Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been developing rapidly and is widely employed in the study of brain cognition due to its non-invasive and non-radiation characteristics. We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT) to improve the results of ADHD classification. Firstly, combining the advantages of convolutional neural networks (CNNs) and the denoising autoencoder (DAE), we developed a convolutional denoising autoencoder to extract the spatial features of fMRI data and obtain spatial features sorted by time. Then, AdaDT was exploited to classify the features extracted by CDAE. Finally, we validate the algorithm on the ADHD-200 test dataset. The experimental results show that our method offers improved classification compared with state-of-the-art methods in terms of the average accuracy of each individual site and all sites, meanwhile, our algorithm can maintain a certain balance between specificity and sensitivity.

Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been developing rapidly and is widely employed in the study of brain cognition due to its non-invasive and non-radiation characteristics. We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT) to improve the results of ADHD classification. Firstly, combining the advantages of convolutional neural networks (CNNs) and the denoising autoencoder (DAE), we developed a convolutional denoising autoencoder to extract the spatial features of fMRI data and obtain spatial features sorted by time. Then, AdaDT was exploited to classify the features extracted by CDAE. Finally, we validate the algorithm on the ADHD-200 test dataset. The experimental results show that our method offers improved classification compared with state-of-the-art methods in terms of the average accuracy of each individual site and all sites, meanwhile, our algorithm can maintain a certain balance between specificity and sensitivity.

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There has been a continuously growing interest in radio frequency identification (RFID) and more recently, in computational RFID, i.e. battery-less sensors that piggyback sensed information, rather than a constant ID bit stream, utilizing Gen2, the physical layer of commercial RFID systems. This work offers a complete software-defined radio (SDR) reader with 1) coherent detection without rate degradation, by exploiting already present preambles in Gen2; 2) full exploitation of FM0 line coding memory in Gen2 tags; 3) careful handling of symbol synchronization and departure of commercial tags from nominal bit duration; and 4) implementation and testing of Gen2 in a commodity SDR, utilizing a single transceiver card. Continuing recent work, this contribution offers an updated prototyping tool that could further unlock the potential of computational RFID and relevant battery-less sensor networks.

This work derives and evaluates single-antenna detection schemes for collided radio frequency identification (RFID) signals, i.e. simultaneous transmission of two RFID tags, following FM0 (biphase-space) encoding. In sharp contrast to prior art, the proposed detection algorithms take explicitly into account the FM0 encoding characteristics, including its inherent memory. The detection algorithms are derived when error at either or only one out of two tags is considered. It is shown that careful design of one-bit-memory two-tag detection can improve bit-error-rate (BER) performance by 3dB, compared to its memoryless counterpart, on par with existing art for single-tag detection. Furthermore, this work calculates the total tag population inventory delay, i.e. how much time is saved when two-tag detection is utilized, as opposed to conventional, single-tag methods. It is found that two-tag detection could lead to significant inventory time reduction (in some cases on the order of 40%) for basic framed-Aloha access schemes. Analytic calculation of inventory time is confirmed by simulation. This work could augment detection software of existing commercial RFID readers, including single-antenna portable versions, without major modification of their RF front ends.

Although deep brain stimulation (DBS) has been a promising alternative for treating several neural disorders, the mechanisms underlying the DBS remain not fully understood. As rat models provide the advantage of recording and stimulating different disease-related regions simultaneously, this paper proposes a battery-less, implantable neuro-electronic interface suitable for studying DBS mechanisms with a freely-moving rat. The neuro-electronic interface mainly consists of a microsystem able to interact with eight different brain regions bi-directionally and simultaneously. To minimize the size of the implant, the microsystem receives power and transmits data through a single coil. In addition, particular attention is paid to the capability of recording neural activities right after each stimulation, so as to acquire information on how stimulations modulate neural activities. The microsystem has been fabricated with the standard 0.18 μm CMOS technology. The chip area is 7.74 mm 2 , and the microsystem is able to operate with a single supply voltage of 1 V. The wireless interface allows a maximum power of 10 mW to be transmitted together with either uplink or downlink data at a rate of 2 Mbps or 100 kbps, respectively. The input referred noise of recording amplifiers is 1.16 μVrms, and the stimulation voltage is tunable from 1.5 V to 4.5 V with 5-bit resolution. After the electrical functionality of the microsystem is tested, the capability of the microsystem to interface with rat brain is further examined and compared with conventional instruments. All experimental results are presented and discussed in this paper.

