Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding

Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 × 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip).     

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (T_m) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × T_m parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R² of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × T_m correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R² = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R² = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m^? 2 month^? 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.     

Laser beam welded structures for a regional aircraft: weight,cost and carbon footprint savings

Laser beam welded structures offer great opportunities for the lightweight design of fuselage structures in order to reduce structural weight for increased fuel efficiency. Our main objective is to validate and demonstrate that laser beam welding (LBW) technology provides the best opportunities in terms of weight reduction, production time and energy consumption for manufacturing aircraft components. To this end, a comparison in terms of energy, process time, cost and carbon footprint is assessed against the ‘conventional’ manufacturing process of riveting, to prove that LBW is actually an environmental friendly process. Manufacturing of a four-stringer stiffened flat subscale component was the case of the present work that was called in the Clean Sky Eco-Design Airframe (EDA) project as the B1 demonstrator (742 mm × 384 mm). The LBW process has been broken down into several sub-processes and activities according to the Activity Based Costing (ABC) methodology and the weight reduction, production time and energy consumption results were compared against the respective of the riveting process. It was proved that for the specific subscale LBW component, it consumes half the energy and can be processed in less than half the time needed (in serial processing of the component) with riveting. Manufacturing of the component with the LBW process (door to door approach) is more environmentally friendly, since it produces 53% less CO_2e emissions than the respective riveted process. This is a clear advantage to this manufacturing process in order to assure a sustainable life cycle of the final product.     

Gröbner Bases over Galois Rings with an Application to Decoding Alternant Codes

We develop a theory of Gröbner bases over Galois rings, following the usual formulation for Gröbner bases over finite fields. Our treatment includes a division algorithm, a characterization of Gröbner bases, and an extension of Buchberger’s algorithm. One application is towards the problem of decoding alternant codes over Galois rings. To this end we consider the module M =  {(a, b) :aS ≡ b  mod x^r} of all solutions to the so-called key equation for alternant codes, where S is a syndrome polynomial. In decoding, a particular solution (Σ, Ω)  ∈ M is sought satisfying certain conditions, and such a solution can be found in a Gröbner basis of M. Applying techniques introduced in the first part of this paper, we give an algorithm which returns the required solution.     

Reaction mechanism and free energy profile for acylation of Candida Antarctica lipase B with methylcaprylate and acetylcholine: Density functional theory calculations

Candida Antarctica lipase B (CALB), a specific enzyme to catalyze the hydrolysis of esters, can be a good candidate for acetylcholine (ACh) hydrolysis instead of acetylcholinesterase. The catalytic mechanism of the CALB acylation, as the first stage in the hydrolysis reaction, with ACh and methylcaprylate (MEC) has been examined by using density functional theory technique. The significant emphasis of this article is on the free energy barriers for the acylation step of hydrolysis reactions. Computed free energy barriers of the first step are 9.2 and 15.9 kcal mol⁻¹, but for the second step are 7.9 and 11.6 kcal mol⁻¹ for MEC and ACh respectively. Activation free energies are in the comparable and acceptable range and imply both of two reactions are theoretically possible. The stability role of the adjacent amino acids was examined by using two applied tools. It is exposed that the oxyanion hole residues decrease energy barriers by stabilizing the transition state structures.     

Transition-aware DVS algorithm for real-time systems using tree structure analysis

Dynamic voltage scaling (DVS) is a key technique for embedded real-time systems to reduce energy consumption by lowering the supply voltage and operating frequency. Many existing DVS algorithms have to generate the canonical schedules or estimate the lengths of slack time in advance for generating the voltage scaling decisions. Therefore, these methods have to compute the schedules with exponential time complexities in general. In this paper, we consider a set of jitter-controlled, independent, periodic, hard real-time tasks scheduled according to preemptive pinwheel model. Our approach constructs a tree structure corresponding to a schedule and maintains the data structure at each early-completion point. Our approach consists of off-line and on-line algorithms which consider the effects of transition time and energy. The off-line and on-line algorithm takes O(k + n log n) and O(k + (p_max/p_min)) time complexity, respectively, where n, k, p_max and p_min denotes the number of tasks, jobs, longest and shortest task period, respectively. Experimental results show that the proposed approach is effective in reducing computational complexity, transition time and energy overhead.     

