Limited Data Bandwidth Research Articles

Resource Consumption and Radiation Tolerance Assessment for Data Analysis Algorithms Onboard Spacecraft

Spacecraft operating at great distances experience limited data bandwidth and high latency for communication with Earth. Data analysis algorithms that operate onboard, the spacecraft can perform detection and discovery of events of interest without human intervention. This capability serves to increase the quality and quantity of science data collected by the mission through data summarization, downlink prioritization, and adaptive instrument mode switching. However, before such technology can be adopted for use by a mission, it is necessary to characterize the required memory and computational resources. For operation in high-radiation environments, such as in orbit around the gas giants, a characterization of radiation tolerance is also important. In this article, we propose a framework to assess the resource and radiation profiles for machine learning algorithms in a simulated spacecraft computational environment. We apply this framework to several use cases designed for the Europa Clipper spacecraft, which plans to study Jupiter’s moon Europa. This approach can also benefit other remote deployments, such as the robotic exploration of hazardous environments on Earth.

Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.

Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an energy-efficient way. Recent work has shown that deep neural networks (DNNs) can serve as valuable models for CS of neural action potentials (APs). However, these models typically require impractically large datasets and computational resources for training, and they do not easily generalize to novel circumstances. Here, we propose a new CS framework, termed APGen, for the reconstruction of APs in a training-free manner. It consists of a deep generative network and an analysis sparse regularizer. We validate our method on two in vivo datasets. Even without any training, APGen outperformed model-based and data-driven methods in terms of reconstruction accuracy, computational efficiency, and robustness to AP overlap and misalignment. The computational efficiency of APGen and its ability to perform without training make it an ideal candidate for long-term, resource-constrained, and large-scale wireless neural recording. It may also promote the development of real-time, naturalistic brain-computer interfaces.

Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization

Objective. Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording. Approach. The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction. Main results. Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency. Significance. Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.

QoS-prioritised media delivery with adaptive data throughput in IoT-based home networks

To maintain network efficiency of a home network, it is crucial to moderately distribute limited data throughput and bandwidth resources to various home devices and services that require different network resource provisions. This paper accounts for QoS differentiation and fairness of network resource allocation which are essential to the design of an efficient IoT-based home media delivery mechanism against network traffic dynamics. Accordingly, our study in this paper proposes a QoS-prioritised media delivery mechanism with adaptive data throughput. This mechanism design includes several functions: assigning weights of relative importance to home service types, adjusting media quality of home services, reducing data workload against traffic congestion, and fairly distributing free bandwidth to prioritised home services. Furthermore, our study fulfills a simplified proof-of-concept implementation in a small-scaled IoT-based home network. Practical experiments can generate real measure data for performance examination. Results show that the proposed mechanism is able to sustain not only QoS differentiation like data throughput and delay, but also fair user satisfaction with comfortable media playing quality.

QoS-prioritised media delivery with adaptive data throughput in IoT-based home networks

To maintain network efficiency of a home network, it is crucial to moderately distribute limited data throughput and bandwidth resources to various home devices and services that require different network resource provisions. This paper accounts for QoS differentiation and fairness of network resource allocation which are essential to the design of an efficient IoT-based home media delivery mechanism against network traffic dynamics. Accordingly, our study in this paper proposes a QoS-prioritised media delivery mechanism with adaptive data throughput. This mechanism design includes several functions: assigning weights of relative importance to home service types, adjusting media quality of home services, reducing data workload against traffic congestion, and fairly distributing free bandwidth to prioritised home services. Furthermore, our study fulfills a simplified proof-of-concept implementation in a small-scaled IoT-based home network. Practical experiments can generate real measure data for performance examination. Results show that the proposed mechanism is able to sustain not only QoS differentiation like data throughput and delay, but also fair user satisfaction with comfortable media playing quality.

