Integer codes are defined by error-correcting codes over integers modulo a fixed positive integer. In this paper, we show that the construction of integer codes can be reduced into the cases of prime-power moduli. We can efficiently search integer codes with small prime-power moduli and can construct target integer codes with a large composite-number modulus. Moreover, we also show that this prime-factorization reduction is useful for the construction of self-orthogonal and self-dual integer codes, i.e., these properties in the prime-power moduli are preserved in the composite-number modulus. Numerical examples of integer codes and generator matrices demonstrate these facts and processes.

When trying to estimate time-varying multipath channels by applying a basis expansion model (BEM) in orthogonal frequency division multiplexing (OFDM) systems, pilot clusters are contaminated by inter-carrier interference (ICI). The pilot cluster ICI (PC-ICI) degrades the estimation accuracy of BEM coefficients, which degrades system performance. In this paper, a PC-ICI suppression scheme is proposed, in which two coded symbols defined as weighted sums of data symbols are inserted on both sides of each pilot cluster. Under the assumption that the channel has Flat Doppler spectrum, the optimized weight coefficients are obtained by an alternating iterative optimization algorithm, so that the sum of the PC-ICI generated by the encoded symbols and the data symbols is minimized. By approximating the optimized weight coefficients, they are independent of the channel tap power. Furthermore, it is verified that the proposed scheme is robust to the estimation error of the normalized Doppler frequency offset and can be applied to channels with other types of Doppler spectra. Numerical simulation results show that, compared with the conventional schemes, the proposed scheme achieves significant improvements in the performance of PC-ICI suppression, channel estimation and system bit-error-ratio (BER).

Wireless information and power transfer technology is a promising means of supplying power for remote terminals in future communication systems. This paper investigates time-splitting (TS) recource allocation schemes for multi-cell massive MIMO systems with downlink (DL) wireless power transfer and uplink (UL) user information transmission under a harvest-then-transmit protocol. In order to jointly optimize the power and time allocation, two power minimization problems are formulated under different constraints on the minimal quality-of-service (QoS) requirement. Then, these original non-convex problems are transformed into their convex approximated ones which can be solved iteratively by successive convex approximation. Simulation results show that by exploiting the diversity effect of large-scale antenna arrays, the complexity-reduced asymptotic recourse allocation scheme almost match the power efficiency of the nonasymptotic scheme.

Data breach and data destruction attack have become the critical security threats for the ICT (Information and Communication Technology) infrastructure. Both the Internet service providers and users are suffering from the cyber threats especially those to confidential data and private information. The requirements of human social activities make people move carrying confidential data and data breach always happens during the transportation. The Internet connectivity and cryptographic technology have made the usage of confidential data much secure. However, even with the high deployment rate of the Internet infrastructure, the concerns for lack of the Internet connectivity make people carry data with their mobile devices. In this paper, we describe the main patterns of data breach occur on mobile devices and propose a secure in-depth file system concealed by GPS-based mounting authentication to mitigate data breach on mobile devices. In the proposed in-depth file system, data can be stored based on the level of credential with corresponding authentication policy and the mounting operation will be only successful on designated locations. We implemented a prototype system using Veracrypt and Perl language and confirmed that the in-depth file system worked exactly as we expected by evaluations on two locations. The contribution of this paper includes the clarification that GPS-based mounting authentication for a file system can reduce the risk of data breach for mobile devices and a realization of prototype system.

High-performance computing (HPC) has penetrated into various research fields, yet the increase in computing power is limited by conventional electrical interconnections. The proposed architecture, NEST, exploits wavelength routing in arrayed waveguide grating routers (AWGRs) to achieve a scalable, low-latency, and high-throughput network. For the intra pod and inter pod communication, the symmetrical topology of NEST reduces the network diameter, which leads to an increase in latency performance. Moreover, the proposed architecture enables exponential growth of network size. Simulation results demonstrate that NEST shows 36% latency improvement and 30% throughput improvement over the dragonfly on an average.

In this paper, we review a super-steep subthreshold slope (SS) (<1 mV/dec) body-tied (BT) silicon on insulator (SOI) metal oxide semiconductor field effect transistor (MOSFET) fabricated with 0.15 µm SOI technology and discuss the possibility of its use in ultralow voltage applications. The mechanism of the super-steep SS in the BT SOI MOSFET was investigated with technology computer-aided design simulation. The gate length/width and Si thickness optimizations promise further reductions in operation voltage, as well as improvement of the ION/IOFF ratio. In addition, we demonstrated control of the threshold voltage and hysteresis characteristics using the substrate and body bias in the BT SOI MOSFET.

In this paper, we propose an IEEE 802.15.10-based layer 2 routing (L2R) method with a load balancing algorithm; the proposal considers fairness in terms of the cumulative number of sending packets at each terminal to resolve the packet concentration problem for the IEEE 802.15.4-based low-power consumption wireless smart utility network (Wi-SUN) systems. The proposal uses the accumulated sending times of each terminal as a weight in calculating each path quality metric (PQM) to decide multi-hopping routes with load balancing in the network. Computer simulation of the mesh network with 256 terminals shows that the proposed routing method can improve the maximum sending ratio (MSR), defined as the ratio of the maximum sending times to the average number of sending times in the network, by 56% with no degradation of the end-to-end communication success ratio (E2E-SR). The proposed algorithm is also experimentally evaluated by using actual Wi-SUN modules. The proposed routing method also improves the MSR by 84% with 70 terminals. Computer simulations and experiments prove the effectiveness of the proposed method in terms of load balancing.

As IoT services become more popular, mobile networks will have to accommodate a wide variety of devices that have different requirements such as different bandwidth limitations and latencies. This paper describes edge distributed mobile network architectures for the IoT era based on dedicated network technology and multi-access edge computing technology, which have been discussed in 3GPP and ETSI. Furthermore, it describes two context-aware control methods that will make mobile networks on the network architecture more efficient, reliable, and real-time: autonomous and distributed mobility management and bandwidth-guaranteed transmission rate control in a networked control system.

A wideband design of the waveguide short-slot 2-plane coupler with 2×2 input/output ports is designed, fabricated, and evaluated. Using coupling coefficients of complementary propagating modes which are TE11, TE21, and TE30 modes, the flatness of the output amplitudes of 2-plane coupler is improved. The coupler operates from 4.96GHz to 5.27GHz (bandwidth 6.1%) which is wider than the former coupler without considering the complementary propagating mode from 5.04GHz to 5.17GHz (bandwidth 2.5%).

Spectrum-based fault localization (SFL) is a lightweight approach, which aims at helping debuggers to identity root causes of failures by measuring suspiciousness for each program component being a fault, and generate a hypothetical fault ranking list. Although SFL techniques have been shown to be effective, the fault component in a buggy program cannot always be ranked at the top due to its complex fault triggering models. However, it is extremely difficult to model the complex triggering models for all buggy programs. To solve this issue, we propose two simple fault triggering models (RIPRα and RIPRβ), and a refinement technique to improve fault absolute ranking based on the two fault triggering models, through ruling out some higher ranked components according to its fault triggering model. Intuitively, our approach is effective if a fault component was ranked within top k in the two fault ranking lists outputted by the two fault localization strategies. Experimental results show that our approach can significantly improve the fault absolute ranking in the three cases.

Job scheduling on many-core computers with tens or even hundreds of processing cores is one of the key technologies in High Performance Computing (HPC) systems. Despite many scheduling algorithms have been proposed, scheduling remains a challenge for executing highly effective jobs that are assigned in a single computing node with diverse scheduling objectives. On the other hand, the increasing scale and the need for rapid response to changing requirements are hard to meet with existing scheduling models in an HPC node. To address these issues, we propose a novel adaptive scheduling model that is applied to a single node with a many-core processor; this model solves the problems of scheduling efficiency and scalability through an adaptive optimistic control mechanism. This mechanism exposes information such that all the cores are provided with jobs and the tools necessary to take advantage of that information and thus compete for resources in an uncoordinated manner. At the same time, the mechanism is equipped with adaptive control, allowing it to adjust the number of running tools dynamically when frequent conflict happens. We justify this scheduling model and present the simulation results for synthetic and real-world HPC workloads, in which we compare our proposed model with two widely used scheduling models, i.e. multi-path monolithic and two-level scheduling. The proposed approach outperforms the other models in scheduling efficiency and scalability. Our results demonstrate that the adaptive optimistic control affords significant improvements for HPC workloads in the parallelism of the node-level scheduling model and performance.

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.

We consider space time block coded-continuous phase modulation (STBC-CPM), which has the advantages of both STBC and CPM at the same time. A weak point of STBC-CPM is that the normalized spectral efficiency (NSE) is limited by the orthogonality of the STBC and CPM parameters. The purpose of this study is to improve the NSE of STBC-CPM. The NSE depends on the transmission rate (TR), the bit error rate (BER) and the occupied bandwidth (OBW). First, to improve the TR, we adapt quasi orthogonal-STBC (QO-STBC) for four transmit antennas and quasi-group orthogonal Toeplitz code (Q-GOTC) for eight transmit antennas, at the expense of the orthogonality. Second, to evaluate the BER, we derive a BER approximation of STBC-CPM with non-orthogonal STBC (NO-STBC). The theoretical analysis and simulation results show that the NSE can be improved by using QO-STBC and Q-GOTC. Third, the OBW depends on CPM parameters, therefore, the tradeoff between the NSE and the CPM parameters is considered. A computer simulation provides a candidate set of CPM parameters which have better NSE. Finally, the adaptation of non-orthogonal STBC to STBC-CPM can be viewed as a generalization of the study by Silvester et al., because orthogonal STBC can be thought of as a special case of non-orthogonal STBC. Also, the adaptation of Q-GOTC to CPM can be viewed as a generalization of our previous letter, because linear modulation scheme can be thought of as a special case of non-linear modulation.

Wireless power transfer (WPT) via coupled magnetic resonances has more than ten years history of development. However, it appears frequency splitting phenomenon in the over-coupled region, thus, the output power of the two-coil WPT system achieves the maximum output power at the two splitting angular frequencies and not at the natural resonant angular frequency. By investigating the relationship between the impedances of the transmitter side and receiver side, we found that WPT system is a power superposition system, and the reasons were given to explaining how to appear the frequency splitting and impact on the maximum output power of the system in details. First, the circuit model was established and transfer characteristics of the two-coil WPT system were studied by utilizing circuit theories. Second, the mechanism of the power superposition of the WPT system was carefully researched. Third, the relationship between the impedances of the transmitter side and receiver side was obtained by investigating the impedance characteristics of a two-coil WPT system, and also the impact factors of the maximum output power of the system were obtained by using a power superposition mechanism. Finally, the experimental circuit was designed and experimental results are well consistent with the theoretical analysis.

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

Kreyvium is a NLFSR-based stream cipher which is oriented to homomorphic-ciphertext compression. This is a variant of Trivium with 128-bit security. Designers have evaluated the security of Kreyvium and concluded that the resistance of Kreyvium to the conditional differential cryptanalysis is at least the resistance of Trivium, and even better. However, we consider that this attack is effective for reduced Kreyvium due to the structure of it. This paper shows the conditional differential cryptanalysis for Kreyvium, and we propose distinguishing and key recovery attacks. We show how to arrange differences and conditions to obtain good higher-order conditional differential characteristics. We use two types of higher-order conditional differential characteristics to find a distinguisher, e.g. the bias of higher-order conditional differential characteristics of a keystream and the probabilistic bias of them. In the first one, we obtain the distinguisher on Kreyvium with 730 rounds from 20-th order characteristics. In the second one, we obtain the distinguisher on Kreyvium with 899 rounds from 25-th order conditional differential characteristics. Moreover, we show the key recovery attack on Kreyvium with 736 rounds from 20-th order characteristics. We experimentally confirm all our attacks. The second distinguisher shows that we can obtain the distinguisher on Kreyvium with more rounds than the distinguisher on Trivium. Therefore, Kreyvium has a smaller security margin than Trivium for the conditional differential cryptanalysis.

Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.

In this letter, we present a novel single-precision floating-point multiply-accumulator (FNA-MAC) to achieve lower hardware resource, reduced computing latency and improved computing accuracy for continuous dot product operations. By further fusing the normalization and alignment in the traditional FMA algorithm, the proposed architecture eliminates the first N-1 normalization and rounding operations for an N-point dot product, and preserves the precision of interim results in a significant bit size that is twice of that in the traditional methods. The normalization and rounding of the final result is processed at the cost of consuming an additional multiply-add operation. The simulation results show that the improvement in computational accuracy is significant. Meanwhile, when comparing to a recently published FMA design, the proposed FNA-MAC can reduce the slice look-up table/flip-flop resource and computing latency by a fact of 18%, 33.3%, respectively.

We have proposed a fish farm monitoring system for achieving efficient fish farming. In our system, sensor nodes are attached at fish to monitor its health status. In this letter, we propose a method for gathering sensor data from sensor nodes to sink nodes when the transmission range of sensor node is shorter than the size of fish cage. In our proposed method, a part of sensor nodes become leader nodes and they forward gathered sensor data to the sink nodes. Through simulation evaluations, we show that the data gathering performance of our proposed method is higher than that of traditional methods.