This paper presents a digital neural/EMG telemetry system small enough and lightweight enough to permit recording from insects in flight. It has a measured flight package mass of only 38 mg. This system includes a single-chip telemetry integrated circuit (IC) employing RF power harvesting for battery-free operation, with communication via modulated backscatter in the UHF (902-928 MHz) band. An on-chip 11-bit ADC digitizes 10 neural channels with a sampling rate of 26.1 kSps and 4 EMG channels at 1.63 kSps, and telemeters this data wirelessly to a base station. The companion base station transceiver includes an RF transmitter of +36 dBm (4 W) output power to wirelessly power the telemetry IC, and a digital receiver with a sensitivity of -70 dBm for 10 -5 BER at 5.0 Mbps to receive the data stream from the telemetry IC. The telemetry chip was fabricated in a commercial 0.35 μ m 4M1P (4 metal, 1 poly) CMOS process. The die measures 2.36 × 1.88 mm, is 250 μm thick, and is wire bonded into a flex circuit assembly measuring 4.6 × 6.8 mm.

Wireless brain-computer interfaces (BCIs) used for fundamental neuroscience research in freely moving non-human primates (NHPs) require communication systems capable of transferring large volumes of recorded neural data while consuming minimal power. We introduce a 6.25 Mbps differential quadrature phase-shift keying (DQPSK) backscatter wireless uplink for the NeuroDisc BCI, operating in the 902-928 MHz industrial, scientific, and medical (ISM)-band. The backscatter uplink consumes 77.5 μW (only 0.06% of the system power budget), yielding a communication energy efficiency of 12.4 pJ/bit, while the measured error vector magnitude of the DQPSK constellation is 9.69%. The neural recording front-end has a measured input-referred noise of 2.35 μVrms at a maximum sampling rate of 20 kSps. We present end-to-end recording and wireless uplink validation with pre-recorded neural data as well as in vivo recordings from a pigtail macaque.

The need for a miniaturized device that can perform closed-loop operation is imminent with the growing interest in brain-controlled devices and in stimulation to treat neural disorders. This work presents the Neural Closed-Loop Implantable Platform (NeuralCLIP), a modular FPGA-based device that can record neural signals, process them locally to detect an event and trigger neural stimulation based on the detection. Specifically, the NeuralCLIP is designed to record and process different neural signals in the frequency range between 20 Hz and 1 kHz. It is a flexible platform that can be reconfigured to optimize parameters like channel count and operation frequency based on the processing requirements. The signal-agnostic feature is demonstrated by testing the device with calibration signals from standard bio-signal emulators. The application focus for this device is a brain-computer-spinal interface (BCSI) which is demonstrated based on local field potential (LFP) signals recorded from a rat motor cortex. This work demonstrates recording and on-device processing of LFP signals to decode action intent and determine stimulation timing. The FPGA implementation of the device also targets development of low power algorithms for closed-loop operation.

The properties of 1.55 mum up-conversion single-photon detector are analyzed using the differential-phase-shift keying (DPSK) protocol. We compare the error rate and the communication rate as a function of distance for three quantum key distribution QKD protocols: the Bennett-Brassard 1984, the Bennett-Brassard-Mermin 1992, and the DPSK. We show that the significant advantage of the up-conversion detector than the commonly used InGaAs/InP avalanche photodiode (APD), and the properties of QKD can be greatly improved by using this detector.

We analyze the performance of a recently proposed multichip differential phase-shift-keying (DPSK) format over the nonlinear fiber-optic channel. For a single wavelength nonlinear phase-noise-limited channel, a multichip DPSK receiver based on a three-chip observation can attain more than two orders of magnitude bit-error-rate reduction relative to a standard DPSK receiver, or equivalently /spl sim/1-dB improvement in Q-factor, significantly exceeding the 0.2-dB improvement achieved by the same format over a linear optical channel.

The impact of self-phase modulation-induced nonlinear phase noise in a 10-Gb/s return-to-zero differential phase-shift keying system is studied by numerical simulation. We show that the simple differential phase Q method based on the Gaussian approximation for the phase noise provides a relatively good estimate of the nonlinear penalty.

