Georgios Angelopoulos

Energy-efficient Time-Stampless Adaptive Nonuniform Sampling

Nowadays, since more and more battery-operated devices are involved in applications with continuous sensing, development of an efficient sampling mechanisms is an important issue for these applications. In this paper, we investigate power efficiency aspects of a recently proposed adaptive nonuniform sampling. This sampling scheme minimizes the energy consumption of the sampling process, which is approximately proportional to sampling rate. The main characteristics of our method are that, first, sampling times do not need to be transmitted, since the receiver can compute them by using a function of previously taken samples, and second, only innovative samples are taken from the signal of interest, reducing the sampling rate and therefore the energy consumption. We call this scheme Time-Stampless Adaptive Nonuniform Sampling (TANS). TANS can be used in several scenarios, showing promising results in terms of energy savings, and can potentially enable the development of new applications that require continuous signals sensing, such as applications related to health monitoring, location tracking and entertainment.

Multichannel Wiener filters in color image restoration

Deals with the design of multichannel Wiener filters both in the spatial and frequency domain. FIR and IIR Wiener filters are presented. Color image restoration based on multi-channel autoregressive (AR) image modeling is examined. Detailed discussions on the use of a multichannel Wiener filter in color image restoration incorporating the interchannel correlations and computer simulations are presented also. >

Energy-aware hardware implementation of network coding

In the last few years, Network Coding (NC) has been shown to provide several advantages, both in theory and in practice. However, its applicability to battery-operated systems under strict power constraints has not been proven yet, since most implementations are based on high-end CPUs and GPUs. This work represents the first effort to bridge NC theory with real-world, low-power applications. In this paper, we provide a detailed analysis on the energy consumption of NC, based on VLSI design measurements, and an approach for specifying optimal algorithmic parameters, such as field size, minimizing the required energy for both transmission and coding of data. Our custom, energy-aware NC accelerator proves the feasibility of incorporating NC into modern, lowpower systems; the proposed architecture achieves a coding throughput of 80MB/s (60MB/s), while consuming 22uW (12.5mW) for the encoding (decoding) process.

Experimental study of the interplay of channel and network coding in low power sensor applications

In this paper, we evaluate the performance of random linear network coding (RLNC) in low data rate indoor sensor applications operating in the ISM frequency band. We also investigate the results of its synergy with forward error correction (FEC) codes at the PHY-layer in a joint channel-network coding (JCNC) scheme. RLNC is an emerging coding technique which can be used as a packet-level erasure code, usually implemented at the network layer, which increases data reliability against channel fading and severe interference, while FEC codes are mainly used for correction of random bit errors within a received packet. The hostile wireless environment that low power sensors usually operate in, with significant interference from nearby networks, motivates us to consider a joint coding scheme and examine the applicability of RLNC as an erasure code in such a coding structure. Our analysis and experiments are performed using a custom low power sensor node, which integrates on-chip a low-power 2.4 GHz transmitter and an accelerator implementing a multi-rate convolutional code and RLNC, in a typical office environment. According to measurement results, RLNC of code rate 4/8 can provide an effective SNR improvement of about 3.4 dB, outperforming a PHY-layer FEC code of the same code rate, at a PER of 10-2. In addition, RLNC performs very well when used in conjunction with a PHY-layer FEC code as a JCNC scheme, offering an overall coding gain of 5.6 dB.

PRAC: Exploiting partial packets without cross-layer or feedback information

This paper proposes a partial packet recovery scheme, called Packetized Rateless Algebraic Consistency (PRAC). PRAC exploits intra and inter-packet consistency to identify and recover erroneous packet segments, without recourse to cross-layer or detailed feedback information. In the absence of cross-layer coordination or detailed feedback, the prevailing methods proposed in the literature have discarded packets with errors. PRAC uses a rateless linear packet code for data encoding and an iterative decoding process consisting of a search algorithm and an algebraic consistency rule (ACR) check. It allows, but not relies upon, the use of any PHY FEC code, requires no feedback other than a notification of completion and, in the absence of partial packets, incurs no overhead. Our implementation and experimental results in a 7-node indoor testbed using wireless boards equipped with CC2500 radio transceivers reveal that PRAC offers an average throughput gain of 35% compared to a baseline ARQ scheme discarding partial packets and 13% compared to an ideal genie-aided HARQ (iHARQ) scheme. Specifically for links with high PERs, PRAC significantly enhances their robustness and its maximum throughput gain is 148% and 34% compared against the baseline and iHARQ schemes, respectively.