A High Reliability Sense Amplifier for Computing In-Memory with STT-MRAM

In the era of big data, massive data requires processing efficiently. However, the limited data bandwidth between the memory and the processor in conventional computer systems could not meet the requirement of data transferring. Computing in-memory has been considered an effective solution to address this problem. In this paper, based on the spin transfer torque-magnetic random access memory (STT-MRAM), a computing in-memory architecture with as few peripheral circuits as possible is proposed. This computing in-memory architecture gives the specific reference cell so that two rows in one array can be activated simultaneously to perform bitwise logic operations, such as OR/NOR and AND/NAND. In addition, with technology scaling down, STT-MRAM suffers from high sensitivity to process variation, which results in more device mismatch in a sense circuit. Additionally, the negative bias temperature instability (NBTI) seriously affects the life of PMOS transistors used in a sense circuit. In this paper, a high-reliability sense amplifier for computing in-memory with STT-MRAM is proposed. By using two self-enabled switching transistors, the proposed sense amplifier not only can decrease the NBTI effect on PMOS transistors but also can achieve a low sensing error rate. Using a CMOS 40[Formula: see text]nm design-kit and an accurate compact model of the STT magnetic tunnel junction (MTJ), mixed transient and statistical simulations have been present to demonstrate the functionality and performance of the proposed circuits.

A Training-Free One-Bit Compressed Sensing Framework for Wireless Neural Recording

Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) has successfully demonstrated its potential in this field. This letter proposes a training-free one-bit compressed sensing framework for wireless neural recording applications. By adopting the analysis model to enforce the signal sparsity and constructing a multi-order difference matrix as the analysis operator, it avoids the dictionary learning procedure and reduces the need for previously acquired data and computational complexity. Also, an analysis $\ell _{p}$ -minimization approach, which can be effectively solved by the analysis iteratively reweighted least squares algorithm, is proposed to recover signals from one-bit measurements. Experimental results on real data set reveal that the proposed approach not only drastically reduces the transmission bits but also outperforms the state-of-the-art CS-based methods in terms of both recovery accuracy and noise robustness.

Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach

Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. Based on deep learning theory, this paper presents a binarized autoencoder scheme for CS, in which a binary sensing matrix and a noniterative recovery solver are jointly optimized. Experimental results on synthetic dataset reveal that the proposed approach outperforms the state-of-the-art CS-based methods both in terms of recovery quality and computation time.

Training-free compressed sensing for wireless neural recording using analysis model and group weighted -minimization

Objective. Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis -minimization (GWALM), is proposed for wireless neural recording. Approach. The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis -minimization. Main results. Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. Significance. Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.

One-bit compressed sensing reconstruction for block sparse signals

Data compression is crucial for resource-constrained signal acquisition and wireless transmission applications with limited data bandwidth. In such applications, wireless data transmission dominates the energy consumption, and the limited telemetry bandwidth could be overwhelmed by the large amount of data generated from multiple sensors. Conventional data compression techniques are computationally intensive, consume large silicon area and offset the energy benefits from reduced data transmission. Recently, compressed sensing (CS) has shown potential in achieving compression performance comparable to previous methods but it has simpler hardware. Especially, one-bit CS theory proves that the signs of compressed measurements contain sufficient information about signal reconstruction, gives that the signals are sparse or compressible in specific dictionaries, thus demonstrating its potential in energy-constrained signal recording and wireless transmission applications. However, the sparsity assumption is too restrictive in many actual scenarios, especially when it is difficult to seek sparse representation for signals. In this paper, a novel one-bit CS method is proposed to reconstruct the signals that are difficult to represent with traditional sparse models. It is capable of recovering signal with comparable compression ratio but avoiding the dictionary selection procedure.The proposed method consists of two parts. 1) The block sparse model is adopted to enforce the structured sparsity of the signals. It not only overcomes the drawbacks of conventional sparse models but also enhances the signal representation accuracy. 2) The probabilistic model of one-bit CS procedure is constructed. Because of the existence of logistic function in probabilistic model of one-bit CS, the Bayesian inference cannot be used to proceed, and the variational Bayesian inference algorithm is developed to reconstruct the original signals from one-bit measurements.Various experiments on different quantities of compressed measurements and iterations are carried out to evaluate the recovery performance of the proposed approach. The photoplethysmography (PPG) signals recorded from subject wrist (dorsal locations) by using PPG sensors built in a wristband are selected as the validation data because they are difficult to represent with traditional sparse dictionaries. The experimental results reveal that the proposed approach outperforms the state-of-the-art one-bit CS method in terms of both reconstruction accuracy and convergence rate.Compared with prior method on one-bit CS, the proposed method shows competitive or superior performance in three aspects. Firstly, by adopting the block sparse model, the proposed method improves the capability to compress signals that are difficult to represent with traditional sparse models, thus making it more practical for long term and real applications. Secondly, by embedding the statistical properties of the one-bit measurements into the recovery algorithm, the proposed method outperforms other one-bit CS methods in terms of both reconstruction performance and convergence speed. Finally, energy and computational efficiency of the proposed method make it an ideal candidate for resource-constrained, large scale, multiple channel signal acquisition and transmission applications.

