A major challenge for speech emotion recognition is that when the training and deployment conditions do not use the same speech corpus, the recognition rates will obviously drop. Transfer learning, which has successfully addressed the cross-domain classification or recognition problem, is presented for cross-corpus speech emotion recognition. First, by using the maximum mean discrepancy embedding (MMDE) optimization and dimension reduction algorithms, two close low-dimensional feature spaces are obtained for source and target speech corpora, respectively. Then, a classifier function is trained using the learned low-dimensional features in the labeled source corpus, and directly applied to the unlabeled target corpus for emotion label recognition. Experimental results demonstrate that the transfer learning method can significantly outperform the traditional automatic recognition technique for cross-corpus speech emotion recognition.

In this paper, we propose a joint transmit and receive linear filtering based on minimum mean square error criterion (joint Tx/Rx MMSE filtering) for single-carrier (SC) multiple-input multiple-output (MIMO) transmission. Joint Tx/Rx MMSE filtering transforms the MIMO channel to the orthogonal eigenmodes to avoid the inter-antenna interference (IAI) and performs MMSE based transmit power allocation to sufficiently suppress the inter-symbol interference (ISI) resulting from the severe frequency-selectivity of the channel. Rank adaptation and adaptive modulation are jointly introduced to narrow the gap of received signal-to-interference plus noise power ratio (SINR) among eigenmodes. The superiority of the SC-MIMO transmission with joint Tx/Rx MMSE filtering and joint rank adaptation/adaptive modulation is confirmed by computer simulation.

In this paper, we study the feasibility of a batteryless wireless sensor supplied with energy by using microwave power transmission (MPT). If we perform co-channel operation of MPT and wireless local area networks (WLANs) for the sake of spectral efficiency, a time division method for MPT and WLAN communications is required to avoid serious interference from MPT to WLAN data transmissions. In addition, to reduce the power consumption of a sensor, the use of power-save operation of the sensor is desirable. We proposed a scheduling scheme that allocates time for MPT and WLAN communications. Specifically, in the proposed scheduling system, an energy source transmits microwave power to a sensor station except when the sensor station transmits data frames or receives beacon frames. In addition, in the proposed scheduling system, we force the remaining energy of the sensor station to converge to a maximum value by adjusting the time interval of data transmission from the sensor station such that the power consumption of the sensor station is reduced. On the basis of the proposition, we implemented a scheduling system and then confirmed that it performed successfully in the conducted experiments. Finally, we discussed the feasibility of the proposed scheduling scheme by evaluating the coverage and then showed that the scheduling scheme can be applied to closed space or room.

This paper presents a method to seamlessly extend the coverage of energy supply field for wireless sensor networks in order to free sensors from wires and batteries, where the multi-point scheme is employed to overcome path-loss attenuation, while the carrier shift diversity is introduced to mitigate the effect of interference between multiple wave sources. As we focus on the energy transmission part, sensor or communication schemes are out of scope of this paper. To verify the effectiveness of the proposed wireless energy transmission, this paper conducts indoor experiments in which we compare the power distribution and the coverage performance of different energy transmission schemes including conventional single-point, simple multi-point and our proposed multi-point scheme. To easily observe the effect of the standing-wave caused by multipath and interference between multiple wave sources, 3D measurements are performed in an empty room. The results of our experiments together with those of a simulation that assumes a similar antenna setting in free space environment show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are limited by path-loss, standing-wave created by multipath and interference between multiple wave sources. On the other hand, the proposed scheme can overcome power attenuation due to the path-loss as well as the effect of standing-wave created by multipath and interference between multiple wave sources.

In this paper, we propose a scalable connection-based time division multiple access architecture for wireless NoC. In this architecture, only one-hop transmission is needed when a packet is transmitted from one wired subnet to another wired subnet, which improves the communication performance and cuts down the energy consumption. Furthermore, by carefully designing the central arbiter, the bandwidth of the wireless channel can be fully used. Simulation results show that compared with the traditional WCube wireless NoC architecture, the proposed architecture can greatly improve the network throughput, and cut down the transmission latency and energy consumption with a reasonable area overhead.

