Compensation of Torque Ripple in High Performance BLDC Motor Drives

Brushless DC motor drives (BLDC) are finding expanded use in high performance applications where torque smoothness is essential. The nature of the square-wave current excitation waveforms in BLDC motor drives permits some important system simplifications compared to sinusoidal permanent magnet AC (PMAC) machines. However, it is the simplicity of the BLDC motor drive that is responsible for causing an additional source of ripple torque commonly known as commutation torque to develop. In this paper, a compensation technique for reducing the commutation torque ripple is proposed. With the experimental results, the proposed method demonstrates the effectiveness for a control system using the BLDC motors that requires high speed and accuracy

Many-to-One Communication Protocol for Wireless Sensor Networks

This paper proposes a novel communication protocol, called Many-to-One Sensors-to-Sink (MOSS), tailored to wireless sensor networks (WSNs). It exploits the unique sensors-to-sink traffic pattern to realize low-overhead medium access and low- latency sensors-to-sink routing paths. In conventional schedule-based MAC protocols such as S-MAC, sensor nodes in the proximity of the event generate reports simultaneously, causing unreliable and unpredictable performance during a brief but critical period of time when an event of interest occurs. MOSS is based on time division multiple access (TDMA) that avoids energy waste due to collisions, idle listening and overhearing and avoids unreliable behavior mentioned above. A small test-bed consisting of 12 TelosB motes as well as extensive simulation study based on ns-2 have shown that MOSS reduces the sensor-to-sink latency by as much as 50.5% while consuming only 12.8 ∼ 19.2% of energy compared to conventional TDMA algorithm

Dynamic Voltage Scaling Techniques for Power Efficient Video Decoding

This paper presents a comparison of power-aware video decoding techniques that utilize dynamic voltage scaling (DVS). These techniques reduce the power consumption of a processor by exploiting high frame variability within a video stream. This is done through scaling of the voltage and frequency of the processor during the video decoding process. However, DVS causes frame deadline misses due to inaccuracies in decoding time predictions and granularity of processor settings used. Four techniques were simulated and compared in terms of power consumption, accuracy, and deadline misses. In addition, this paper proposes the frame-data computation aware (FDCA) technique, which is a useful power-saving technique not only for stored video but also for real-time video applications. The FDCA method is compared with the GOP, Direct, and Dynamic methods, which tend to be more suited for stored video applications. The simulation results indicated that the Dynamic per-frame technique, where the decoding time prediction adapts to the particular video being decoded, provides the most power saving with performance comparable to the ideal case. On the other hand, the FDCA method consumes more power than the Dynamic method but can be used for stored video and real-time time video scenarios without the need for any preprocessing. Our findings also indicate that, in general, DVS improves power savings, but the number of deadline misses also increase as the number of available processor settings increases. More importantly, most of these deadline misses are within 10–20% of the playout interval and thus have minimal affect on video quality. However, video clips with high variability in frame complexities combined with inaccurate decoding time predictions may degrade the video quality. Finally, our results show that a processor with 13 voltage/frequency settings is sufficient to achieve near maximum performance with the experimental environment and the video workloads we have used

Selective Advance Reservations Based on Host Movement Detection and Resource-Aware Handoff

This paper proposes a new mechanism, which addresses the excessive advance reservation requirements of QoS guarantee methods for mobile Internet. To save resources for excessive advance reservations, the proposed mechanism employs a movement detection scheme for a mobile host (MH) using link-layer functionalities. With the movement detection scheme, advance reservations can be established at only where a MH is likely to visit soon. Another novel feature of our mechanism is resource-aware handoff direction scheme that allows a MH to choose its next BS according to not only the link-layer signal strength, but also the available amount of resources in the reachable base stations (BSs). It considerably decreases a probability that QoS is disrupted due to the failure in advance reservation request. Also, the proposed mechanism requires fewer functional and structural changes to the current Internet components and protocols since all the enhanced features are integrated only into leaf BSs and MHs. It does not suffer from the problems of the conventional approaches based on Mobile IP and RSVP Tunnel, such as non-optimal routing path and signalling overhead. Our experiment results show that the proposed mechanism successfully eliminates the overhead for useless advance reservations while guaranteeing seamless QoS for MHs. The performance comparison demonstrates that our mechanism slightly outperforms the conventional approaches while requiring fewer modifications and additions to the existing Internet architecture. This performance advantage of the proposed mechanism becomes noticeable when the network is congested and the mobility of a host is high. Copyright © 2006 John Wiley & Sons, Ltd

Selective Advance Reservations Based on Host Movement Detection and Resource-Aware Handoff

This paper proposes a new mechanism, which addresses the excessive advance reservation requirements of QoS guarantee methods for mobile Internet. To save resources for excessive advance reservations, the proposed mechanism employs a movement detection scheme for a mobile host (MH) using link-layer functionalities. With the movement detection scheme, advance reservations can be established at only where a MH is likely to visit soon. Another novel feature of our mechanism is resource-aware handoff direction scheme that allows a MH to choose its next BS according to not only the link-layer signal strength, but also the available amount of resources in the reachable base stations (BSs). It considerably decreases a probability that QoS is disrupted due to the failure in advance reservation request. Also, the proposed mechanism requires fewer functional and structural changes to the current Internet components and protocols since all the enhanced features are integrated only into leaf BSs and MHs. It does not suffer from the problems of the conventional approaches based on Mobile IP and RSVP Tunnel, such as non-optimal routing path and signalling overhead. Our experiment results show that the proposed mechanism successfully eliminates the overhead for useless advance reservations while guaranteeing seamless QoS for MHs. The performance comparison demonstrates that our mechanism slightly outperforms the conventional approaches while requiring fewer modifications and additions to the existing Internet architecture. This performance advantage of the proposed mechanism becomes noticeable when the network is congested and the mobility of a host is high. Copyright © 2006 John Wiley & Sons, Ltd

Dynamic Voltage Scaling Techniques for Power Efficient Video Decoding

This paper presents a comparison of power-aware video decoding techniques that utilize dynamic voltage scaling (DVS). These techniques reduce the power consumption of a processor by exploiting high frame variability within a video stream. This is done through scaling of the voltage and frequency of the processor during the video decoding process. However, DVS causes frame deadline misses due to inaccuracies in decoding time predictions and granularity of processor settings used. Four techniques were simulated and compared in terms of power consumption, accuracy, and deadline misses. In addition, this paper proposes the frame-data computation aware (FDCA) technique, which is a useful power-saving technique not only for stored video but also for real-time video applications. The FDCA method is compared with the GOP, Direct, and Dynamic methods, which tend to be more suited for stored video applications. The simulation results indicated that the Dynamic per-frame technique, where the decoding time prediction adapts to the particular video being decoded, provides the most power saving with performance comparable to the ideal case. On the other hand, the FDCA method consumes more power than the Dynamic method but can be used for stored video and real-time time video scenarios without the need for any preprocessing. Our findings also indicate that, in general, DVS improves power savings, but the number of deadline misses also increase as the number of available processor settings increases. More importantly, most of these deadline misses are within 10–20% of the playout interval and thus have minimal affect on video quality. However, video clips with high variability in frame complexities combined with inaccurate decoding time predictions may degrade the video quality. Finally, our results show that a processor with 13 voltage/frequency settings is sufficient to achieve near maximum performance with the experimental environment and the video workloads we have used

Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms