In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.

We propose a cooperative cognitive radio network (CCRN) with secondary users (SUs) employing two simultaneous transmit and receive (STAR) antennas. In the proposed framework of full-duplex (FD) multiple-input-multiple-output (MIMO) CCRN, the region of achievable rate is expanded via FD communication among SUs enabled by the STAR antennas adopted for the SUs. The link capacity of the proposed framework is analyzed theoretically. It is shown through numerical analysis that the proposed FD MIMO-CCRN framework can provide a considerable performance gain over the conventional frameworks of CCRN and MIMO-CCRN.

We propose a weighted subtask controller and sufficient conditions for boundedness of the controller both velocity and acceleration domain. Prior to designing the subtask controller, a task controller is designed for global asymptotic stability of task space error and subtask error. Although the subtask error converges to zero by the task controller, the boundedness of the subtask controller is also important, therefore its boundedness conditions are presented. The weighted pseudo inverse is introduced to relax the constraints of the null-space of Jacobian. Using the pseudo inverse, we design subtask controller and propose sufficient conditions for boundedness of the auxiliary signal to show the existence of the inverse kinematic solution. The results of experiments using 7-DOF WAM show the effectiveness of the proposed controller.

As interaction between human and computer continues to develop to the most natural form possible, it becomes increasingly urgent to incorporate emotion in the equation. This paper describes a step toward extending the research on emotion recognition to Indonesian. The field continues to develop, yet exploration of the subject in Indonesian is still lacking. In particular, this paper highlights two contributions: (1) the construction of the first emotional audio-visual database in Indonesian, and (2) the first multimodal emotion recognizer in Indonesian, built from the aforementioned corpus. In constructing the corpus, we aim at natural emotions that are corresponding to real life occurrences. However, the collection of emotional corpora is notably labor intensive and expensive. To diminish the cost, we collect the emotional data from television programs recordings, eliminating the need of an elaborate recording set up and experienced participants. In particular, we choose television talk shows due to its natural conversational content, yielding spontaneous emotion occurrences. To cover a broad range of emotions, we collected three episodes in different genres: politics, humanity, and entertainment. In this paper, we report points of analysis of the data and annotations. The acquisition of the emotion corpus serves as a foundation in further research on emotion. Subsequently, in the experiment, we employ the support vector machine (SVM) algorithm to model the emotions in the collected data. We perform multimodal emotion recognition utilizing the predictions of three modalities: acoustic, semantic, and visual. When compared to the unimodal result, in the multimodal feature combination, we attain identical accuracy for the arousal at 92.6%, and a significant improvement for the valence classification task at 93.8%. We hope to continue this work and move towards a finer-grain, more precise quantification of emotion.

Although Wolf Pack Algorithm (WPA) is a novel optimal algorithm with good performance, there is still room for improvement with respect to its convergence. In order to speed up its convergence and strengthen the search ability, we improve WPA with the Differential Evolution (DE) elite set strategy. The new proposed algorithm is called the WPADEES for short. WPADEES is faster than WPA in convergence, and it has a more feasible adaptability for various optimizations. Six standard benchmark functions are applied to verify the effects of these improvements. Our experiments show that the performance of WPADEES is superior to the standard WPA and other intelligence optimal algorithms, such as GA, DE, PSO, and ABC, in several situations.

Recently, stochastic computing based on stochastic numbers attracts attention as an effective computation method, which realizes arithmetic operations by simple logic circuits with a tolerance of bit errors. When we input two or more identical values to a stochastic circuit, we require to duplicate a stochastic number. However, if bit streams of duplicated stochastic numbers are dependent on each other, their arithmetic operation results can be inaccurate. In this paper, we propose two stochastic number duplicators, called FSR and RRR. The stochastic numbers duplicated by the FSR and RRR duplicators have the equivalent values but have independent bit streams, effectively utilizing bit re-arrangement using randomized bit streams. Experimental evaluation results demonstrate that the RRR duplicator, in particular, obtains more accurate results even if a circuit has re-convergence paths, reducing the mean square errors by 20%-89% compared to a conventional stochastic number duplicator.

We present a modular way of implementing adaptive decisions in performing scientific simulations. The proposed method employs modern software engineering mechanisms to allow for better software management in scientific computing, where software adaptation has often been implemented manually by the programmer or by using in-house tools, which complicates software management over time. By applying the aspect-oriented programming (AOP) paradigm, we consider software adaptation as a separate concern and, using popular AOP constructs, implement adaptive decision separately from the original code base, thereby improving software management. We demonstrate the effectiveness of our approach with applications to stochastic simulation software.