Objective quality definition of scalable video coding and its application for optimal streaming of FGS-coded videos

With the proliferation of video contents widely distributed over the Internet and the progress of video coding (e.g., H.264/AVC) and transmission technologies, more challenges need to overcome in order to meet the requirements of all users with diverse terminals. Video streaming over IP and wireless becomes a popular issue since the new century.However, there is little work concerning the quantitative analysis on the objective quality of streaming videos. Thus a strict definition of the objective quality and quality variation of scalable video coding (SVC) is required, in order to efficiently transmit video contents over Internet and wireless and reach an attainable subjective quality perception for end-users. Since FGS (fine granularity scalability) video is coded in bit planes, its enhancing layer can be truncated arbitrarily, as a case study of scalable video coding, an objective quality definition for FGS-coded video is introduced in this paper, based on MSE (mean square error) and PSNR (peak signal-to-noise ratio). This definition can also be generalized to any layered scalable coding videos, such as the traditional layered videos in BL + ELs (base layer + enhancing layers) formats or H.264/AVC in BL + CGS (coarse granularity scalability) + FGS structures, and it can be applied to design optimal algorithms for video streaming. Furthermore, It can also be taken as a measure to assess the subjective quality of streaming videos, by incorporating user preferences and terminal capacities.According to this definition, a quality optimal problem of scene in video segments is formulated and solved using the state transfer graph and dynamic programming. The optimal transmission policy is also obtained and compared with a real-time transmission algorithm. Different aggregation levels (segmentation granularity) of video segments for optimal transmission are also examined by experimental data. Simulation results validate our observations.     

Effects of captions and time-compressed video on learner performance and satisfaction

Digital video is becoming increasingly popular in higher education with faculty digitally recording and broadcasting lectures for students to learn-on-demand, such as iTunes University or YouTube. Students have discovered accelerated playback features in popular computer software and use it to reduce the amount of time watching video-enhanced instruction. In the current study, 147 undergraduate students were randomly assigned to one of six video treatments based on a 3 Video Speed (1.0 = Normal vs. 1.25 = Fast vs. 1.50 = Very Fast) × 2 Captions (Captions Present vs. Captions Absent) × 2 Trial (Trial 1 vs. Trial 2) design. Results show no significant difference on learner performance across treatments based on Video Speed. Captions were found to have a significant negative effect on learner performance. A significant difference was found on learner satisfaction in favor of a normal Video Speed. The findings suggest that learners might be able to accelerate Video Speeds up to 1.5 times the normal speed, but are generally less satisfied with the learning experience.     

Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function

There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent (λE) and sensible heat flux (H).In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / (λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies (R² = 0.88; RMSE = 0.18 (22%)).To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties.     

Interpretation and topographic compensation of conifer canopy self-shadowing

The self-shadowing of conifer canopies results from the size and arrangement of trees within a stand and is a first-order term controlling radiance from forested terrain at common pixel scales of tens of meters. Although self-shadowing is a useful attribute for forest remote-sensing classification, compensation for the topographic effects of self-shadowing has proven problematic. This study used airborne canopy LiDAR measurements of 80 Pacific Northwest, USA conifer stands ranging in development stage from pre-canopy closure to old-growth in order to model canopy self-shadowing for four solar zenith angles (SZA). The shadow data were compared to physical measurements used to characterize forest stands, and were also used to test and improve terrain compensation models for remotely sensed images of forested terrain. Canopy self-shadowing on flat terrain strongly correlates with the canopy's geometric complexity as measured by the rumple index (canopy surface area/ground surface area) (R² = 0.94–0.87 depending on SZA), but is less correlated with other stand measurements: 95th percentile canopy height (R² = 0.68), mean diameter at breast height (dbh) (R² = 0.65), basal area ha^? 1 (R² = 0.18), and canopy stem count ha^? 1 (R² = 0.18). The results in this paper support interpretation of self-shadowing as a function of canopy complexity, which is an important ecological characteristic in its own right. Modeling of canopy self-shadowing was used to assess the accuracy of the Sun-Canopy-Sensor (SCS) topographic correction, and to develop a new empirical Adaptive Shade Compensation (ASC) topographic compensation model. ASC used measured shadow (as an estimate of canopy complexity) and the SCS term (to describe the illumination geometry) as independent variables in multiple regressions to determine the topographic correction. The ASC model provided more accurate radiance corrections with limited variation in results across the full range of canopy complexities and incidence angles.     

