The Impact of Land Use and Land Cover Changes on the Nkula Dam in the Middle Shire River Catchment, Malawi

Land use and land cover changes over a 26-year period for the middle Shire River catchment, Malawi, in southern Africa, were assessed using geographic information systems (GIS) and remote sensing techniques. The catchment area under study was divided into two sections, western and eastern sides of the Shire River. High rate of deforestation averaging 4.3% per annum was observed and more pronounced in the western side of the river. Rapid population growth and increase in gross domestic product (GDP) are identified as the major drivers of deforestation and forest degradation due to clearing of vast fields for agriculture, land expansion for urban settlement, and cutting down of trees for wood fuel energy. Deforestation in the middle Shire River catchment has resulted into increased soil loss through erosion causing huge accumulation of sediment at the Nkula B Hydroelectric Power Dam downstream and, consequently, causing serious problems with generation of hydroelectricity. Frequent droughts and floods in the area have drastically affected crop production forcing people into cutting down of trees for charcoal as a livelihood strategy. Combined techniques such as GIS, remote sensing, and socioeconomic factors used in this study could be applied in other places where similar challenges occur

Fusion of Landsat-8/OLI and GOCI Data for Hourly Mapping of Suspended Particulate Matter at High Spatial Resolution: A Case Study in the Yangtze (Changjiang) Estuary

Suspended particulate matter (SPM) concentrations ([SPM]) in the Yangtze estuary, which has third-order bifurcations and four outlets, exhibit large spatial and temporal variations. Studying the characteristics of these variations in [SPM] is important for understanding sediment transport and pollutant diffusion in the estuary as well as for the construction of port and estuarine engineering structures. The 1-h revisit frequency of the Geostationary Ocean Color Imager (GOCI) sensor and the 30-m spatial resolution of the Landsat 8 Operational Land Imager (L8/OLI) provide a new opportunity to study the large spatial and temporal variations in the [SPM] in the Yangtze estuary. In this study, [SPM] images with a temporal resolution of 1 h and a spatial resolution of 30 m are generated through the product-level fusion of [SPM] data derived from L8/OLI and GOCI images using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The results show that the details and accuracy of the spatial and temporal variations are maintained well in the [SPM] images that are predicted based on the fused images. Compared to the [SPM] observations at fixed field stations, the mean relative error (MRE) of the predicted SPM is 17.7%, which is lower than that of the GOCI-derived [SPM] (27.5%). In addition, thanks to the derived high-resolution [SPM] with high spatiotemporal dynamic changes, both natural phenomena (dynamic variation of the maximum turbid zone) and human engineering changes leading to the dynamic variability of SPM in the channel are observed

Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home

High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in household appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable β, is used to model the stochastic energy consumption patterns for various household appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical household appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for household appliance applications. © 2010-2012 IEEE

Design of a hard real-time multi-core testbed for energy measurement

This paper presents a systematic methodology for designing a hard real-time multi-core testbed to validate and benchmark various rate monotonic scheduling (RMS)-based task allocation and scheduling schemes in energy consumption. The hard real-time multi-core testbed comprises Intel Core Duo T2500 processor with dynamic voltage scaling (DVS) capability and runs the Linux Fedora 8 operating system supporting soft real-time scheduling. POSIX threads API and Linux FIFO scheduling policy are utilized to facilitate the design and Dhrystone-based tasks are generated to verify the design. A LabView-based DAQ system is designed to measure the energy consumption of CPU and system board of the testbed. A case study of task allocation and scheduling algorithms is also presented that aim to optimize the schedule feasibility and energy consumed by the processor and memory module in the multi-core platform. The experience from the implementation is summarized to serve as potential guidelines for other researchers and practitioners. © 2011 Elsevier Ltd. All rights reserved

Reliability-driven energy-efficient task scheduling for multiprocessor real-time systems

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences. © 2006 IEEE

An interconnect reliability-driven routing technique for electromigration failure avoidance

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density- induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents. © 2012 IEEE

Remote Sensing Observations of a Coastal Water Environment Based on Neural Network and Spatiotemporal Fusion Technology: A Case Study of Hangzhou Bay

The coastal environment is characterized by high, multi-scale dynamics and the corresponding observations from a single remote sensing sensor are still facing challenges in achieving both high temporal and spatial resolution. This study proposed a spatiotemporal fusion model for coastal environments, which could fully enhance the efficiency of remote sensing data use and overcome the shortcomings of traditional spatiotemporal models that are insensitive to small-scale disturbances. The Enhanced Deep Super-Resolution Network (EDSR) was used to reconstruct spatial features in the lower spatial resolution GOCI-II data. The spatial features obtained instead of GOCI-II data were fed into the spatiotemporal fusion model, which enabled the fusion data to achieve an hour-by-hour observation of the water color and morphology information changes at 30 m resolution, including the changes in the spatial and temporal distributions of suspended particulate matter (SPM), the characterization of the vortex street caused by the bridge piers, the inundation process of the tidal flats, and coastline changes. In addition, this study analyzed the various factors affecting fusion accuracy, including spectral difference, errors in both temporal difference and location distance, and the structure of the EDSR model on the fusion accuracy. It is demonstrated that the location distance error and the spectral difference have the most significant impact on the fusion data, which may lead to the introduction of some ambiguous or erroneous spatial features

Adaptive fault-tolerant task scheduling for real-time energy harvesting systems

Fault tolerance and energy have become important design issues in multiprocessor system-on-chips (SoCs) with the technology scaling and the proliferation of battery-powered multiprocessor SoCs. This paper proposed an energy-efficient fault tolerance task allocation scheme for multiprocessor SoCs in real-time energy harvesting systems. The proposed fault-tolerance scheme is based on the principle of the primiary/backup task scheduling, and can tolerate at most one single transient fault. Extensive simulated experiment shows that the proposed scheme can save up to 30% energy consumption and reduce the miss ratio to about 8% in the presence of faults. © 2012 World Scientific Publishing Company