Multiuser MIMO (MU-MIMO) improves the system channel capacity by generating a large virtual MIMO channel between a base station and multiple user terminals (UTs) with effective utilization of wireless resources. Block beamforming algorithms such as Block Diagonalization (BD) and Block Maximum Signal-to-Noise ratio (BMSN) have been proposed in order to realize MU-MIMO broadcast transmission. The BD algorithm cancels inter-user interference (IUI) by creating the weights so that the channel matrices for the other users are set to be zero matrices. The BMSN algorithm has a function of maintaining a high gain response for each desired user in addition to IUI cancellation. Therefore, the BMSN algorithm generally outperforms the BD algorithm. However, when the number of transmit antennas is equal to the total number of receive antennas, the transmission rate by both BD and BMSN algorithms is decreased. This is because the eigenvalues of channel matrices are too small to support data transmission. To resolve the issue, this paper focuses on an antenna selection (AS) method at the UTs. The AS method reduces the number of pattern nulls for the other users except an intended user in the BD and BMSN algorithms. It is verified via bit error rate (BER) evaluation that the AS method is effective in the BD and BMSN algorithms, especially, when the number of user antennas with a low bit rate (i.e., low signal-to-noise power ratio) is increased. Moreover, this paper evaluates the achievable bit rate and throughput including an actual channel state information feedback based on IEEE802.11ac standard. Although the number of equivalent receive antenna is reduced to only one by the AS method when the number of antennas at the UT is two, it is shown that the throughputs by BD and BMSN with the AS method (BD-AS and BMSN-AS) are higher than those by the conventional BD and BMSN algorithms.

Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

A frequently occurring subcircuit consists of a loop of a resistor (R), a field-effect transistor (FET), and a capacitor (C). The FET acts as a switch, controlled at its gate terminal by a clock voltage. This subcircuit may be acting as a sample-and-hold (S/H), as a passive mixer (P-M), or as a bandpass filter or bandpass impedance. In this work, we will present a useful analysis that leads to a simple signal flow graph (SFG), which captures the FET-R-C circuit's action completely across a wide range of design parameters. The SFG dissects the circuit into three filtering functions and ideal sampling. This greatly simplifies analysis of frequency response, noise, input impedance, and conversion gain, and leads to guidelines for optimum design. This paper focuses on the analysis of a single-path FET-R-C circuit's signal transfer characteristics including the reconstruction of the complete waveform from the discrete-time sampled voltage.

Virtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combinationBS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.

This paper proposes a hardware configuration for uplink multi-user multiple-input multiple-output (MU-MIMO) transmissions in a distributed antenna system (DAS). The demand for high-speed transmission in the uplink has increased recently, because of which standardizations in LTE-advanced and IEEE 802.11ax networks is currently underway. User terminal (UT) scheduling on the downlink MU-MIMO transmission is easy even in unlicensed band such as those in wireless local area network (WLAN) systems. However, the detailed management of the UTs is difficult on the uplink MU-MIMO transmissions because of the decentralized wireless access control. The proposed configuration allows an antenna to be selected from an external device on the access point (AP). All AP antennas are divided into groups, and the received signal in each group is input to the amplitude detector via a directional coupler. Subsequently, the selected antenna is fed by a multiple-to-one switch instead of a matrix switch. To clarify the effectiveness of the proposed configuration, we conduct computer simulations based on the ray-tracing method for propagation channels in an indoor environment.

We have proposed and developed new electro-optic modulators for the pre-equalization of signal distortion caused by the optical fiber chromatic dispersion effect. We found that the synthesis of an almost arbitrary impulse response function is obtainable by utilizing an electro-optic modulator composed of a Mach-Zehnder waveguide and travelling-wave electrodes on a ferro-electric material substrate with polarization-reversed structures. In this paper, the operational principle, design and simulation results of the pre-equalization modulator are presented. Some preliminary experimental results are also shown with future prospects.

