In this letter, we propose a novel technique to increase the visibility of the hazy image. Benefiting from the atmospheric scattering model and the invariance principle for scene structure, we formulate structure constraint equations that derive from two simulated inputs by performing gamma correction on the input image. Relying on the inherent boundary constraint of the scattering function, the expected scene albedo can be well restored via these constraint equations. Extensive experimental results verify the power of the proposed dehazing technique.

We consider the intrinsic randomness problem for correlated sources. Specifically, there are three correlated sources, and we want to extract two mutually independent random numbers by using two separate mappings, where each mapping converts one of the output sequences from two correlated sources into a random number. In addition, we assume that the obtained pair of random numbers is also independent of the output sequence from the third source. We first show the δ-achievable rate region where a rate pair of two mappings must satisfy in order to obtain the approximation error within δ ∈ [0,1), and the second-order achievable rate region for correlated general sources. Then, we apply our results to non-mixed and mixed independently and identically distributed (i.i.d.) correlated sources, and reveal that the second-order achievable rate region for these sources can be represented in terms of the sum of normal distributions.

The effect of the hidden terminal (HT) over multi-hop cascaded wireless networks with the omni-directional full-duplex relays will cause data collision. We allocate the frequency band among different hops in an orthogonal way based on link grouping strategy to avoid this HT problem. In order to maximize the achievable rate, an optimal frequency allocation scheme is proposed by boundary alignment. Performance analyses are provided and further validated by the simulation results.

In this paper, we propose a construction of non-binary WOM (Write-Once-Memory) codes for WOM storages such as flash memories. The WOM codes discussed in this paper are fixed rate WOM codes where messages in a fixed alphabet of size M can be sequentially written in the WOM storage at least t*-times. In this paper, a WOM storage is modeled by a state transition graph. The proposed construction has the following two features. First, it includes a systematic method to determine the encoding regions in the state transition graph. Second, the proposed construction includes a labeling method for states by using integer programming. Several novel WOM codes for q level flash memories with 2 cells are constructed by the proposed construction. They achieve the worst numbers of writes t* that meet the known upper bound in the range 4≤q≤8, M=8. In addition, we constructed fixed rate non-binary WOM codes with the capability to reduce ICI (inter cell interference) of flash cells. One of the advantages of the proposed construction is its flexibility. It can be applied to various storage devices, to various dimensions (i.e, number of cells), and various kind of additional constraints.

The purpose of this study is to investigate the effects of device size on non-visual icon search using a touch interface with voice output. We conducted an experiment in which twelve participants searched for the target icons with four different-sized touchscreen devices. We analyzed the search time, search strategies and subjective evaluations. As a result, mobile devices with a screen size of 4.7 inches had the shortest search time and obtained the highest subjective evaluation among the four devices.

Streaming services that use TCP have increased; however, throughput is unstable due to congestion control caused by packet loss when TCP is used. Thus, TCP control to secure a required transmission rate for streaming communication using Forward Error Correction (FEC) technology (TCP-AFEC) has been proposed. TCP-AFEC can control the appropriate transmission rate according to network conditions using a combination of TCP congestion control and FEC. However, TCP-AFEC was not developed for wireless Local Area Network (LAN) environments; thus, it requires a certain time to set the appropriate redundancy and cannot obtain the required throughput. In this paper, we demonstrate the drawbacks of TCP-AFEC in wireless LAN environments. Then, we propose a redundancy setting method that can secure the required throughput for FEC, i.e., TCP-TFEC. Finally, we show that TCP-TFEC can secure more stable throughput than TCP-AFEC.

As financial products have grown in complexity and level of risk compounding in recent years, investors have come to find it difficult to assess investment risk. Furthermore, companies managing mutual funds are increasingly expected to perform risk control and thus prevent assumption of unforeseen risk by investors. A related revision to the investment fund legal system in Japan led to establishing what is known as “the rule for investment diversification” in December 2014, without a clear discussion of its expected effects on market price formation having taken place. In this paper, we therefore used an artificial market to investigate its effects on price formation in financial markets where investors follow the rule at the time of a market crash that is caused by the collapse of an asset fundamental price. As results, we found the possibility that when the fundamental price of one asset collapses and its market price also collapses, some asset market prices also fall, whereas other asset market prices rise for a market in which investors follow the rule for investment diversification.

In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.

In recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy — such as the diversity and novelty of the recommended items — are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyond-accuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness — factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

Malaysia is one of the countries with the highest car crash fatality rates in Asia. The high implementation cost of in-vehicle driver behavior warning system and autonomous driving remains a significant challenge. Motivated by the large number of simple yet effective inventions that benefitted many developing countries, this study presents the findings of emotion recognition based on skin conductance response using a low-cost wearable sensor. Emotions were evoked by presenting the proposed display stimulus and driving stimulator. Meaningful power spectral density was extracted from the filtered signal. Experimental protocols and frameworks were established to reduce the complexity of the emotion elicitation process. The proof of concept in this work demonstrated the high accuracy of two-class and multiclass emotion classification results. Significant differences of features were identified using statistical analysis. This work is one of the most easy-to-use protocols and frameworks, but has high potential to be used as biomarker in intelligent automobile, which helps prevent accidents and saves lives through its simplicity.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

In this paper, a multichannel speech enhancement system which adopts a denoising auto-encoder as part of the beamformer is proposed. The proposed structure of the generalized sidelobe canceller generates enhanced multi-channel signals, instead of merely one channel, to which the following denoising auto-encoder can be applied. Because the beamformer exploits spatial information and compensates for differences in the transfer functions of each channel, the proposed system is expected to resolve the difficulty of modelling relative transfer functions consisting of complex numbers which are hard to model with a denoising auto-encoder. As a result, the modelling capability of the denoising auto-encoder can concentrate on removing the artefacts caused by the beamformer. Unlike conventional beamformers, which combine these artefacts into one channel, they remain separated for each channel in the proposed method. As a result, the denoising auto-encoder can remove the artefacts by referring to other channels. Experimental results prove that the proposed structure is effective for the six-channel data in CHiME, as indicated by improvements in terms of speech enhancement and word error rate in automatic speech recognition.

