This paper empirically validates battery-less sensor activation via wireless energy transmission to release sensors from wires and batteries. To seamlessly extend the coverage and activate sensor nodes distributed in any indoor environment, we proposed multi-point wireless energy transmission with carrier shift diversity. In this scheme, multiple transmitters are employed to compensate path-loss attenuation and orthogonal frequencies are allocated to the multiple transmitters to avoid the destructive interference that occurs when the same frequency is used by all transmitters. In our previous works, the effectiveness of the proposed scheme was validated theoretically and also empirically by using just a spectrum analyzer to measure the received power. In this paper, we develop low-energy battery-less sensor nodes whose consumed power and required received power for activation are respectively 142µW and 400µW. In addition, we conduct indoor experiments in which the received power and activation of battery-less sensor node are simultaneously observed by using the developed battery-less sensor node and a spectrum analyzer. The results show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are, respectively, 84.4% and 83.7%, while the coverage of the proposed scheme is 100%. It can be concluded that the effectiveness of the proposed scheme can be verified by our experiments using real battery-less sensor nodes.

Out-of-order superscalar processors have high performance but consume a large amount of energy for dynamic instruction scheduling. We propose a front-end execution architecture (FXA) for improving the energy efficiency of out-of-order superscalar processors. FXA has two execution units: an out-of-order execution unit (OXU) and an in-order execution unit (IXU). The OXU is the execution core of a common out-of-order superscalar processor. In contrast, the IXU consists only of functional units and a bypass network only. The IXU is placed at the processor front end and executes instructions in order. The IXU functions as a filter for the OXU. Fetched instructions are first fed to the IXU, and the instructions are executed in order if they are ready to execute. The instructions executed in the IXU are removed from the instruction pipeline and are not executed in the OXU. The IXU does not include dynamic scheduling logic, and thus its energy consumption is low. Evaluation results show that FXA can execute more than 50% of the instructions by using IXU, thereby making it possible to shrink the energy-consuming OXU without incurring performance degradation. As a result, FXA achieves both high performance and low energy consumption. We evaluated FXA and compared it with conventional out-of-order/in-order superscalar processors after ARM big.LITTLE architecture. The results show that FXA achieves performance improvements of 7.4% on geometric mean in SPECCPU INT 2006 benchmark suite relative to a conventional superscalar processor (big), while reducing the energy consumption by 17% in the entire processor. The performance/energy ratio (the inverse of the energy-delay product) of FXA is 25% higher than that of a conventional superscalar processor (big) and 27% higher than that of a conventional in-order superscalar processor (LITTLE).

In the field of education such as elementary and middle schools, teachers want to take care of schoolchildren during physical trainings and after-school club activities. On the other hand, in the field of sports such as professional and national-level sports, physical or technical trainers want to manage the health, physical and physiological conditions of athletes during exercise trainings in the grounds. In this way, it is required to monitor vital signs for persons during exercises, however, there are several technical problems to be solved in its realization. In this paper, we present the importance and necessity of vital monitoring for persons during exercises, and to make it possible periodically, reliably and in real-time, we present the solutions which we have so far worked out and point out remaining technical challenges in terms of vital/physical sensing, wireless transmission and human interface.

Out-of-order superscalar processors rename register numbers to remove false dependencies between instructions. A renaming logic for register renaming is a high-cost module in a superscalar processor, and it consumes considerable energy. A renamed trace cache (RTC) was proposed for reducing the energy consumption of a renaming logic. An RTC caches and reuses renamed operands, and thus, register renaming can be omitted on RTC hits. However, conventional RTCs suffer from several performance, energy consumption, and hardware overhead problems. We propose a semi-global renamed trace cache (SGRTC) that caches only renamed operands that are short distance from producers outside traces, and solves the problems of conventional RTCs. Evaluation results show that SGRTC achieves 64% lower energy consumption for renaming with a 0.2% performance overhead as compared to a conventional processor.