A lot of vision systems have been embedded in devices around us, like mobile phones, vehicles and UAVs. Many of them still need interactive operations of human users. However, specifying accurate object information could be a challenging task due to video jitters caused by camera shakes and target motions. In this paper, we first collect practical hand drawn bounding boxes on real-life videos which are captured by hand-held cameras and UAV-based cameras. We give a deep look into human-computer interactive operations on unstable images. The collected data shows that human input suffers heavy deviations which are harmful to interaction accuracy. To achieve robust interactions on unstable platforms, we propose a target-focused video stabilization method which utilizes a proposal-based object detector and a tracking-based motion estimation component. This method starts with a single manual click and outputs stabilized video stream in which the specified target stays almost stationary. Our method removes not only camera jitters but also target motions simultaneously, therefore offering an comfortable environment for users to do further interactive operations. The experiments demonstrate that the proposed method effectively eliminates image vibrations and significantly increases human input accuracy.

This paper proposes MRO-PUF, a new architecture for ring-oscillator-based physically unclonable functions (PUFs) with enhanced resistance against machine learning attacks. In the proposed PUF, an instantaneous output value of a ring oscillator is used as a response, whereas the most existing PUFs directly use propagation delays to determine the response. Since the response of the MRO-PUF is non-linear and discontinuous as the delay of the ring oscillator increases, the prediction of the response by machine learning attacks is difficult. Through the performance evaluation of the MRO-PUF with simulations, it achieves 15 times stronger resistance against machine learning attacks using a support vector machine compared to the existing ones such as an arbiter PUF and a bistable ring PUF. The MRO-PUF also achieves a sufficient level of the basic performance of PUFs in terms of uniqueness and robustness.

Approximate computing is a promising solution for future energy-efficient designs because it can provide great improvements in performance, area and/or energy consumption over traditional exact-computing designs for non-critical error-tolerant applications. However, the most challenging issue in designing approximate circuits is how to guarantee the pre-specified computation accuracy while achieving energy reduction and performance improvement. To address this problem, this paper starts from the state-of-the-art general approximate adder model (GeAr) and extends it for more possible approximate design candidates by relaxing the design restrictions. And then a maximum-error-distance-based performance/accuracy formulation, which can be used to select the performance/energy-accuracy optimal design from the extended design space, is proposed. Our evaluation results show the effectiveness of the proposed method in terms of area overhead, performance, energy consumption, and computation accuracy.

Response property is a kind of liveness property. Response property problem is defined as follows: Given two activities α and β, whenever α is executed, is β always executed after that? In this paper, we tackled the problem in terms of Workflow Petri nets (WF-nets for short). Our results are (i) the response property problem for acyclic WF-nets is decidable, (ii) the problem is intractable for acyclic asymmetric choice (AC) WF-nets, and (iii) the problem for acyclic bridge-less well-structured WF-nets is solvable in polynomial time. We illustrated the usefulness of the procedure with an application example.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in two-way relay channels where two users exchange information with each other via a multi-antenna relay node. The signals forwarded by the relay node are also used to supply the power to two users. We formulate a max-min optimization problem aiming to maximize the minimum harvested energy between two users to achieve fairness. We jointly optimize the relay beamforming matrix and allocating powers at the two users subject to the quality of service (QoS) constraints. To be specific, we consider the amplify-and-forward (AF) relay strategy and the time splitting SWIPT strategy. To this end, we propose two different time splitting protocols to enable relay to supply power to two users. To solve the non-convex joint optimization problem, we propose to split the original optimization problem into two subproblems and solving them iteratively to obtain the final solution. It is shown that the first subproblem dealing with the beamforming matrix can be optimally solved by using the technique of relaxed semidefinite programming (SDR), and the second subproblem, which deals with the power allocation, can be solved via linear programming. The performance comparison of two schemes as well as the one-way relaying scheme are provided and the effectiveness of the proposed schemes is verified.

Operations, such as text entry and zooming, are simple and frequently used on mobile touch devices. However, these operations are far from being perfectly supported. In this paper, we present our prototype, BackAssist, which takes advantage of back-of-device input to augment front-of-device touch interaction. Furthermore, we present the results of a user study to evaluate whether users can master the back-of-device control of BackAssist or not. The results show that the back-of-device control can be easily grasped and used by ordinary smart phone users. Finally, we present two BackAssist supported applications - a virtual keyboard application and a map application. Users who tried out the two applications give positive feedback to the BackAssist supported augmentation.

In this letter, we propose a solution for managing multiple adaptive streaming clients running on different devices in a wireless home network. Our solution consists of two main aspects: a manager that determines bandwidth allocated for each client and a client-based throughput control mechanism that regulates the video traffic throughput of each client. The experimental results using a real test-bed show that our solution is able to effectively improve the quality for concurrent streaming clients.