In this paper, the Voronoi region of the transmitted codeword is employed to improve the sphere bound on the maximum-likelihood decoding (MLD) performance of binary linear block codes over additive white Gaussian noise (AWGN) channels. We obtain the improved sphere bounds both on the frame-error probability and the bit-error probability. With the framework of the sphere bound proposed by Kasami et al., we derive the conditional decoding error probability on the spheres by defining a subset of the Voronoi region of the transmitted codeword, since the Voronoi regions of a binary linear block code govern the decoding error probability analysis over AWGN channels. The proposed bound improves the sphere bound by Kasami et al. and the sphere bound by Herzberg and Poltyrev. The computational complexity of the proposed bound is similar to that of the sphere bound by Kasami et al.

A 197mW 70ms-latency Full-HD 12-channel video-processing SoC for in-vehicle information systems has been implemented in 16nm CMOS. The SoC integrates 17 video processors of 6 types to operate video processing independently of other processing in CPU/GPU. The synchronous scheme between the video processors achieves 70ms low-latency for driver assistance. The optimized implementation of lossy and lossless video-data compression reduces memory access data by half and power consumption by 20%.

This paper proposes a novel wavelet de-noising scheme regarding the existing burst noises that consist of background and impulsive noises in power-line communications. The proposed de-noising scheme employs multi-level threshold functions to efficiently and adaptively reduce the given burst noises. The experiment results show that the proposed de-noising scheme significantly outperformed the conventional schemes.

A fully integrated CMOS pulse transceiver with digital beam-formability for mm-wave active imaging is presented. The on-chip pulse transmitter of the transceiver includes an eight-element antenna array connected to eight pulse transmitters and a built-in relative pulse delay calibration system. The receiver employs a non-coherent detection method by using a FET direct-power detection circuit integrated with an antenna. The receiver dipole-patch antenna derives from the transmitter antenna but is modified with an on-chip DC-bias tail by shorting two arms of the dipole. The bandwidth of the receiver antenna with the DC-bias tail is designed to achieve 50.4-GHz in simulation and to cover the bandwidth of transmitter antennas. The output of the receiver antenna is connected to a resistive self-mixer followed by an on-chip low pass filter and then an amplifier stage. The built-in relative pulse delay calibration system is used to align the pulse delays of each transmitter array elements for the purpose of controlling the beam steering towards imaging objects. Both transmitter and receiver chips are fabricated in a 65-nm CMOS technology process. Measured pulse waveform of the receiver after relatively aligning all Tx's pulses is 0.91 mV (peak-peak) and 3-ns duration with a distance of 25mm between Rx and Tx. Beam steering angles are achieved in measurement by changing the digital delay code of antenna elements. Experimental results show that the proposed on-chip transceiver has an ability of digital transmitted-pulse calibration, controlling of beam-steeting, and pulse detection for active imaging applications.

Three-dimensional integrated circuits (3D ICs) that employ through-silicon vias (TSVs) integrating multiple dies vertically have opened up the potential of highly improved circuit designs. However, various types of TSV defects may occur during the assembly process, especially the clustered TSV faults because of the winding level of thinned wafer, the surface roughness and cleanness of silicon dies,inducing TSV yield reduction greatly. To tackle this fault clustering problem, router-based and ring-based TSV redundancy architectures were previously proposed. However, these schemes either require too much area overhead or have limited reparability to tolerant clustered TSV faults. Furthermore, the repairing lengths of these schemes are too long to be ignored, leading to additional delay overhead, which may cause timing violation. In this paper, we propose a region-based TSV redundancy design to achieve relatively high reparability as well as low additional delay overhead. Simulation results show that for a given number of TSVs (8*8) and TSV failure rate (1%), our design achieves 11.27% and 20.79% reduction of delay overhead as compared with router-based design and ring-based scheme, respectively. In addition, the reparability of our proposed scheme is much better than ring-based design by 30.84%, while it is close to that of the router-based scheme. More importantly, the overall TSV yield of our design achieves 99.88%, which is slightly higher than that of both router-based method (99.53%) and ring-based design (99.00%).

It has been reported that malicious third-party IC vendors often insert hardware Trojans into their IC products. How to detect them is a critical concern in IC design process. Machine-learning-based hardware-Trojan detection gives a strong solution to tackle this problem. Hardware-Trojan infected nets (or Trojan nets) in ICs must have particular Trojan-net features, which differ from those of normal nets. In order to classify all the nets in a netlist designed by third-party vendors into Trojan nets and normal ones by machine learning, we have to extract effective Trojan-net features from Trojan nets. In this paper, we first propose 51 Trojan-net features which describe well Trojan nets. After that, we pick up random forest as one of the best candidates for machine learning and optimize it to apply to hardware-Trojan detection. Based on the importance values obtained from the optimized random forest classifier, we extract the best set of 11 Trojan-net features out of the 51 features which can effectively classify the nets into Trojan ones and normal ones, maximizing the F-measures. By using the 11 Trojan-net features extracted, our optimized random forest classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several Trust-HUB benchmarks and obtained the average F-measure of 79.3% and the accuracy of 99.2%, which realize the best values among existing machine-learning-based hardware-Trojan detection methods.