Abstract Recently, a mix of traditional and modern approaches have been proposed to detect brain abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the initial/scheduled assessments consider the bio‐signal‐based appraisal, due to its non‐invasive nature and low cost. Further, brain bio‐signal scans can be recorded using a single/multi‐channel electrode setup, which is further evaluated by an experienced doctor, as well as computer software, to identify the nature and severity of abnormality. In this paper, we describe the development of a system for computer supported detection (CSD) of schizophrenia using the electroencephalogram (EEG) signal collected with a 19‐channel electrode array. Schizophrenia is a mental illness that interferes with the way an individual thinks and behaves. It is characterised by psychotic symptoms such as hallucinations or delusions, negative symptoms such as decreased motivation or a lack of interest in daily activities and cognitive symptoms such challenges in processing information to make informed decisions or staying focused. This research has utilized 1142 EEGs (516 normal and 626 schizophrenia) with a frame length of 25 s (6250 samples) for investigation. The work initially converts the EEG signals to images using a spectrogram. Local configuration pattern features were extracted from the images thereafter, and 10‐fold validation technique was used wherein Student's t‐test and z‐score standardization were computed per fold. The highest accuracy of 97.20% was achieved with the K‐nearest neighbour (KNN) classifier. The results obtained confirm that the KNN classifier is helpful in the rapid detection of schizophrenia. This work is one of the first studies to extract local configuration pattern features from spectrogram images, yielding a high accuracy of 97.20%, with reduced computational complexity.

Abstract In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to theirp‐value, which was established with Student'st‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection.

Multi-carrier modulation has been widely used in wireless communications. Thus accurately detecting multi-carrier signal is of great importance to dynamic spectrum access and spectrum sharing. In multi-carrier modulation, a common approach for enabling channel estimation and amplitude/phase compensation is to insert pilots on some subcarriers. In this paper the pilot subcarriers are used for the detection of the signal. Two methods are proposed: the average pilot energy detection (APED) and joint time and frequency processing (JTFP). APED uses the property of boosted power at pilot locations in the signal power spectrum density, while JTFP uses the property of repetition of pilots in signal blocks. Frequency offset is a major obstacle to many detection methods. To handle this problem, we propose a low complexity method to compensate the composite frequency offset in time domain. We also use neighborhood search in frequency domain to handle the pilot location change due to the frequency offset. Furthermore, the methods are extended to multi-slot sensing, where the sensing duration is non-continuous and divided into multiple slots. Extensive simulations have been carried out and the results have shown that the JTFP can achieve superior performance without the requirement of time and frequency synchronization.

Dynamic spectrum access or cognitive radio is widely recognized as a major technology in the coming 5G communications to greatly increase spectral efficiency. In cognitive radio, different networks#x002F;systems may dynamically share some spectrum to achieve the maximum overall capacity. Each network#x002F;system may just use a few subcarriers or a few non-contiguous frequencies at a given time. It is very challenging to design the waveform to effectively use the fragmented spectrum with high flexibility for dynamical change. Filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM) are regarded as good candidates for the waveform at such situations. In this paper, we propose fast algorithms to implement the FBMC and GFDM when a number of cognitive radio users sharing the same spectrum. The major contributions are as follows. (1) We use the "sparse" property of the user's spectrum to reduce the sampling rate and complexity in the discrete time implementation; (2) We propose fast algorithms for computing the special Fourier transform, which reduce the complexity by nearly half.

In this paper we consider multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM)-based systems under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides a close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramér-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

Multiple antennas at the transmitter and the receiver can increase the channel capacity significantly. However, this is at the expense of linearly increasing circuit power consumption due to the use of multiple radio frequency (RF) chains to support the antennas, which is quite significant for large-scale MIMO systems but is largely ignored in the literature. Hence, in this paper, we assess the performance of a point-to-point large-scale MIMO channel considering the overall power consumption on both the transmitter and the receiver, and study the optimal RF chain configurations to maximize the transmission rate under such a total power constraint. Both configurations with and without the channel state information (CSI) of the channel matrix are considered. While the former requires the design of optimal selection of RF chains, the latter only needs to determine the optimal number of RF chains. We find through numerical results that the gain of configuration with CSI over that without CSI diminishes as the dimension of the channel gets large. We also provide guidelines for selecting the optimal number RF chains for configuration without CSI. In particular, for MISO case, we find that it is near-optimal to choose half of the maximum number of transmit RF chains, the circuits of which are affordable to be powered on by the total power budget.

In this paper we consider two-way relaying systems consisting of two end users, N1and N2, which exchange their information with the help of a relay, R. The three terminals are equipped with multiple antennas. The information exchange process between N1and N2are divided into two phases. Both N1and N2send their information to R at the first phase. The relay will amplify its received signals and transmit the resultant signals back to N1and N2at the second phase. The end users estimate the necessary channel coefficients to decode the signals; therefore, the processing burden on the relay is reduced. In this paper, a least-squared based estimation method is proposed to estimate those coefficients. Optimal pilot vectors are also proposed to minimize the mean-square-error (MSE) of channel estimation. Furthermore, the proposed training signals has the peak-to-average-power ratio (PAPR) of 1.

