This paper presents a hippocampal cognitive prosthesis chip designed for restoring the ability to form new long-term memories due to hippocampal system damage. The system-on-chip (SOC) consists of a 16-channel micro-power low-noise amplifier (LNA), high-pass filters, analog-digital converters (ADCs), a 16-channel spike-sorter, a generalized Laguerre-Volterra model multi-input, multi-output (GLVM-MIMO) hippocampal processor, an 8-channel neural stimulator and peripheral circuits. The proposed LNA achieved a voltage gain of 50dB, input-referred noise of 3.95µVrms, and noise efficiency factor (NEF) of 3.45 with the power consumption of 3.3µW. High-pass filters with a 300-Hz bandwidth are used to filter out the unwanted local field potential (LFP). 4 12-bit successive approximation register (SAR) ADCs with a signal-to-noise-and-distortion ratio (SNDR) of 63.37dB are designed for the digitization of the neural signals. A 16-channel spike-sorter has been integrated in the chip enabling a detection accuracy of 98.3% and a classification accuracy of 93.4% with power consumption of 19µW/ch. The MIMO hippocampal model processor predict output spatio-temporal patterns in CA1 according to the recorded input spatio-temporal patterns in CA3. The neural stimulator performs bipolar, symmetrical charge-balanced stimulation with a maximum current of 310µA, triggered by the processor output. The chip has been fabricated in 40nm standard CMOS technology, occupying a silicon area of 3mm2.

The pervasive application of Small Private Online Course (SPOC) provides a powerful impetus for the reform of higher education. During the teaching process, a teacher needs to understand the difficulty of SPOC videos for students in real time to be more focused on the difficulties and key points of the course in a flipped classroom. However, existing educational data mining techniques pay little attention to the SPOC video difficulty clustering or classification. In this paper, we propose an approach to cluster SPOC videos based on the difficulty using video-watching data in a SPOC. Specifically, a bipartite graph that expresses the learning relationship between students and videos is constructed based on the number of video-watching times. Then, the SimRank++ algorithm is used to measure the similarity of the difficulty between any two videos. Finally, the spectral clustering algorithm is used to implement the video clustering based on the obtained similarity of difficulty. Experiments on a real data set in a SPOC show that the proposed approach has better clustering accuracy than other existing ones. This approach facilitates teachers learn about the overall difficulty of a SPOC video for students in real time, and therefore knowledge points can be explained more effectively in a flipped classroom.

In this paper, a Proof-of-Concept (PoC) architecture is constructed, and the effectiveness of mmWave overlay heterogeneous network (HetNet) with mesh backhaul utilizing route-multiplexing and Multi-access Edge Computing (MEC) utilizing prefetching algorithm is verified by measuring the throughput and the download time of real contents. The architecture can cope with the intensive mobile data traffic since data delivery utilizes multiple backhaul routes based on the mesh topology, i.e. route-multiplexing mechanism. On the other hand, MEC deploys the network edge contents requested in advance by nearby User Equipment (UE) based on pre-registered context information such as location, destination, demand application, etc. to the network edge, which is called prefetching algorithm. Therefore, mmWave access can be fully exploited even with capacity-limited backhaul networks by introducing the proposed algorithm. These technologies solve the problems in conventional mmWave HetNet to reduce mobile data traffic on backhaul networks to cloud networks. In addition, the proposed architecture is realized by introducing wireless Software Defined Network (SDN) and Network Function Virtualization (NFV). In our architecture, the network is dynamically controlled via wide-coverage microwave band links by which UE's context information is collected for optimizing the network resources and controlling network infrastructures to establish backhaul routes and MEC servers. In this paper, we develop the hardware equipment and middleware systems, and introduce these algorithms which are used as a driver of IEEE802.11ad and open source software. For 5G and beyond, the architecture integrated in mmWave backhaul, MEC and SDN/NFV will support some scenarios and use cases.

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.

This letter proposes performance evaluation of phase-only correlation (POC) functions using signal-to-noise ratio (SNR) and peak-to-correlation energy (PCE). We derive the general expressions of SNR and PCE of the POC functions as correlation performance measures. SNR is expressed by simple fractional function of circular variance. PCE is simply given by squared peak value of the POC functions, and its expectation can be expressed in terms of circular variance.

This article describes the frequency response and the applications of the optical electric-field sensor consisting of a 1 mm1 mm1 mm CdTe crystal mounted on the tip of an optical fiber, which theoretically possesses the potential to cover the frequency band from below megahertz to terahertz. We utilize a capacitor, GTEM-Cell, and standard gain horn antennas for applying a free-space electric field to the optical sensor at frequencies from 20 kHz to 1 GHz, from 1 GHz to 18 GHz, and from 10 to 180 GHz, respectively. An electric-field measurement demonstrates its flat frequency response within a 6-dB range from 20 kHz to 50 GHz except for the resonance due to the piezo-electric effect at a frequency around 1 MHz. The sensitivity increases due to the resonance of the radio frequency wave propagating in the crystal at the frequencies higher than 50 GHz. These experimental results demonstrate that the optical electric-field sensor is a superior tool for the wide-band measurement which is impossible with conventional sensors such as a dipole, a loop, and a horn antenna. In transient electrostatic discharge measurements, electric-field mapping, and near-field antenna measurements, the optical electric-field sensor provide the useful information for the deterioration diagnosis and the lifetime prognosis of electric circuits and devices. These applications of the optical electric-field sensor are regarded as promising ways for sowing the seeds of evolution in electric-field measurements for antenna measurement, EMC, and EMI.

A projection onto convex sets (POCS)-based annotation method for semantic image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image from its known visual features based on a POCS algorithm, which includes two novel approaches. First, the proposed method semantically assigns database images some clusters and introduces a nonlinear eigenspace of visual and semantic features in each cluster into the constraint of the POCS algorithm. This approach accurately provides semantic features for each cluster by using its visual features in the least squares sense. Furthermore, the proposed method monitors the error converged by the POCS algorithm in order to select the optimal cluster including the query image. By introducing the above two approaches into the POCS algorithm, the unknown semantic features of the query image are successfully estimated from its known visual features. Consequently, similar images can be easily retrieved from the database based on the obtained semantic features. Experimental results verify the effectiveness of the proposed method for semantic image retrieval.

Subjects' episodic memory performance is not simply reflected by eye movements. We use a 'theta phase coding' model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

This paper includes different approaches for analysis of a thin-wire antenna in the presence of de-ionized water box at different temperatures as a high-permittivity three-dimensional dielectric body. In continuation with the previous work of authors, first, the coupled tensor-volume/line integral equations is solved by using Galerkin-based moment method (MoM) consisting of a combination of entire-domain and sub-domain basis functions including three-dimensional polynomials with different degrees. Then, the accuracy of such MoM, specifically for a high-permittivity dielectric scatterer, is substantiated by comparing its numerical results with that of FDTD method and some experimental data.

A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

In this paper, we describe the effect of p-regions on the I-V kink in GaAs FETs. A kink-free p-pocket-type self-aligned gate GaAs MESFET (PP-MESFET), which does not include p-regions under the channel, has been analyzed and compared with a conventional buried-p-type self-aligned gate GaAs MESFET (BP-MESFET) using two-dimensional device simulation. The relation between the I-V kink and the layout of p-regions has been demonstrated by numerical simulation for the first time. For both the BP-MESFET and PP-MESFET, impact ionization produces holes in high-field regions. The holes accumulate under the channel, widen the channel, and cause an abrupt increase in drain current in turn in the BP-MESFET. On the other hand, in the PP-MESFET, holes generated in the high-field region are transported to the source region easily over the lower barrier owing to the absence of p-regions under the channel. Holes do not accumulate under the channel, leading to kink-free I-V characteristics of the PP-MESFET. P-regions should be located so as not to cause the accumulation of holes in GaAs FETs where p-regions are required for high-frequency performance.

Based on the theory of block projection onto convex sets (BPOCS), a novel de-blocking algorithm is proposed. A new smoothness constraint set (SCS) is used to remove the unnecessary high frequencies. In addition, an adaptive quantization constraint set (AQCS) is employed to suppress error in the smoothing process. The proposed size and position of new SCS are different from traditional ones. Extensive experimental results are provided to demonstrate that the proposed method can achieve better image quality with fewer iterations.

This paper presents a new type of optical probe designed to detect magnetic near-fields with high accuracy in the gigahertz range. Its probe head consists of a loop antenna element doubly loaded with LiNbO3 electro-optic crystals. Through an optical technique, it can work as a conventional double-loaded loop probe without metallic cables or an electrical hybrid junction. We examined probe characteristics for magnetic field detection up to 20 GHz. We confirmed that the probe can measure magnetic fields near a microstrip line in the gigahertz range and can suppress influence of electric fields.

A novel class of time-varying subband transforms and its application in image coding are introduced. We construct the framework in which an analysis subband transform is chosen from a given set of analysis transforms and perfect reconstruction is achieved. To this end, we suggest the reconstruction method from the transformed coefficients by applying the theory of convex projections (POCS). We introduce convex sets for perfect reconstruction from the transformed vector. We further propose other convex sets which assure smoothness of plane regions for image coding applications. We show several image coding examples. The proposed coding method is an extension of conventional one with multiple block-based transforms. At each time instance (block), the transform to be applied is chosen from a given set of orthogonal subband transforms according to a certain criterion. Experimental results show that the use of multiple subband transforms leads to the improvement of coding performance compared to the use of single transform even though there exists side information.