Two-dimensional FFT algorithms on hypercube and mesh machines

Abstract This paper discusses the parallel computation of the 2-D discrete Fourier transform by using three different fast ourier transform (FFT) algorithms. The examined algorithms are: row-column FFT, vector radix FFT and polynomial transform FFT. Two machine architectures are considered: hypercube and mesh connected computers with non-shared memory. The proposed implementations allow both numeric computations and the bit-reversion operation to be performed in parallel. Detailed discussion about the performance of these FFTs is included.

Multichannel Wiener filters in color image restoration based on AR color image modelling

Color images are multichannel signals, whereas monochrome images are single-channel signals. Therefore, the extension of techniques used in monochrome digital restoration to color images is not straightforward. The interchannel correlations which exist in color images must be taken into account in the restoration procedure. The design of a multichannel Wiener filter in the frequency domain is presented. The sensitivity of the multichannel Wiener filter to the spectral estimates requires the use of a robust multichannel power spectral estimator. The use of multichannel autoregressive (AR) spectral estimators results in multichannel filters which have excellent performance.<<ETX>>

Harnessing Partial Packets in Wireless Networks: Throughput and Energy Benefits

This paper proposes a partial packet recovery scheme called packetized rateless algebraic consistency (PRAC). PRAC exploits intra- and inter-packet consistency to identify and recover erroneous packet segments, without recourse to soft physical layer (PHY) or detailed feedback information. PRAC uses a rateless linear code for data encoding and an iterative decoding process for data reconstruction. It allows, but does not rely upon, the use of any PHY forward error correction code, and requires no feedback other than a notification of completion and, in the absence of partial packets, incurs no overhead. In order to quantify PRAC’s performance in terms of both throughput and energy efficiency, experiments are conducted using commercial transceivers in two different scenarios. Our implementation results reveal that PRAC offers an average throughput gain of 35% compared with a baseline ARQ scheme discarding partial packets, and 13% compared with an ideal hybrid-ARQ scheme. On high PER links, throughput is improved by 148% and 34%, respectively. In addition, PRAC reduces on average the total energy consumption of the transmitting nodes by 16%, while, on high PER links, savings can be up to 50%.

Fast parallel DSP algorithms on barrel shifter computers

Parallel algorithms on barrel shifter computers for a broad class of 1-D and 2-D signal operators are presented. The max/min selection filter, the moving average filter, and the sorting and sliding window fast Fourier transform algorithms are examined. The proposed algorithms require a significantly smaller number of comparisons/computations than the conventional ones. A novel extension of the barrel shifter networks for 2-D signals is introduced, and the implementation of the proposed algorithms on them is also discussed.

Backward Adaptation for Power Efficient Sampling

Advances in sampling and coding theory have contributed significantly towards lowering power consumption of resource-constrained devices, e.g. battery-operated sensor nodes, enabling them to operate for extended periods of time. In this paper, rate and energy efficiency of a recently proposed adaptive nonuniform sampling framework by Feizi , called Time-Stampless Adaptive Nonuniform Sampling (TANS), is examined and compared against state-of-the-art methods. TANS addresses one of the main limitations of nonuniform sampling schemes: sampling times do not need to be stored/transmitted since they can be computed using a function of previously taken samples. The sampling rate is adapted continuously with the aim of reducing the rate and therefore the energy consumption of the sampling process when the signal is varying slowly. Three TANS methods are proposed for different signal models and sampling requirements: i) TANS by polynomial extrapolation, which only assumes the third derivative of the signal is bounded but requires no other specific knowledge of the signal; ii) TANS by incremental variation, where the sampling time intervals are chosen from a lattice; and iii) TANS constrained to a finite set of sampling rates. Practical implementation details of TANS are discussed, and its rate and energy performance are compared with uniform sampling followed by a transformation-based compression, nonuniform sampling, and compressed sensing. Our results demonstrate that TANS provides significant improvements in terms of both the rate-distortion performance and the energy consumption compared against the other approaches.