Significance Driven Computation: A Voltage-Scalable, Variation-Aware, Quality-Tuning Motion Estimator

In this paper we present a design methodology for algorithm/architecture co-design of a voltage-scalable, process variation aware motion estimator based on significance driven computation. The fundamental premise of our approach lies in the fact that all computations are not equally significant in shaping the output response of video systems. We use a statistical technique to intelligently identify these significant/not-so-significant computations at the algorithmic level and subsequently change the underlying architecture such that the significant computations are computed in an error free manner under voltage over-scaling. Furthermore, our design includes an adaptive quality compensation (AQC) block which "tunes" the algorithm and architecture depending on the magnitude of voltage over-scaling and severity of process variations. Simulation results show average power savings of similar to 33% for the proposed architecture when compared to conventional implementation in the 90 nm CMOS technology. The maximum output quality loss in terms of Peak Signal to Noise Ratio (PSNR) was similar to 1 dB without incurring any throughput penalty.

Containing the Nanometer "Pandora-Box": Cross-Layer Design Techniques for Variation Aware Low Power Systems

The demand for richer multimedia services, multifunctional portable devices and high data rates can only been visioned due to the improvement in semiconductor technology. Unfortunately, sub-90 nm process nodes uncover the nanometer Pandora-box exposing the barriers of technology scaling-parameter variations, that threaten the correct operation of circuits, and increased energy consumption, that limits the operational lifetime of today's systems. The contradictory design requirements for low-power and system robustness, is one of the most challenging design problems of today. The design efforts are further complicated due to the heterogeneous types of designs ( logic, memory, mixed-signal) that are included in today's complex systems and are characterized by different design requirements. This paper presents an overview of techniques at various levels of design abstraction that lead to low power and variation aware logic, memory and mixed-signal circuits and can potentially assist in meeting the strict power budgets and yield/quality requirements of future systems.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Real-time temperature estimation for power MOSFETs considering thermal aging effects

This paper presents a novel real-time power-device temperature estimation method that monitors the power MOSFET's junction temperature shift arising from thermal aging effects and incorporates the updated electrothermal models of power modules into digital controllers. Currently, the real-time estimator is emerging as an important tool for active control of device junction temperature as well as online health monitoring for power electronic systems, but its thermal model fails to address the device's ongoing degradation. Because of a mismatch of coefficients of thermal expansion between layers of power devices, repetitive thermal cycling will cause cracks, voids, and even delamination within the device components, particularly in the solder and thermal grease layers. Consequently, the thermal resistance of power devices will increase, making it possible to use thermal resistance (and junction temperature) as key indicators for condition monitoring and control purposes. In this paper, the predicted device temperature via threshold voltage measurements is compared with the real-time estimated ones, and the difference is attributed to the aging of the device. The thermal models in digital controllers are frequently updated to correct the shift caused by thermal aging effects. Experimental results on three power MOSFETs confirm that the proposed methodologies are effective to incorporate the thermal aging effects in the power-device temperature estimator with good accuracy. The developed adaptive technologies can be applied to other power devices such as IGBTs and SiC MOSFETs, and have significant economic implications. 

(Re) making trajectories: Eviction, control and power

The City of Cairo's experienced a major shift in its urban planning attitude and practice since the mid 1970s which mostly validated elements of economic planning while neutralizing its social aspects. This ad hoc approach escalated conflict in planning politics over the control of space between tiers of the planning institution and the locals leading to rigorous planning actions among major stakeholders such as eviction and control of spaces. The article examines how institutional claims over space reassembled alternative definitions of quality of life in one of Cairo's oldest quarters, and how ambitious planning schemes were mostly driven by entrepreneurial rather than societal. Based on first-hand interviews and visuals, the article aims to reveal the local's struggle to survive such interventions with attention to their daily negotiations in place.

Molecular pedigree reconstruction and estimation of evolutionary parameters in a wild Atlantic salmon river system with incomplete sampling: a power analysis

Background: Pedigree reconstruction using genetic analysis provides a useful means to estimate fundamental population biology parameters relating to population demography, trait heritability and individual fitness when combined with other sources of data. However, there remain limitations to pedigree reconstruction in wild populations, particularly in systems where parent-offspring relationships cannot be directly observed, there is incomplete sampling of individuals, or molecular parentage inference relies on low quality DNA from archived material. While much can still be inferred from incomplete or sparse pedigrees, it is crucial to evaluate the quality and power of available genetic information a priori to testing specific biological hypotheses. Here, we used microsatellite markers to reconstruct a multi-generation pedigree of wild Atlantic salmon (Salmo salar L.) using archived scale samples collected with a total trapping system within a river over a 10 year period. Using a simulation-based approach, we determined the optimal microsatellite marker number for accurate parentage assignment, and evaluated the power of the resulting partial pedigree to investigate important evolutionary and quantitative genetic characteristics of salmon in the system.