This work presents the exact outage performance and throughput of two-way cognitive decode-and-forward relaying wireless sensor networks with realistic transceiver relay. The relay is a self-powered wireless node that harvests radio frequency energy from the transmitted signals. We consider four configurations of a network with formed by combining two bidirectional relaying protocols (multiple access broadcast protocol and time division broadcast protocol), and two power transfer policies (dual-source energy transfer and single-fixed-source energy transfer). Based on our analysis, we provide practical insights into the impact of transceiver hardware impairments on the network performance, such as the fundamental capacity ceiling of the network with various configurations that cannot be exceeded by increasing transmit power given a fixed transmission rate and the transceiver selection strategy for the network nodes that can optimize the implementation cost and performance tradeoff.

We propose a cooperative spectrum sensing scheme based on sub-Nyquist sampling in cognitive radios. Our main purpose is to understand the uncertainty caused by sub-Nyquist sampling and to present a sensing scheme that operates at low sampling rates. In order to alleviate the aliasing effect of sub-Nyquist sampling, we utilize cooperation among secondary users and the sparsity order of channel occupancy. The simulation results show that the proposed scheme can achieve reasonable sensing performance even at low sampling rates.

Past disasters, e.g., mega-quakes, tsunamis, have taught us that it is difficult to fully repair heavily damaged network systems in a short time. The only method for quickly restoring core communications is to start by fully utilizing the surviving network resources from different networks. However, as these networks might be built using different vendors' products (which are often incompatible with each other), the interconnection and utilization of these surviving resources are not straightforward. In this paper, we consider an all-optical multi-vendor interconnection method as an efficient reactive approach during disaster recovery. First, we introduce a disaster recovery scenario in which we use the multi-vendor interconnection approach. Second, we present two sub-problems and propose solutions: (1) network planning problem for multi-vendor interconnection-based emergency optical network construction and (2) interconnection problem for multi-vendor optical networks including both the data-plane and the control-and-management-plane. To enable the operation of multi-vendor systems, command translation middleware is developed for individual vendor-specific network control-and-management systems. Simulations are conducted to evaluate our proposal for sub-problem (1). The results reveal that multi-vendor interconnection can lead to minimum-cost network recovery. Additionally, an emergency optical network prototype is implemented on a two-vendor optical network test-bed to address sub-problem (2). Demonstrations of both the data-plane and the control-and-management-plane validate the feasibility of the multi-vendor interconnection approach in disaster recovery.

This paper presents a novel low-power decoder architecture for the (36420, 32778) binary LDPC code targeting energy-efficient NAND-flash-based mobile devices. The proposed energy-scalable decoding algorithm reduces the operating bit-width of decoding function units at the early-use stage where the channel condition is good enough to lower the precision of computation. Based on a flexible adder structure, the decoding energy of the proposed LDPC decoder can be reduced by freezing the unnecessary parts of hardware resources. A prototype 4KB LDPC decoder is designed in a 65nm CMOS technology, which achieves an average decoding throughput of 8.13Gb/s with 1.2M equivalent gates. The power consumption of the decoder ranges from 397mW to 563mW depending on operating conditions.

WLAN infrastructure has been deployed densely and extensively in the past few years. Since APs are always kept online, a dense WLAN will waste energy during idle hours. In this paper, we first state some principles for powering on/off APs in order to save energy. Then we design an energy saving mechanism correspondingly. The energy saving mechanism includes three processes: clustering APs, estimating user location, and powering on/off APs, which tries to choose appropriate APs being online according to user location information. Results of trace-driven simulation show that our mechanism could achieve about 42% energy conservation. More importantly, our mechanism can provide better network service for users than previous mechanisms which usually ignore user location information.

Although many 3D structures have been solved for proteins to date, functions of some proteins remain unknown. To predict protein functions, comparison of local structures of proteins with pre-defined model structures, whose functions have been elucidated, is widely performed. For the comparison, the root mean square deviation (RMSD) has been used as a conventional index. In this work, adaptive deviation was incorporated, along with Bregmann Divergence Regularized Machine, in order to detect analogous local structures with such model structures more effectively than the conventional index.

In many real-world classification problems, the class balance often changes between training and test datasets, due to sample selection bias or the non-stationarity of the environment. Naive classifier training under such changes of class balance systematically yields a biased solution. It is known that such a systematic bias can be corrected by weighted training according to the test class balance. However, the test class balance is often unknown in practice. In this paper, we consider a semi-supervised learning setup where labeled training samples and unlabeled test samples are available and propose a class balance estimator based on the energy distance. Through experiments, we demonstrate that the proposed method is computationally much more efficient than existing approaches, with comparable accuracy.

In this paper, we propose a low-power circuit-shared static flip-flop (CS2FF) for extremely low power digital VLSIs. The CS2FF consists of five static NORs and two inverters (INVs). The CS2FF utilizes a positive edge of a buffered clock signal, which is generated from a root clock, to take data into a master latch and a negative edge of the root clock to hold the data in a slave latch. The total number of transistors is only 24, which is the same as the conventional transmission-gate flip flop (TGFF) used in the most standard cell libraries. SPICE simulations in 0.18-µm standard CMOS process demonstrated that our proposed CS2FF achieved clock-to-Q delay of 18.3ns, setup time of 10.0ns, hold time of 5.5ns, and power dissipation of 9.7nW at 1-MHz clock frequency and 0.5-V power supply. The physical design area increased by 16% and power dissipation was reduced by 13% compared with those of conventional TGFF. Measurement results demonstrated that our proposed CS2FF can operate at 0.352V with extremely low energy of 5.93fJ.

Content centric networking (CCN) is a newly proposed futuristic Internet paradigm in which communication depends on the decoupling of content names from their locations. In CCN-based multihop wireless ad hoc networks, the participating nodes show dynamic topology, intermittent connectivity, channels fluctuation, and severe constraints such as limited battery power. In the case of traffic congestion, the affected nodes die early owing to the shortage of battery power. Consequently, all pending request entries are also destroyed, which further degrades the network performance as well as the node working lifetime. In this study, we have proposed a novel energy aware transmission scheme in which the forwarding mechanism is based on a node's residual energy. The proposed scheme is evaluated using official ndnSIM. This scheme enhances performance in terms of content retrieval time and total Interest transmission in the network.

In this paper, we analyze a cooperative communication network with multi energy-harvesting and decode-and-forward relays in which the best relay is selected based on criteria such as Maximizing First-Hop Signal to Noise Ratios (SNRs) (MFHS protocol), Maximizing Second-Hop SNRs (MSHS protocol), and Maximizing End-to-End SNRs (MEES protocol). In these protocols, the relays apply power-splitting receivers to harvest energy from radio frequency signals emitted from a source. Thus, each received SNR in the second hop is a function of a direct relay-destination gain and an indirect source-relay gain. The system performance of the proposed protocols is evaluated via exact outage probability analyses and Monte Carlo simulations. For further comparisons, an energy-harvesting decode-and-forward scheme with randomly relay selection (RRS protocol) and an energy-harvesting amplify-and-forward scheme (BAF protocol) are investigated and discussed. The simulation results show that 1) the MEES protocol outperforms the MFHS and MSHS protocols, and the MFHS protocol is more efficient than the MSHS protocol in the low SNR regions; 2) the proposed protocols achieve the best performance at the specific optimal power splitting ratios for which the MEES protocol has a balanced ratio for energy harvesting and decoding capacity; and 3) the theoretical analyses agree well with the simulation results.

Almost sure convergence coding theorems of one-shot and multi-shot Tunstall codes are proved for stationary memoryless sources. Coding theorem of one-shot Tunstall code is proved in the case that the leaf count of Tunstall tree increases. On the other hand, coding theorem is proved for multi-shot Tunstall code with increasing parsing count, under the assumption that the Tunstall tree grows as the parsing proceeds. In this result, it is clarified that the theorem for the one-shot Tunstall code is not a corollary of the theorem for the multi-shot Tunstall code. In the case of the multi-shot Tunstall code, it can be regarded that the coding theorem is proved for the sequential algorithm such that parsing and coding are processed repeatedly. Cartesian concatenation of trees and geometric mean of the leaf counts of trees are newly introduced, which play crucial roles in the analyses of multi-shot Tunstall code.

Non-volatile memory has many advantages such as high density and low leakage power but it consumes larger writing energy than SRAM. It is quite necessary to reduce writing energy in non-volatile memory design. In this paper, we propose write-reduction codes based on error correcting codes and reduce writing energy in non-volatile memory by decreasing the number of writing bits. When a data is written into a memory cell, we do not write it directly but encode it into a codeword. In our write-reduction codes, every data corresponds to an information vector in an error-correcting code and an information vector corresponds not to a single codeword but a set of write-reduction codewords. Given a writing data and current memory bits, we can deterministically select a particular write-reduction codeword corresponding to the data to be written, where the maximum number of flipped bits are theoretically minimized. Then the number of writing bits into memory cells will also be minimized. Experimental results demonstrate that we have achieved writing-bits reduction by an average of 51% and energy reduction by an average of 33% compared to non-encoded memory.

This paper studies the impact of integrating pricing with connection admission control (CAC) on the congestion management practices in contention-based wireless random access networks. Notably, when the network is free of charge, each self-interested user tries to occupy the channel as much as possible, resulting in the inefficient utilization of network resources. Pricing is therefore adopted as incentive mechanism to encourage users to choose their access probabilities considering the real-time network congestion level. A Stackelberg leader-follower game is formulated to analyze the competitive interaction between the service provider and the users. In particular, each user chooses the access probability that optimizes its payoff, while the self-interested service provider decides whether to admit or to reject the user's connection request in order to optimize its revenue. The stability of the Stackelberg leader-follower game in terms of convergence to the Nash equilibrium is established. The proposed CAC scheme is completely distributed and can be implemented by individual access points using only local information. Compared to the existing schemes, the proposed scheme achieves higher revenue gain, higher user payoff, and higher QoS performance.

When there are multiple component predictors, it is promising to integrate them into one predictor for advanced reasoning. If each component predictor is given as a stochastic model in the form of probability distribution, an exponential mixture of the component probability distributions provides a good way to integrate them. However, weight parameters used in the exponential mixture model are difficult to estimate if there is no training samples for performance evaluation. As a suboptimal way to solve this problem, weight parameters may be estimated so that the exponential mixture model should be a balance point that is defined as an equilibrium point with respect to the distance from/to all component probability distributions. In this paper, we propose a weight parameter estimation method that represents this concept using a symmetric Kullback-Leibler divergence and generalize this method.

A cognitive radio user (CU) can get assistance from sensor nodes (SN) to perform spectrum sensing. However, the SNs are often powered by a finite-capacity battery, which can maintain operations of the SNs over a short time. Therefore, energy-efficiency of the SNs becomes a crucial problem. In this paper, an SN is considered to be a device with an energy harvester that can harvest energy from a non-radio frequency (non-RF) energy resource while performing other actions concurrently. In any one time slot, in order to maintain the required sensing accuracy of the CR network and to conserve energy in the SNs, only a small number of SNs are required to sense the primary user (PU) signal, and other SNs are kept silent to save energy. For this, an algorithm to divide all SNs into groups that can satisfy the required sensing accuracy of the network, is proposed. In a time slot, each SN group can be assigned one of two actions: stay silent, or be active to perform sensing. The problem of determining the optimal action for all SN groups to maximize throughput of the CR network is formulated as a framework of a partially observable Markov decision process (POMDP), in which the effect of the current time slot's action on the throughput of future time slots is considered. The solution to the problem, that is the decision mode of the SN groups (i.e., active or silent), depends on the residual energy and belief of absence probability of the PU signal. The simulation results show that the proposed scheme can improve energy efficiency of CR networks compared with other conventional schemes.

Most research projects with respect to energy saving are trying to improve power efficiency and are using software to manage the power systems in the power on mode; but in our design, we modify the original Suspend to RAM mode-S3 state, which is the 3rd system state as defined by the ACPI specification, in order to reduce power consumption. We've redesigned the control circuit to save power while a PC is in the standby mode. First, we re-examine the entire circuit in the standby mode, and clarify which chip is used both to wake up the system and to turn off all unnecessary standby power previously used by the chips. Secondly, we redesign the power sequence and use an additional chip to control the system power supply, to allow a PC's normal system's operation to turn off the unnecessary power control chips. Third, in order to save power supply in the standby mode, we have simplified the multiple remote wake-up mechanism to control the remote boot device. The improvement shows that our design reduced power consumption to 0.21W from the original 0.56W while all the remote wake-up functions are disabled; and consumes 0.42W when using multiple remote wake-up functions. We implement the above modification from the legacy S3 state, and obtain lower power consumption. In order to distinguish the standby states, we name the modified S3 state as Deep S3 state.