Investigating data preprocessing methods for circuit complexity models

Preprocessing the data is an important step while creating neural network (NN) applications because this step usually has a significant effect on the prediction performance of the model. This paper compares different data processing strategies for NNs for prediction of Boolean function complexity (BFC). We compare NNs’ predictive capabilities with (1) no preprocessing (2) scaling the values in different curves based on every curve’s own peak and then normalizing to [0, 1] range (3) applying z-score to values in all curves and then normalizing to [0, 1] range, and (4) logarithmically scaling all curves and then normalizing to [0, 1] range. The efficiency of these methods was measured by comparing RMS errors in NN-made BFC predictions for numerous ISCAS benchmark circuits. Logarithmic preprocessing method resulted in the best prediction statistics as compared to other techniques.     

A magnetoelectric energy harvester and management circuit for wireless sensor network

This paper presents an electromagnetic energy harvesting scheme by using a composite magnetoelectric (ME) transducer and a power management circuit. In the transducer, the vibrating wave induced from the magnetostrictive Terfenol-D plate in dynamic magnetic field is converged by using an ultrasonic horn. Consequently more vibrating energy can be converted into electricity by the piezoelectric element. A switching capacitor network for storing electricity is developed. The output of the transducer charges the storage capacitors in parallel until the voltage across the capacitors arrives at the threshold, and then the capacitors are automatically switched to being in series. More capacitors can be employed in the capacitor network to further raise the output voltage in discharging. For the weak magnetic field environment, an active magnetic generator and a magnetic coil antenna under ground are used for producing an ac magnetic field of 0.2–1 Oe at a distance of 25–50 m. In combination with the supply management circuit, the electromagnetic energy harvester with a rather weak power output (about 20 μW) under an ac magnetic field of 1 Oe can supply power for wireless sensor nodes with power consumption of 75 mW at a duration of 620 ms.     

An RL-based scheduling algorithm for video traffic in high-rate wireless personal area networks

The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, video streams are deemed to be a dominant traffic type, and require quality of service (QoS) support. However, in the current IEEE 802.15.3 standard for MAC (media access control) of high-rate WPANs, the implementation details of some key issues such as scheduling and QoS provisioning have not been addressed. In this paper, we first propose a Markov decision process (MDP) model for optimal scheduling for video flows in high-rate WPANs. Using this model, we also propose a scheduler that incorporates compact state space representation, function approximation, and reinforcement learning (RL). Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT, EDD + SRPT, and PAP scheduling algorithms in terms of a lower decoding failure rate.     

Energy-efficient bi-objective single-machine scheduling with power-down mechanism

This paper considers a single-machine scheduling problem with power-down mechanism to minimize both total energy consumption and maximum tardiness. The aim is to find an optimal processing sequence of jobs and determine if the machine should be executed a power-down operation between two consecutive jobs. To formulate the problem, a mixed-integer linear programming (MILP) model is developed. Then a basic ε − constraint method is proposed to obtain the complete Pareto front of the problem. Considering the particularity of the problem, we also develop local search, preprocessing technique and valid inequalities to strengthen the basic ε − constraint method. Finally, to obtain approximate Pareto fronts for large-size problems, we utilize the method of cluster analysis to divide the jobs into several sorted clusters according to their release times and due dates. Any job in a preceding cluster must be processed before all jobs in a subsequent cluster. Thus, the solution space is reduced significantly. Computational experiments on benchmark and randomly generated instances demonstrate the effectiveness of the proposed exact and approximation approaches.     

Knowledge sharing and social media: Altruism,perceived online attachment motivation,and perceived online relationship commitment

Social media, such as Facebook and Twitter, have become extremely popular. Facebook, for example, has more than a billion registered users and thousands of millions of units of information are shared every day, including short phrases, articles, photos, and audio and video clips. However, only a tiny proportion of these sharing units trigger any type of knowledge exchange that is ultimately beneficial to the users. This study draws on the theory of belonging and the intrinsic motivation of altruism to explore the factors contributing to knowledge sharing behavior. Using a survey of 299 high school students applying for university after the release of the public examination results, we find that perceived online attachment motivation (β = 0.31, p < 0.001) and perceived online relationship commitment (β = 0.49, p < 0.001) have positive, direct, and significant effects on online knowledge sharing (R² 0.568). Moreover, when introduced into the model, altruism has a direct and significant effect on online knowledge sharing (β = 0.46, p < 0.001) and the total variance explained by the extended model increases to 64.9%. The implications of the findings are discussed.     

Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink

Uneven energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Such unbalanced energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of prolonging network lifetime in large-scale wireless sensor networks where a mobile sink gathers data periodically along the predefined path and each sensor node uploads its data to the mobile sink over a multi-hop communication path. By using greedy policy and dynamic programming, we propose a heuristic topology control algorithm with time complexity O(n(m + n log n)), where n and m are the number of nodes and edges in the network, respectively, and further discuss how to refine our algorithm to satisfy practical requirements such as distributed computing and transmission timeliness. Theoretical analysis and experimental results show that our algorithm is superior to several earlier algorithms for extending network lifetime.     

Energy efficiency analysis of high speed triple-play services in next-generation PON deployments

In this paper, the energy consumption of high speed access services up to 1 Gb/s per customer is estimated for different passive optical network (PON) technologies. While other studies on PON power consumption typically assume a fixed split ratio, we also consider a greenfield approach, where the split ratio can be optimized for each technology, taking full advantage of its capacity and reach. The split ratio optimization takes into account Quality of Service (QoS) in terms of bandwidth availability and packet loss for triple-play services (voice, television and Internet). This paper includes an in-depth discussion of our split ratio dimensioning approach and our power consumption model for an optical access network in a major city. The obtained results show that statistical gain provided by dynamic bandwidth allocation as well as power splitting ratio optimization in PONs are key factors for achieving energy efficiency. For access rates up to 900 Mb/s, XG-PON1 turns out to be the most energy efficient option. For higher access rates up to 1 Gb/s, the optimal technology depends on split ratio restrictions. If an existing optical distribution network (ODN) with split ratio 1:64 is used, XG-PON1 remains the most energy efficient technology. If higher split ratios up to 1:256 can be achieved, TWDM PON becomes the most energy efficient solution for access rates up to 1 Gb/s.     

Design,fabrication, and testing of MEMS solid propellant thruster array chip on glass wafer

This paper presents the internal ballistic design, fabrication procedure, and performance evaluation of a micro-electro mechanical systems (MEMS) solid propellant thruster array chip. The internal ballistic design was carried out to predict the performance of the thruster. Two different ignition models were used. The numerical results gave a maximum thrust of 3840 mN, and a total impulse of 0.42 mNs at the local ignition model. A photosensitive glass wafer only was used as the bare material for the thruster. The stability of the micro-igniter was improved by using a glass membrane with a thickness of tens of microns. The average thickness of the membrane was 35 μm. The proposed micro-igniter had a level of power consumption appropriate to ignite the solid propellant. The thermal, electrical, and mechanical characteristics of the fabricated micro-igniter were measured. The solid propellant was loaded into the propellant chamber without resort to a special technique due to the high structural stability of the glass membrane. An MEMS solid propellant thruster (MSPT) array was fabricated through anisotropic etching of photosensitive glass. An ignition control system was developed to control the ignition sequence. Ignition and combustion tests of the fully assembly MEMS thruster were performed successfully. The minimum ignition delay was 27.5 ms with an ignition energy of 19.3 mJ. The average of the measured maximum thrust and total impulse were 3619 mN and 0.381 mNs, respectively.     

Algorithmic complexity bounds on future prediction errors

We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution μ by the algorithmic complexity of μ. Here we assume that we are at a time t > 1 and have already observed x = x₁ ⋯ x_t. We bound the future prediction performance on x_t+1x_t+2 ⋯ by a new variant of algorithmic complexity of μ given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.