Results: We show that at least 20 microsatellites (ave. 12 alleles/locus) are required to maximise parentage assignment and to improve the power to estimate reproductive success and heritability in this study system. We also show that 1.5 fold differences can be detected between groups simulated to have differing reproductive success, and that it is possible to detect moderate heritability values for continuous traits (h(2) similar to 0.40) with more than 80% power when using 28 moderately to highly polymorphic markers.

Conclusion: The methodologies and work flow described provide a robust approach for evaluating archived samples for pedigree-based research, even where only a proportion of the total population is sampled. The results demonstrate the feasibility of pedigree-based studies to address challenging ecological and evolutionary questions in free-living populations, where genealogies can be traced only using molecular tools, and that significant increases in pedigree assignment power can be achieved by using higher numbers of markers.

Inexact-aware architecture design for ultra-low power bio-signal analysis

This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.

Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

Metabonomic studies to assess beer quality and stability

The work reported in this thesis aimed at applying the methodology known as metabonomics to the detailed study of a particular type of beer and its quality control, with basis on the use of multivariate analysis (MVA) to extract meaningful information from given analytical data sets. In Chapter 1, a detailed description of beer is given considering the brewing process, main characteristics and typical composition of beer, beer stability and the commonly used analytical techniques for beer analysis. The fundamentals of the analytical methods employed here, namely nuclear magnetic resonance (NMR) spectroscopy, gas-chromatography-mass spectrometry (GC-MS) and mid-infrared (MIR) spectroscopy, together with the description of the metabonomics methodology are described shortly in Chapter 2. In Chapter 3, the application of high resolution NMR to characterize the chemical composition of a lager beer is described. The 1H NMR spectrum obtained by direct analysis of beer show a high degree of complexity, confirming the great potential of NMR spectroscopy for the detection of a wide variety of families of compounds, in a single run. Spectral assignment was carried out by 2D NMR, resulting in the identification of about 40 compounds, including alcohols, amino acids, organic acids, nucleosides and sugars. In a second part of Chapter 3, the compositional variability of beer was assessed. For that purpose, metabonomics was applied to 1H NMR data (NMR/MVA) to evaluate beer variability between beers from the same brand (lager), produced nationally but differing in brewing site and date of production. Differences between brewing sites and/or dates were observed, reflecting compositional differences related to particular processing steps, including mashing, fermentation and maturation. Chapter 4 describes the quantification of organic acids in beer by NMR, using different quantitative methods: direct integration of NMR signals (vs. internal reference or vs. an external electronic reference, ERETIC method) and by quantitative statistical methods (using the partial least squares (PLS) regression) were developed and compared. PLS1 regression models were built using different quantitative methods as reference: capillary electrophoresis with direct and indirect detection and enzymatic essays. It was found that NMR integration results generally agree with those obtained by the best performance PLS models, although some overestimation for malic and pyruvic acids and an apparent underestimation for citric acid were observed. Finally, Chapter 5 describes metabonomic studies performed to better understand the forced aging (18 days, at 45 ºC) beer process. The aging process of lager beer was followed by i) NMR, ii) GC-MS, and iii) MIR spectroscopy. MVA methods of each analytical data set revealed clear separation between different aging days for both NMR and GC-MS data, enabling the identification of compounds closely related with the aging process: 5-hydroxymethylfurfural (5-HMF), organic acids, γ-amino butyric acid (GABA), proline and the ratio linear/branched dextrins (NMR domain) and 5-HMF, furfural, diethyl succinate and phenylacetaldehyde (known aging markers) and, for the first time, 2,3-dihydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one xii (DDMP) and maltoxazine (by GC-MS domain). For MIR/MVA, no aging trend could be measured, the results reflecting the need of further experimental optimizations. Data correlation between NMR and GC-MS data was performed by outer product analysis (OPA) and statistical heterospectroscopy (SHY) methodologies, enabling the identification of further compounds (11 compounds, 5 of each are still unassigned) highly related with the aging process. Data correlation between sensory characteristics and NMR and GC-MS was also assessed through PLS1 regression models using the sensory response as reference. The results obtained showed good relationships between analytical data response and sensory response, particularly for the aromatic region of the NMR spectra and for GC-MS data (r > 0.89). However, the prediction power of all built PLS1 regression models was relatively low, possibly reflecting the low number of samples/tasters employed, an aspect to improve in future studies.

