A novel scan architecture for power-efficient, rapid test

Scan-based testing methodologies remedy the testability problem of sequential circuits; yet they suffer from prolonged test time and excessive test power due to numerous shift op-erations. The high density of the unspecified bits in test data enables the utilization of the test response data captured in the scan chain for the generation of the subsequent test stimulus, thus reducing both test time and test data volume. The pro-posed scan-based test scheme accesses only a subset of scan cells for loading the subsequent test stimulus while freezing the remaining scan cells with the response data captured, thus decreasing the scan chain transitions during shift operations. The experimental results confirm the significant reductions in test application time, test data volume and test power achieved by the proposed scan-based testing methodology.

A 64-point Fourier transform chip for high-speed wireless LAN application using OFDM

In this article, we present a novel fixed-point 16-bit word-width 64-point FFT/IFFT processor developed primarily for the application in the OFDM based IEEE 802.11a Wireless LAN (WLAN) baseband processor. The 64-point FFT is realized by decomposing it into a 2-D structure of 8-point FFTs. This approach reduces the number of required complex multiplications compared to the conventional radix-2 64-point FFT algorithm. The complex multiplication operations are realized using shift-and-add operations. Thus, the processor does not use any 2-input digital multiplier. It also does not need any RAM or ROM for internal storage of coefficients. The proposed 64-point FFT/IFFT processor has been fabricated and tested successfully using our in-house 0.25 ?m BiCMOS technology. The core area of this chip is 6.8 mm2. The average dynamic power consumption is 41 mW @ 20 MHz operating frequency and 1.8 V supply voltage. The processor completes one parallel-to-parallel (i. e., when all input data are available in parallel and all output data are generated in parallel) 64-point FFT computation in 23 cycles. These features show that though it has been developed primarily for application in the IEEE 802.11a standard, it can be used for any application that requires fast operation as well as low power consumption

Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competetive accuracy with respect to state-of-the-art non-spiking neural models.Comment: 13 figures (includes supplementary information

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

An Efficient Uplink Multi-Connectivity Scheme for 5G mmWave Control Plane Applications

The millimeter wave (mmWave) frequencies offer the potential of orders of magnitude increases in capacity for next-generation cellular systems. However, links in mmWave networks are susceptible to blockage and may suffer from rapid variations in quality. Connectivity to multiple cells - at mmWave and/or traditional frequencies - is considered essential for robust communication. One of the challenges in supporting multi-connectivity in mmWaves is the requirement for the network to track the direction of each link in addition to its power and timing. To address this challenge, we implement a novel uplink measurement system that, with the joint help of a local coordinator operating in the legacy band, guarantees continuous monitoring of the channel propagation conditions and allows for the design of efficient control plane applications, including handover, beam tracking and initial access. We show that an uplink-based multi-connectivity approach enables less consuming, better performing, faster and more stable cell selection and scheduling decisions with respect to a traditional downlink-based standalone scheme. Moreover, we argue that the presented framework guarantees (i) efficient tracking of the user in the presence of the channel dynamics expected at mmWaves, and (ii) fast reaction to situations in which the primary propagation path is blocked or not available.Comment: Submitted for publication in IEEE Transactions on Wireless Communications (TWC

MORA - an architecture and programming model for a resource efficient coarse grained reconfigurable processor

This paper presents an architecture and implementation details for MORA, a novel coarse grained reconfigurable processor for accelerating media processing applications. The MORA architecture involves a 2-D array of several such processors, to deliver low cost, high throughput performance in media processing applications. A distinguishing feature of the MORA architecture is the co-design of hardware architecture and low-level programming language throughout the design cycle. The implementation details for the single MORA processor, and benchmark evaluation using a cycle accurate simulator are presented

Management and Security of IoT systems using Microservices

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi’s and IoT devices from different vendors. We validate our design by presenting some simple experimental results