Real Time Artificial Neural Network FPGA Implementation for Triple Coincidences Recovery in PET

In small-animal Positron Emission Tomography (PET), spatial resolution improvements rely on detector minimization in size and often come at the expense of lowering the detector photoelectric fraction. As a result, Inter-Crystal Scatter (ICS) occurrences are increased and affect the overall PET detection efficiency. To reclaim some lost efficiency, previous work used an artificial neural network (ANN) to identify the true line of response (LOR) for the simplest multiple event detection case, three coincident singles known as triplets. Despite promising results, this method is limited to an offline processing which is impractical when a limited data bandwidth is present between the scanner and the PC. This paper demonstrates the capability of processing triplets in real time using an ANN implemented in the field-programmable gate array (FPGA). The ANN pipelined architecture can process over 1 million triplets/second using less than 6000 FPGA slices. Real time processing on the LabPET I scanner yielded an overall 39.7% increase in detection efficiency relative to traditional high resolution settings with a 360-660 keV energy window along with a slight Contrast-to-Noise Ratio ( $CNR$ ) degradation. Although improvements are still possible, the proposed FPGA implementation proves the usability of an ANN in real time PET applications in conditions where spare computational resources are limited and the data rate to be processed is high.

Simulations on the Stability and Stabilization Method for Random Long Delay Network Control

Networked Control System (NCS) is a class of feedback control system forming a closed loop through the communication network. Due to the nodes conflicts, poor network stability, limited data bandwidth and other uncontrollable factors in the transmission, network-induced delay is the main factor reducing system performance and stability. Studying the state feedback rules of Long Delay Network Control system, the author proposes Stability and stabilization method for Random Long Delay Network Control. The method models random long delay closed-loop network control system, compensate the delay and complete the network stability and stabilization. Finally, a numerical example is given to verify the validity of the conclusion.

Desert RATS 2011: Human and robotic exploration of near-Earth asteroids

The Desert Research and Technology Studies (D-RATS) 2011 field test involved the planning and execution of a series of exploration scenarios under operational conditions similar to those expected during a human exploration mission to a near-Earth asteroid (NEA). The focus was on understanding the operations tempo during simulated NEA exploration and the implications of communications latency and limited data bandwidth. Anchoring technologies and sampling techniques were not evaluated due to the immaturity of those technologies and the inability to meaningfully test them at D-RATS. Reduced gravity analogs and simulations are being used to fully evaluate Space Exploration Vehicle (SEV) and extravehicular (EVA) operations and interactions in near-weightlessness at a NEA as part of NASA's integrated analogs program. Hypotheses were tested by planning and performing a series of 1-day simulated exploration excursions comparing test conditions all of which involved a single Deep Space Habitat (DSH) and either 0, 1, or 2 SEVs; 3 or 4 crewmembers; 1 of 2 different communications bandwidths; and a 50-second each-way communications latency between the field site and Houston. Excursions were executed at the Black Point Lava Flow test site with a remote Mission Control Center and Science Support Room at Johnson Space Center (JSC) being operated with 50-second each-way communication latency to the field. Crews were composed of astronauts and professional field geologists. Teams of Mission Operations and Science experts also supported the mission simulations each day. Data were collected separately from the Crew, Mission Operations, and Science teams to assess the test conditions from multiple perspectives. For the operations tested, data indicates practically significant benefits may be realized by including at least one SEV and by including 4 versus 3 crewmembers in the NEA exploration architecture as measured by increased scientific data quality, EVA exploration time, capability assessment ratings, and consensus acceptability ratings provided by Crew, Mission Operations, and Science teams. A combination of text and voice was used to effectively communicate over the communications latency, and increased communication bandwidth yielded a small but practically significant improvement in overall acceptability as rated by the Science team, although the impact of bandwidth on scientific strategic planning and public outreach was not assessed. No effect of increased bandwidth was observed with respect to Crew or Mission Operations team ratings of overall acceptability.