A Decision Diagram Machine (DDM) is a special-purpose processor that has special instructions to evaluate a decision diagram. Since the DDM uses only a limited number of instructions, it is faster than the general-purpose Micro Processor Unit (MPU). Also, the architecture for the DDM is much simpler than that for an MPU. This paper presents a packet classifier using a parallel EVMDD (k) machine. To reduce computation time and code size, first, a set of rules for a packet classifier is partitioned into groups. Then, the parallel EVMDD (k) machine evaluates them. To further speed-up for the standard EVMDD (k) machine, we propose the prefetching EVMDD (k) machine which reads both the index and the jump address at the same time. The prefetching EVMDD (k) machine is 2.4 times faster than the standard one using the same memory size. We implemented a parallel prefetching EVMDD (k) machine consisting of 30 machines on an FPGA, and compared it with the Intel's Core i5 microprocessor running at 1.7GHz. Our parallel machine is 15.1-77.5 times faster than the Core i5, and it requires only 8.1-58.5 percents of the memory for the Core i5.

Dubé and Beaudoin proposed a lossless data compression called compression via substring enumeration (CSE) in 2010. We evaluate an upper bound of the number of bits used by the CSE technique to encode any binary string from an unknown member of a known class of k-th order Markov processes. We compare the worst case maximum redundancy obtained by the CSE technique for any binary string with the least possible value of the worst case maximum redundancy obtained by the best fixed-to-variable length code that satisfies the Kraft inequality.

Wireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

In this paper, we propose a superpixel based depth map generation scheme for the application to monoscopic to stereoscopic video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in the video sequences. First, the depth maps of the key frames in the input sequence are generated by superpixel merging and some user interactions. Second, the frames in the input sequences are over-segmented by Simple Linear Iterative Clustering (SLIC) or depth aided SLIC method depending on whether or not they have the depth maps. Third, each superpixel in current frame is used to match the corresponding superpixel in its previous frame. Finally, depth map is propagated with a joint bilateral filter based on the estimated matching vector of each superpixel. We show an improved performance of the proposed algorithm through experimental results.

A 60-GHz power amplifier (PA) with a reliability consideration for a hot-carrier-induced~(HCI) degradation is presented. The supply voltage of the last stage of the PA ($V_{{ m PA}}$) is dynamically controlled by an on-chip digitally-assisted low drop-out voltage regulator (LDO) to alleviate HCI effects. A physical model for estimation of HCI degradation of NMOSFETs is discussed and investigated for dynamic operation. The PA is fabricated in a standard 65-nm CMOS process with a core area of 0.21,mm$^{2}$, which provides a saturation power of 10.1,dBm to 13.2,dBm with a peak power-added efficiency~(PAE) of 8.1% to 15.0% for the supply voltage $V_{{ m PA}}$ which varies from 0.7,V to 1.0,V at 60,GHz, respectively.

We give some attacks on the DBL hash modes MDC-4 and MJH. Our preimage attack on the MDC-4 hash function requires the time complexity O(23n/2) for the block length n of the underlying block cipher, which significantly improves the previous results. Our collision attack on the MJH hash function has a time complexity less than 2124 for n=128. Our preimage attack on the the MJH compression function finds a preimage with the time complexity of 2n. It is converted to a preimage attack on the hash function with the time complexity of O(23n/2). As far as we know, any cryptanalytic result for MJH has not been published before. Our results are helpful for understanding the security of the hash modes together with their security proofs.

This study analyzes emotions established between people while interacting in face-to-face conversation. By focusing on empathy and antipathy, especially the process by which they are perceived by external observers, this paper aims to elucidate the tendency of their perception and from it develop a computational model that realizes the automatic inference of perceived empathy/antipathy. This paper makes two main contributions. First, an experiment demonstrates that an observer's perception of an interacting pair is affected by the time lags found in their actions and reactions in facial expressions and by whether their expressions are congruent or not. For example, a congruent but delayed reaction is unlikely to be perceived as empathy. Based on our findings, we propose a probabilistic model that relates the perceived empathy/antipathy of external observers to the actions and reactions of conversation participants. An experiment is conducted on ten conversations performed by 16 women in which the perceptions of nine external observers are gathered. The results demonstrate that timing cues are useful in improving the inference performance, especially for perceived antipathy.

The use of cooperative nodes is effective for enhancing the reliability of wireless data transmission between a source and a destination by means of transmit diversity effect. However, in its application to wireless multi-hop networks, how to form cooperative node candidates and how to select multiple cooperative nodes out of them have not been well investigated. In this paper, we propose a multiple cooperative node selection method based on a criterion composed of “quality” and “angle” metrics, which can select and order adequate cooperative nodes. Computer simulation results show that the proposed method can effectively reduce the packet error rate without any knowledge on node location.

A smoothing method for minimum phone error linear regression (MPELR) is proposed in this paper. We show that the objective function for minimum phone error (MPE) can be combined with a prior mean distribution. When the prior mean distribution is based on maximum likelihood (ML) estimates, the proposed method is the same as the previous smoothing technique for MPELR. Instead of ML estimates, maximum a posteriori (MAP) parameter estimate is used to define the mode of prior mean distribution to improve the performance of MPELR. Experiments on a large vocabulary speech recognition task show that the proposed method can obtain 8.4% relative reduction in word error rate when the amount of data is limited, while retaining the same asymptotic performance as conventional MPELR. When compared with discriminative maximum a posteriori linear regression (DMAPLR), the proposed method shows improvement except for the case of limited adaptation data for supervised adaptation.

A novel fusion method for semantic concept detection in images, called tree fusion, is proposed. Various kinds of features are given to different classifiers. Then, according to the importance of features and effectiveness of classifiers, the results of feature-classifier pairs are ranked and fused using C4.5 algorithm. Experimental results conducted on the MSRC and PASCAL VOC 2007 datasets have demonstrated the effectiveness of the proposed method over the traditional fusion methods.

Because dielectrics between active layers have low thermal conductivities, there is a demand to reduce the temperature increase in three-dimensional integrated circuits (3D ICs). This paper demonstrates that, in the design of 3D ICs, different layer assignments often lead to different temperature increases. Based on this observation, we are motivated to perform temperature-aware layer assignment. Our work includes two parts. Firstly, an integer linear programming (ILP) approach that guarantees a minimum temperature increase is proposed. Secondly, a polynomial-time heuristic algorithm that reduces the temperature increase is proposed. Compared with the previous work, which does not take the temperature increase into account, the experimental results show that both our ILP approach and our heuristic algorithm produce a significant reduction in the temperature increase with a very small area overhead.

Curve extension is a useful function in shape modeling for cyberworlds, while a Ball B-spline Curve (BBSC) has its advantages in representing freeform tubular objects. In this paper, an extension algorithm for the BBSC with G2-continuity is investigated. We apply the extending method of B-Spline curves to the skeleton of the BBSC through generalizing a minimal strain energy method from 2D to 3D. And the initial value of the G2-continuity parameter for the skeleton is selected by minimizing the approximate energy function which is a problem with O(1) time complexity. The corresponding radius function of the control ball points is determined through applying the G2-continuity conditions for the skeleton to the scalar function. In order to ensure the radii of the control ball points are positive, we make a decision about the range of the G2-continuity parameter for the radius and then determine it by minimizing the strain energy in the affected area. Some experiments comparing our method with other methods are given. And at the same time, we present the advantage of our method in modeling flexibility from the aspects of the skeleton and radius. The results illustrate our method for extending the BBSC is effective.

The past decade has witnessed a growing interest in vehicular networking. Initially motivated by traffic safety, vehicles equipped with computing, communication and sensing capabilities will be organized into ubiquitous and pervasive networks with a significant Internet presence while on the move. Large amount of data can be generated, collected, and processed on the vehicular networks. Big data on vehicular networks include useful and sensitive information which could be exploited by malicious intruders. But intrusion detection in vehicular networks is challenging because of its unique features of vehicular networks: short range wireless communication, large amount of nodes, and high mobility of nodes. Traditional methods are hard to detect intrusion in such sophisticated environment, especially when the attack pattern is unknown, therefore, it can result unacceptable false negative error rates. As a novel attempt, the main goal of this research is to apply data mining methodology to recognize known attacks and uncover unknown attacks in vehicular networks. We are the first to attempt to adapt data mining method for intrusion detection in vehicular networks. The main contributions include: 1) specially design a decentralized vehicle networks that provide scalable communication and data availability about network status; 2) applying two data mining models to show feasibility of automated intrusion detection system in vehicular networks; 3) find the detection patterns of unknown intrusions.

In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.