Small-size MOSFETs are becoming core devices in RF applications because of improved high frequency characteristics. For reliable design of RF integrated circuits operating at the GHz range, accurate modeling of small-size MOSFET characteristics is indispensable. In MOSFETs with reduced gate length (Lg), the lateral field along the MOSFET channel is becoming more pronounced, causing short-channel effects. These effects should be included in the device modeling used for circuit simulation. In this work, we investigated the effects of the field gradient in the gate-drain capacitance (Cgd). 2-Dimensional (2D) simulations done with MEDICI show that the field gradient, as it influences the channel condition, induces a capacitance which is visible in the MOSFET saturation operation. Changes in Cgd is incorporated in the modeling by an induced capacitance approach. The new approach has been successfully implemented in the surface-potential based model HiSIM (Hiroshima-university STARC IGFET Model) and is capable of reproducing accurately the measured Cgd-Lg characteristics, which are particularly significant for pocket-implant technology. Results show that pocket-implantation introduces a steep potential increase near the drain region, which results to a shift of the Cgd transition region (from linear to saturation) to lower bias voltages. Cgd at saturation decreases with Lg due to steeper surface potential and increased impurity concentration effects at reduced Lg.

We studied the possibility of making an objective diagnosis of dementia based on radiological findings by evaluating cerebral and hippocampal atrophy and the corpus callosum shape on MRI images in patients with Alzheimer-type dementia, compared with healthy elderly individuals. There was a statistically significant difference in the hippocampus area index, the ventricle area index, and the area ratio for the second, forth, and fifth parts of corpus callosum. Discriminant analysis using these three parameters demonstrated the sensitivity of 88.5% and the specificity of 85.7%, suggesting a highly positive diagnostic rate. These results indicate that quantitative MRI measurements could be used for differentiating Alzheimer-type dementia from similar diseases.

High-speed and low-power DSPs have been developed for versatile applications, especially for digital communications. These DSPs contain a 16-bit fixed point DSP core with multiple buses, highly tuned instruction set and low-power architecture, featuring 0.45 mA/MIPS, 100-120 MIPS performance by a single CPU core, 200 MIPS performance by dual CPU core architecture, respectively and also contain a 1.2 V low-voltage DSP core with 30 MIPS performance for super low-power applications. In this paper, new architecture VIA2 programming ROM for high-speed and new D flip-flop circuit considering the impact of pocket implantation process for low power are discussed, including key C-MOS process technology.

The hippocampus is thought to play an important role in the transformation from short-term memory into long-term memory, which is called consolidation. The physiological phenomenon of synaptic change called LTP or LTD has been studied as a basic mechanism for learning and memory. The neural network mechanism of the consolidation, however, is not clarified yet. The authors' approach is to construct information processing theory in learning and memory, which can explain the physiological data and behavioral data. This paper proposes a dynamical hippocampal model which can store and recall spatial input patterns. The authors assume that the primary functions of hippocampus are to store episodic information of sensory signals and to keep them for a while until the neocortex stores them as a long-term memory. On the basis of the hippocampal architecture and hypothetical synaptic dynamics of LTP/LTD, the authors construct a hippocampal model. This model considers: (1) divergent connections, (2) the synaptic dynamics of LTP and LTD based on pre- and postsynaptic coincidence, and (3) propagation of LTD. Computer simulations show that this model can store and recall its input spatial pattern by self-organizing closed activating pathways. By the backward propagation of LTD, the synaptic pathway for a specific spatial input pattern can be selected among the divergent closed connections. In addition, the output pattern also suggests that this model is sensitive to the temporal timing of input signals. This timing sensitivity suggests the applicability to spatio-temporal input patterns of this model. Future extensions of this model are also discussed.

A new optical wavelength demultiplexer using quasi-phase-matched sum-frequency-generation (QPM-SFG) is proposed. The device consists of an optical deflector using Pockels effect and a nonlinear crystal with a periodic structure. The demultiplexing characteristics of the device composed of a LiNbO3 crystal are analyzed theoretically. Wavelength demultiplexing can be made simply by changes in the electric field applied to the deflector.

This paper describes a novel image reconstruction algorithm and experimental results of a 3-dimensional acoustical holographic imaging system which has a limited number of transducers distributed sparsely. The proposed algorithm is based on the conjugate gradient projection onto convex sets (CGPOCS), which allows the addition of convex sets constrained by a priori information to reduce ambiguity and extract resolution iteratively. By several experiments, it is proven that the concept of the new 3-D acoustic image reconstruction algorithm has following improvements:1. the artifacts caused by the spurious lobes can be reduced under the condition that the inter-spacing of elements is larger than the wave length,2. the instability caused by the lack of information about the actual point spread function (PSF) can be reduced,3. the actual PSF can be estimated concurrently with during the image reconstruction process.