The authors extend multicanonical sampling to the determination of probability density functions in communications systems. The resulting easily programmed iterative procedure generates the probability of both frequent and rare events with equivalent accuracy. The applicability of the technique is verified through a calculation of the differential group delay probability distribution in a multisection polarization emulator.

Recently introduced differential phase-shift keying (DPSK) extensions, collectively referred to here as multilevel differential phase (MDP) formats, explore an increase in data throughput for a given bandwidth by effectively multiplexing differential phase encoding and amplitude modulation onto the same fiber link. In this letter, we derive and present analytic models for the quantum limits of bit-error rate for leading MDP modulation formats (binary phase differential phase amplitude-shift keying (DPASK), quaternary phase DPASK), comparing their performance to that of conventional systems.

An alternative modulation technique, called multi-ring DPSK (MR-DPSK), is proposed. The proposed technique can achieve lower variation of the transmitted power and less hardware complexity than can differentially encoded amplitude-and phase-shift keying (DAPSK). It is, thus, more suitable for mobile applications due to its features of lower power dissipation, lower error probability and lower hardware complexity.

Polybinary, optical amplitude modulated phase shift keying (AM-PSK) polybinary, M-ary amplitude shift keying (ASK), and polyquaternary signaling schemes operating at 10 Gb/s are investigated in 1550-nm lightwave systems operating over standard, single-mode fiber. The premise for exploring these signal types is that they concentrate power at frequencies closer to the optical carrier where phase distortion of the optical field from chromatic dispersion is less severe. Issues such as modulator chirp, optimal level spacing in a 4-ary ASK signal, and phase modulated to amplitude modulated (PM-AM) noise conversion from a nonzero laser linewidth are studied. It is found that higher order polybinary signals do not offer an improvement in dispersion tolerance over a duobinary signal. 4-ary ASK is demonstrated to increase the dispersion-limited distance to 225 km experimentally and 350 km through simulation, but at the expense of a /spl sim/8 dB degradation in receiver sensitivity due to the increased number of levels and the signal dependence of signal-spontaneous beat noise. Furthermore, the linewidth requirement for a 4-ary ASK signal is less than 1 MHz in order to minimize the impact of PM-AM relative intensity noise (RIN) when transmitting over 200-300 km.

We introduce a new multichip differential phase-shift keying (MC-DPSK) family of advanced optical modulation formats, whereby differential phase encoding is simultaneously applied to multiple (>2) consecutive time slots (chips). Four-chip quaternary phase MC-DPSK attains the same spectral efficiency (SE) as differential quaternary phase-shift keying while improving the quantum limit sensitivity by 1.4 dB [22.4 versus 31 photons per bit at 10/sup -9/ bit-error rate (BER)]. A novel coded version of four-chip four-phase MC-DPSK using 16 codewords forming a maximally unbiased base attains 33% better SE than differential binary phase-shift keying, reaching the lowest quantum limit sensitivity to date (14.6 versus 20 photons per bit at 10/sup -9/ BER).

A new optical modulation format is described, based on the simultaneous modulation of the amplitude and the phase of an optical signal. The proposed modulation format offers significant advantages in wavelength-division-multiplexing transmission, offering high spectral efficiency values, high extinction ratio, and requiring electronics with reduced bandwidth.

We report an experimental investigation of transmission and transparent wavelength conversion properties of a two-level optically labeled signal using amplitude-shift-keying/differential-phase-shift-keying orthogonal modulation. Error-free transmission of a 10-Gb/s payload and 2.5-Gb/s label over 80-km nonzero dispersion-shifted fiber is achieved with less than 1-dB power penalty. Transparent wavelength label swapping based on four-wave mixing in a highly nonlinear fiber is also demonstrated, clearly validating this orthogonal modulation scheme as a potential solution for optical labeling.

Assigning a wavelength label as well as a label in a DPSK modulation format orthogonal to the data payload significantly increases the forwarding and routing capabilities of optical packet routers in IP-over-WDM networks.

Nonlinear crosstalk in the quaternary differential-phase amplitude-shift-keying (QDPASK) modulation format is analyzed. Significant crosstalk penalty is measured on a QDPASK signal with a 20-Gb/s aggregate capacity for nonlinear phase shifts of 0.17/spl pi/ rad and above. Two different compensation techniques are demonstrated based on either prechirping or postchirping of the optical signal, increasing the nonlinear tolerance by 6.1 and 4.5 dB, respectively.