The impact of biofuels for road traffic on air quality : a modelling approach

The selection of the energy source to power the transport sector is one of the main current concerns, not only relative with the energy paradigm but also due to the strong influence of road traffic in urban areas, which highly affects human exposure to air pollutants and human health and quality of life. Due to current important technical limitations of advanced energy sources for transportation purposes, biofuels are seen as an alternative way to power the world’s motor vehicles in a near-future, helping to reduce GHG emissions while at the same time stimulating rural development. Motivated by European strategies, Portugal, has been betting on biofuels to meet the Directive 2009/28/CE goals for road transports using biofuels, especially biodiesel, even though, there is unawareness regarding its impacts on air quality. In this sense, this work intends to clarify this issue by trying to answer the following question: can biodiesel use contribute to a better air quality over Portugal, particularly over urban areas? The first step of this work consisted on the characterization of the national biodiesel supply chain, which allows verifying that the biodiesel chain has problems of sustainability as it depends on raw materials importation, therefore not contributing to reduce the external energy dependence. Next, atmospheric pollutant emissions and air quality impacts associated to the biodiesel use on road transports were assessed, over Portugal and in particular over the Porto urban area, making use of the WRF-EURAD mesoscale numerical modelling system. For that, two emission scenarios were defined: a reference situation without biodiesel use and a scenario reflecting the use of a B20 fuel. Through the comparison of both scenarios, it was verified that the use of B20 fuels helps in controlling air pollution, promoting reductions on PM10, PM2.5, CO and total NMVOC concentrations. It was also verified that NO2 concentrations decrease over the mainland Portugal, but increase in the Porto urban area, as well as formaldehyde, acetaldehyde and acrolein emissions in the both case studies. However, the use of pure diesel is more injurious for human health due to its dominant VOC which have higher chronic hazard quotients and hazard indices when compared to B20.

RadiaLE: A framework for designing and assessing link quality estimators in wireless sensor networks

Stringent cost and energy constraints impose the use of low-cost and low-power radio transceivers in large-scale wireless sensor networks (WSNs). This fact, together with the harsh characteristics of the physical environment, requires a rigorous WSN design. Mechanisms for WSN deployment and topology control, MAC and routing, resource and mobility management, greatly depend on reliable link quality estimators (LQEs). This paper describes the RadiaLE framework, which enables the experimental assessment, design and optimization of LQEs. RadiaLE comprises (i) the hardware components of the WSN testbed and (ii) a software tool for setting-up and controlling the experiments, automating link measurements gathering through packets-statistics collection, and analyzing the collected data, allowing for LQEs evaluation. We also propose a methodology that allows (i) to properly set different types of links and different types of traffic, (ii) to collect rich link measurements, and (iii) to validate LQEs using a holistic and unified approach. To demonstrate the validity and usefulness of RadiaLE, we present two case studies: the characterization of low-power links and a comparison between six representative LQEs. We also extend the second study for evaluating the accuracy of the TOSSIM 2 channel model.

Radio link quality estimation in wireless sensor networks: a survey

Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

Particulate matter flux deposition in the vicinity of a coal-fired power plant

This paper describes the methodology adopted to assess local air quality impact in the vicinity of a coal power plant located in the south of Portugal. Two sampling areas were selected to assess the deposition flux of dust fallout and its potential spatial heterogeneity. The sampling area was divided into two subareas: the inner, with higher sampling density and urban and suburban characteristics, inside a 6-km circle centered on the stacks, and an outer subarea, mainly rural, with lower sampling density within a radius of 20 km. Particulate matter deposition was studied in the vicinity of the coal fired power plant during three seasonal sampling campaigns. For the first one, the average annual flux of dust fallout was 22.51 g/(m2 yr), ranging from 4.20 to 65.94 g/(m2 yr); for the second one was 9.47 g/(m2 yr), ranging from 0.78 to 32.72 g/(m2 yr) and for the last one was 38.42 g/(m2 yr), ranging from 1.41 to 117.48 g/(m2 yr). The fallout during the second campaign turned out to be much lower than for others. This was in part due to meteorological local patterns but mostly due to the fact that the power plant was not working at full power during the second sampling campaign.155

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.