An Efficient Data-Distribution Mechanism in a Processor-In-Memory (PIM) Architecture Applied to Motion Estimation

In general, the main purpose of using processor-in-memory (PIM) modules is to dramatically increase the data-level parallelism (DLP) and avoid the limited issue rate of current systems (even when they include SIMD extensions) caused by the limited data bandwidth and functional units. Our approach is to divide the PIM module into hundreds of smaller pieces so that each of these smaller PIMs can execute motion estimation for a group of macro blocks in a parallel fashion. We also design the logic in each PIM to execute in a highly pipelined fashion so that even more parallelism can be exploited. The main contribution of this paper is the presentation of architectural techniques that can be used in the PIM module to overcome the addressing and data sharing overhead when these smaller PIMs are used. Our architectural techniques have been applied to motion estimation. Indeed, it has been reported that motion estimation takes the majority of the execution time of MPEG encoding and it has been researched by many because of its importance in MPEG encoding. With our paradigm and techniques, the host processor can be relieved from the most computationally demanding and data-intensive portions of the workload, which should therefore yield a significant performance gain. Indeed, we observed (when 512 of these smaller PIMs were used) a reduction in the number of memory accesses by a factor of up to 2,034 times. At the same time, the performance improved by a multiplicative factor as high as 439 times.

Development of the first audio microphone for use on the surface of Mars

In 1999, NASA launched the Mars Polar Lander (MPL) to study volatiles and the climate history of Mars. Among the many instruments aboard the spacecraft was the first audio microphone sensor ever developed for the environment of Mars, included as a piggy-back sensor on the Russian LIDAR experiment. Funded by The Planetary Society and built and tested by the U.C. Berkeley Space Sciences Laboratory, the Mars Microphone was a unique and powerful tool for public engagement with the Mars exploration program. The possibilities for sound sources on Mars included wind and dust found in dust devils and storms, atmospheric electricity, and lander-generated sounds such as camera movements and the digging of the robotic arm. Here we outline the design, development, and test program for a spaceflight acoustic sensor, where the unique challenges of extreme environments, limited data bandwidth, and requirements for a low-power, light-weight system were overcome. While the MPL mission was lost and no data ever returned, the Mars Microphone nonetheless stimulated interest in the science of extra-terrestrial acoustics and in future planetary mission instruments based on acoustic methods.

Stratal slicing of Miocene-Pliocene sediments in Vermilion Block 50-Tiger Shoal Area, offshore Louisiana

Many people have viewed modern land surfaces from commercial airplanes and marveled at the form and geometry of geomorphic features such as river channels, deltas, barrier islands, and dune fields. These views represent complete images of the modern time surfaces. We can classify the depositional nature of features on these images by interpreting their planiform geometry and geographic context. In fact, modern 3-D seismic technology has made it possible for us to image similar, but much older, geomorphic or depositional features preserved in the rock record. Unfortunately, although many reservoir-scale (well-to-well scale) features can be detected in vertical seismic lines, few such features can be resolved and interpreted in the vertical perspective because of the data's limited bandwidth. Only in the horizontal perspective are such depositional features large enough to be resolvable when displayed in map view on geologic time surfaces.

1-D seismic inversion of dual wavefield data: Part I, Nonuniqueness and stability

The inversion problem for determining seismic impedance is nonunique and nonstable because of limited recording aperture, data bandwidth, and data noise. For large reflection angles, small errors in the reflection coefficients give rise to arbitrarily large errors in the seismic impedance estimates. Spatial resolution of the seismic impedance response is controlled by the dominant wavelength corresponding to the source time wavelet; aperture limitations control the resolution of material impedance and interval velocity. Analysis of a linearization‐approximation approach shows that this method degenerates into a single‐parameter estimator for material impedance when using only small‐offset data and for velocity when using only far‐offset data. A nonlocal inversion method is introduced to estimate the material impedance and interval velocity by exploring interval velocity space and computing an associated variance estimate surface. Using this method, the resolution of the material impedance and compressional and shear interval velocities is shown to be poor in the elastic case because of a “valley” feature in the variance estimate surface; in the acoustic problem, resolution of the material impedance and interval velocity is excellent.