them is the ability to direct conversion of non-removeable physical quantities such as time intervals. Referencing to this aspect a general systematization of the variants and a name for each of them is proposed. In addition, the article raises the issues related to the complexity of implementation and energy consumption for each of the discussed binary SA variants.

Synthetic neuromorphic computing in living cells

Biological regulatory networks in cells and neuronal networks employ complex circuit topologies with highly interconnected nodes to perform sophisticated information processing. Despite the complexity of neuronal networks, their information processing and computational capabilities can be recapitulated using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Here, we argue that analogous to their revolutionary impact on computing, neuro-inspired models can similarly transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and can be readily reconfigurable for new tasks. We introduce neuromorphic design for synthetic gene circuits by first defining the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in the cellular milieu. Working in Escherichia coli cells, we experimentally demonstrated logarithmic scale analog multiplication using a single perceptgene. We modified perceptgene parameters (weights and biases) to create devices that compute a log-transformed negative rectifier encoding the minimum operation, log-transformed positive rectifier encoding the maximum operation, and log-transformed average of analog inputs. We then created multi-layer perceptgene circuits that compute a majority function, perform analog-to-digital conversion, and implement a ternary switch. Experimental and theoretical analysis showed that our approach enables circuit optimization via artificial intelligence algorithms such as gradient descent and backpropagation. Realizing neural-like computing in the noisy resource-limited environments of individual cells marks an important step towards synthetic biological systems that can be engineered effectively via supervised ANN optimization algorithms.

Control-Bounded Analog-to-Digital Conversion

A control-bounded analog-to-digital converter consists of a linear analog system that is subject to digital control, and a digital filter that estimates the analog input signal from the digital control signals. Such converters have many commonalities with delta–sigma converters, but they can use more general analog filters. The paper describes the operating principle, gives a transfer function analysis, and describes the digital filtering. In addition, the paper discusses two examples of such architectures. The first example is a cascade structure reminiscent of, but simpler than, a high-order MASH converter. The second example combines two attractive properties that have so far been considered incompatible. Its nominal conversion noise (assuming ideal components) essentially equals that of the first example. However, its analog filter is a fully connected network to which the input signal is fed in parallel, which potentially makes it more robust against nonidealities.

A pneumatic random-access memory for controlling soft robots

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas.