This paper proposes a triangular active charge injection method to reduce resonant power supply noise by injecting the adequate amount of charge into the supply line of the LSI in response to the current consumption of the core circuit. The proposed circuit is composed of three key components, a voltage drop detector, an injection controller circuit and a canceling capacitor circuit. In addition to the theoretical analysis of the proposed method, the measurement results indicate that our proposed method with active capacitor can realize about 14% noise reduction compared with the original noise amplitude. The proposed circuit consumes 25.2 mW in steady state and occupies 0.182 mm2.

SPARX-128/256 is one of the two versions of the SPARX-128 block cipher family. It has 128-bit block size and 256-bit key size. SPARX has been developed using ARX-based S-boxes with the aim of achieving provable security against single-trail differential and linear cryptanalysis. In this letter, we propose 20-round impossible differential distinguishers for SPARX-128. Then, we utilize these distinguishers to attack 24 rounds (out of 40 rounds) of SPARX-128/256. Our attack has time complexity of 2232 memory accesses, memory complexity of 2160.81 128-bit blocks, and data complexity of 2104 chosen plaintexts.

This paper presents an algorithm for a physical activity (PA) classification and metabolic equivalents (METs) monitoring and its System-on-a-Chip (SoC) implementation to realize both power reduction and high estimation accuracy. Long-term PA monitoring is an effective means of preventing lifestyle-related diseases. Low power consumption and long battery life are key features supporting the wider dissemination of the monitoring system. As described herein, an adaptive sampling method is implemented for longer battery life by minimizing the active rate of acceleration without decreasing accuracy. Furthermore, advanced PA classification using both the heart rate and acceleration is introduced. The proposed algorithms are evaluated by experimentation with eight subjects in actual conditions. Evaluation results show that the root mean square error with respect to the result of processing with fixed sampling rate is less than 0.22[METs], and the mean absolute error is less than 0.06[METs]. Furthermore, to minimize the system-level power dissipation, a dedicated SoC is implemented using 130-nm CMOS process with FeRAM. A non-volatile CPU using non-volatile memory and a flip-flop is used to reduce the stand-by power. The proposed algorithm, which is implemented using dedicated hardware, reduces the active rate of the CPU and accelerometer. The current consumption of the SoC is less than 3-µA. And the evaluation system using the test chip achieves 74% system-level power reduction. The total current consumption including that of the accelerometer is 11.3-µA on average.

The recent switch from analog to digital TV broadcasting around the world has led to the development of communications standards that consider the use of TV White Spaces (TVWS). One such standard is the IEEE 802.22 wireless regional area network (WRAN), which considers the use of TVWS to provide broadband wireless services over long transmission links, and therefore presents an opportunity to bring connectivity and data-based services from urban to rural areas. Services that could greatly benefit from the deployment of wireless broadband data links between urban and rural areas are those related to telemedicine and m-health. To enable proper telemedicine service delivery from urban (e.g. an urban hospital) to rural locations (e.g. a rural clinic) it is of paramount importance to provide a certain quality of service (QoS) level. In this context, QoS provisioning for telemedicine applications over wireless networks presents a major challenge that must be addressed to fulfill the potential that rural wireless telemedicine has to offer. In this paper, a cross-layer approach combining medium access control (MAC) and application (APP) layers is proposed with the aim of reducing blocking probability in teleconsulting services operating over IEEE802.22/WRANs. At the APP layer, a teleconsulting traffic profile based on utilization rates is defined. On the other hand, at the MAC layer, an Adaptive Bandwidth Management (ABM) mechanism is used to perform a QoS-based classification of teleconsulting services and then dynamically allocate the bandwidth requirements. Three teleconsulting services with different bandwidth requirements are considered in order to evaluate the performance of the proposed approach: high-resolution teleconsulting, medium-resolution teleconsulting, and audio-only teleconsulting. Simulation results demonstrate that the proposed approach is able to reduce blocking probability by using different criteria for service modes within the admission control scheme.

Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.

Image boundary detection or image segmentation is an important step in image analysis. However, choosing appropriate parameters for boundary detection algorithms is necessary to achieve good boundary detection results. Image boundary detection fusion with unsupervised parameters can output a final consensus boundary, which is generally better than using unsupervised or supervised image boundary detection algorithms. In this study, we theoretically examine why image boundary detection fusion can work well and we propose a mixture model for image boundary detection fusion (MMIBDF) to achieve good consensus segmentation in an unsupervised manner. All of the segmentation algorithms are treated as new features and the segmentation results obtained by the algorithms are the values of the new features. The MMIBDF is designed to sample the boundary according to a discrete distribution. We present an inference method for MMIBDF and describe the corresponding algorithm in detail. Extensive empirical results demonstrate that MMIBDF significantly outperforms other image boundary detection fusion algorithms and the base image boundary detection algorithms according to most performance indices.

To prepare antireflection coating (ARC) by wet process is important technology for low cost fabrication of solar cells. In this research, we consider the optical reflectance of a three layer stack structure of ARC films on the pyramidally textured single-crystalline silicon substrates. Each layer of the ARC films is deposited by a spin-coating method. The triple layers consist of SiO2, SiO2-TiO2 mixture, and TiO2 films from air to the silicon substrate in that order, and the refractive index is slightly increased from air to the substrate. Light reflection can be reduced further mainly due to graded index effect. The optimized three layer structure ARC shows that the reflectance is below 0.048 at the wavelength of 600 nm.

We propose a time synchronization technique for mobile base stations (BSs) by distributing the reference time information from one optical network unit (ONU) to the BSs under different ONUs over Time Division Multiplexing Passive Optical Network (TDM-PON) using common Precision Time Protocol (PTP). The time accuracy, long term time stability and time source switchover functionality for redundancy are confirmed by experimental verification. Furthermore, an interoperability test between a 10G-EPON prototype in which the proposed protocol is implemented and a commercial Time Division Long Term Evolution (TD-LTE) BS is successfully demonstrated obtaining time error within 119ns, which is much less than the criterion value of 1.5µs, for 60 hours.

A continuous-time (CT) ΔΣ analog-to-digital converter (ADC) is a high resolution, wide-bandwidth ADC. A Gm-C filter is suitable for low power consumption and its frequency characteristics for a loop filter of the ADC. However, in practice, distortion generated in the Gm-C filter degrades the SNDR of the ADC, therefore a high-linearity Gm-cell with low power consumption is needed. A flipped voltage follower (FVF) Gm-cell is also used as a high-linearity Gm-cell, but distortion is caused by variation of drain-source voltage of its input transistors. In this paper, a new high-linearity Gm-cell is proposed for the CT ΔΣ ADC in order to address this problem. A proposed topology is a combination of a FVF and a cascode topology. The inserted transistors in the proposed Gm-cell behave as cascode transistors, therefore the drain-source voltage variation of the input transistor and a PMOS transistor for current source which causes distortion is suppressed. Simulation results show the proposed Gm-cell can realize the same linearity as the conventional Gm-cell with reducing 36% power consumption. A 20MHz-bandwidth CT ΔΣ ADC employing the proposed Gm-cells achieves SNDR of 72.4dB with power consumption of 6.8mW. Active area and FoM of the ADC are, respectively, 250μm × 220μm and 50fJ/conv.-step in 65nm CMOS process.

To help with decision making, online shoppers tend to go through both a list of a product's features and functionality provided by the vendor, as well as a list of reviews written by other users. Unfortunately, this process is ineffective when the buyer is confronted with large amounts of information, particularly when the buyer has limited experience with and knowledge of the product. In order to avoid this problem, we propose a framework of purpose-oriented recommendation that presents a ranked list of products suitable for a designated user purpose by identifying important product features to fulfill the purpose from online reviews. As technical foundation for realizing the framework, we propose several methods to mine relation between user purposes and product features from the consumer reviews. Using digital camera reviews on Amazon.com, the experimental results show that our proposed method is both effective and stable, with an acceptable rate of precision and recall.

Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.

In this paper we show a simple heuristic for constructing smaller automata for a set of regular expressions, based on suffix sorting: finding common prefixes and suffixes in regular expressions and merging them. It is an important problem in network security. We applied our approach to random and real-world regular expressions. Experimental results showed that our approach yields up to 12 times enhancement in throughput.

In this paper, a new and an accurate symbol error probability's analytical model of Rectangular Quadrature Amplitude Modulation in α-µ fading channel is presented for single-user single-input multi-output environment, which can be easily extended to generalized fading channels. The maximal-ratio combining technique is utilized at the receiving end and unified moment generating functions are used to derivate the results. The fading mediums considered are independent and non-identical. The mathematical model presented is applicable for slow and frequency non-selective fading channels only. The final expression is presented in terms of Meijer G-function; it contains single integrals with finite limits to evaluate the mathematical expressions with numerical techniques. The beauty of the model will help evaluate symbol error probability of rectangular quadrature amplitude modulation with spatial diversity over various fading mediums not addressed in this article. To check for the validity of derived analytical expressions, comparison is made between theoretical and simulation results at the end.

Self-paced learning (SPL) gradually trains the data from easy to hard, and includes more data into the training process in a self-paced manner. The advantage of SPL is that it has an ability to avoid bad local minima, and the system can improve the generalization performance. However, SPL's system needs an expert to judge the complexity of data at the beginning of training. Generally, this expert does not exist in the beginning, and is learned by gradually training the samples. Based on this consideration, we add an uncertainty of complexity judgment into SPL's system, and propose a self-paced learning with uncertainty prior (SPUP). For efficiently solving our system optimization function, an iterative optimization and statistical simulated annealing method are introduced. The final experimental results indicate that our SPUP has more robustness to the outlier and achieves higher accuracy and less error than SPL.

This paper shows an optimal spreading sequence in the Weyl sequence class, which is similar to the set of the Oppermann sequences for asynchronous CDMA systems. Sequences in Weyl sequence class have the desired property that the order of cross-correlation is low. Therefore, sequences in the Weyl sequence class are expected to minimize the inter-symbol interference. We evaluate the upper bound of cross-correlation and odd cross-correlation of spreading sequences in the Weyl sequence class and construct the optimization problem: minimize the upper bound of the absolute values of cross-correlation and odd cross-correlation. Since our optimization problem is convex, we can derive the optimal spreading sequences as the global solution of the problem. We show their signal to interference plus noise ratio (SINR) in a special case. From this result, we propose how the initial elements are assigned, that is, how spreading sequences are assigned to each users. In an asynchronous CDMA system, we also numerically compare our spreading sequences with other ones, the Gold codes, the Oppermann sequences, the optimal Chebyshev spreading sequences and the SP sequences in Bit Error Rate. Our spreading sequence, which yields the global solution, has the highest performance among the other spreading sequences tested.

Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.

In this paper we propose the Zone-based Energy Aware data coLlection (ZEAL) protocol. ZEAL is designed to be used in agricultural applications for wireless sensor networks. In these type of applications, all data is often routed to a single point (named “sink” in sensor networks). The overuse of the same routes quickly depletes the energy of the nodes closer to the sink. In order to minimize this problem, ZEAL automatically creates zones (groups of nodes) independent from each other based on the trajectory of one or more mobile sinks. In this approach the sinks collects data queued in sub-sinks in each zone. Unlike existing protocols, ZEAL accomplish its routing tasks without using GPS modules for location awareness or synchronization mechanisms. Additionally, ZEAL provides an energy saving mechanism on the network layer that puts zones to sleep when there are no mobile sinks nearby. To evaluate ZEAL, it is compared with the Maximum Amount Shortest Path (MASP) protocol. Our simulations using the ns-3 network simulator show that ZEAL is able to collect a larger number of packets with significantly less energy in the same amount of time.

In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

A monaural speech enhancement method combining deep neural network (DNN) with low rank analysis and speech present probability is proposed in this letter. Low rank and sparse analysis is first applied on the noisy speech spectrogram to get the approximate low rank representation of noise. Then a joint feature training strategy for DNN based speech enhancement is presented, which helps the DNN better predict the target speech. To reduce the residual noise in highly overlapping regions and high frequency domain, speech present probability (SPP) weighted post-processing is employed to further improve the quality of the speech enhanced by trained DNN model. Compared with the supervised non-negative matrix factorization (NMF) and the conventional DNN method, the proposed method obtains improved speech enhancement performance under stationary and non-stationary conditions.

Previous research has shown that transcripts generated by automatic speech recognition (ASR) technologies can improve the listening comprehension of non-native speakers (NNSs). However, we still lack a detailed understanding of how ASR transcripts affect listening comprehension of NNSs. To explore this issue, we conducted two studies. The first study examined how the current presentation of ASR transcripts impacted NNSs' listening comprehension. 20 NNSs engaged in two listening tasks, each in different conditions: C1) audio only and C2) audio+ASR transcripts. The participants pressed a button whenever they encountered a comprehension problem, and explained each problem in the subsequent interviews. From our data analysis, we found that NNSs adopted different strategies when using the ASR transcripts; some followed the transcripts throughout the listening; some only checked them when necessary. NNSs also appeared to face difficulties following imperfect and slightly delayed transcripts while listening to speech - many reported difficulties concentrating on listening/reading or shifting between the two. The second study explored how different display methods of ASR transcripts affected NNSs' listening experiences. We focused on two display methods: 1) accuracy-oriented display which shows transcripts only after the completion of speech input analysis, and 2) speed-oriented display which shows the interim analysis results of speech input. We conducted a laboratory experiment with 22 NNSs who engaged in two listening tasks with ASR transcripts presented via the two display methods. We found that the more the NNSs paid attention to listening to the audio, the more they tended to prefer the speed-oriented transcripts, and vice versa. Mismatched transcripts were found to have negative effects on NNSs' listening comprehension. Our findings have implications for improving the presentation methods of ASR transcripts to more effectively support NNSs.