In this paper we consider an orthogonal frequency-division multiplexing (OFDM)-based system under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides an extremely close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramer-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

In this paper, we consider multi-input multi-output (MIMO) two-way relay networks with orthogonal frequency-division multiplexing (OFDM) modulation scheme. The networks consist of two end users exchanging their information via a relay. One information exchange between two end users is divided into two Phases. In Phase 1, two users send their information to the relay. The relay then amplifies and broadcasts its received signals to the two users in Phase 2, i.e., the relay works in the amplify-and-forward (AF) mode. By doing so, two-way relay networks require half as many time slots to accomplish one information exchange as the traditional one-way relay networks. We propose at each end user an iterative algorithm to jointly estimate the channel information and detect the data transmitted from the other user. The channel information consists of the \textit{composite} channels which are the combinations of individual channels in the networks and noise covariance matrices resulting from the AF working mode of the networks. We apply the Expectation Conditional Maximization (ECM) algorithm to estimate the necessary channel information for detection. Simulation results show that the performance of our proposed iterative algorithm is close to that given by the perfect channel information.

In this paper we consider a Multi-Input Multi-Output (MIMO) wireless relay network which comprises of a source terminal, a destination terminal and a relay. The transmission from the source terminal to the destination is divided into two phases. During Phase I, the source terminal sends its signals to the relay. The relay only amplifies and forwards its received signals to the destination terminal during Phase II, i.e., the network works in the Amplify-and-Forward (AF) mode. In this mode, the noise elements at the relay at Phase I are also amplified and forwarded through a link (from the relay to the destination terminal). It makes the overall noises across antennas of the destination terminal correlated. This correlation is characterized by a noise covariance matrix. Hence, for the purpose of data detection at the destination terminal, we need to estimate channel coefficients as well as the noise covariance matrix. In the considered network, an error correcting code (ECC) is used. We propose a doubly iterative receiver to estimate channel information and decode transmitted signals. The estimation part is an application of the Expectation Conditional Maximization (ECM) algorithm. Turbo-equalization is used to provide soft information of transmitted signals to the ECM-based algorithm. Simulation results show that our doubly iterative receiver provides an excellent BER performance.

In this letter, we consider a bi-directional relay network in which two users, U1and U2, exchange their information via a relay station, RS. Multiple antennas are deployed at both users and at RS. Single carrier cyclic prefix (SCCP) is used to combat intersymbol interference (ISI) in frequency-selective fading channels. The transmission process is divided into two time slots. At the first time slot, both users send their information to RS concurrently. RS then amplifies and broadcasts its received signals at the second time slot. We propose an algorithm to estimate the channel information at end users based on the least square (LS) principle. To further minimize the mean square error (MSE) of the estimate, a method to design the optimal training sequences is also proposed. Simulation results show that the performance achieved by our optimal design is close to that with perfect channel information.

In this paper, we propose an iterative receiver for a multi-input multi-output (MIMO) two-way wireless relay system. The two-way relay system requires two time slots to accomplish one information exchange. Compared to the traditional one-way relay system, which demands four time slots to complete one information exchange, our system only uses half as many time slots. Our proposed iterative receiver consists of two loops. One is the minimum mean square error (MMSE)-based iterative soft interference cancellation (SIC) algorithm which is extended from the algorithm in to cover correlated noise environment incurred from the nature of signalling. The other is the Expectation Conditional Maximization (ECM)-based estimation algorithm. The MMSE-SIC algorithm provides the soft information of the transmitted signals and this information is transferred to the ECM-based estimation algorithm to update the channel information. Simulation results show that the performance of the iterative receiver proposed here is close to that given by perfect channel information with small number of iterations.

In this letter, the problem of training sequence design in a bi-directional relay network is investigated. The two nodes, Tx1 and Tx2, communicate with each other via a relay (RL). The RL receives the signals that are transmitted simultaneously from both nodes and then broadcasts the superposition back to the two nodes. Single carrier cyclic prefix (SCCP) is deployed in the transmission from the two nodes and the RL as well for combating intersymbol interference (ISI). We are interested in the channel estimation problem in this scenario. We propose a design of training sequences from two nodes to minimize the mean-square error (MSE) of the channel estimation according to zero forcing (ZF) criterion. We do not separate the links between each node to RL but incorporate them into the composite channels. Simulation results show that our design performance suffers only 1-dB loss in terms of bit-error rate (BER) as compared to the case with perfect channel information.

In this paper, we are interested in the problem of joint channel estimation and data detection for multi-input multi-output (MIMO) interleaved frequency division multiple access (IFDMA) systems. Although IFDMA is free from the multiple access interference (MAI), it suffers from intersymbol interference (ISI). MIMO-IFDMA system suffers from both ISI and multi-stream interference (MSI). The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancelation scheme which could provide near maximum likelihood (ML) performance with very low complexity. However, BI-GDFE needs the channel state information (CSI). In this paper, we utilize the soft estimates of the transmitted symbols provided by the BI-GDFE to estimate the channel via an Expectation Maximization (EM)-based algorithm. By doing so, a joint channel estimation and data detection receiver is developed. To evaluate the performance of the proposed channel estimation algorithm, we derive the Cramér-Rao Lower Bound (CRLB). Computer simulations show that the bit error rate (BER) performance of the proposed joint channel estimation and signal detection receiver can reach the performance of the BI-GDFE with perfect CSI.

Cyclic-prefix code division multiple access (CP-CDMA), multicarrier CDMA (MC-CDMA) and single carrier cyclic-prefix (SCCP) transmission are some schemes that could support the increasing demand of future high data rate applications. The linear and nonlinear equalizers used to detect the transmitted signal are always far from the maximum-likelihood (ML) detection bound. The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancelation scheme which could provide near-ML performance yet with very low complexity. In order to deploy this scheme, the channel state information (CSI) must be available at the receiver. In practice, this information has to be estimated by using pilot and data symbols. This paper investigates the problem of channel estimation using the expectation maximization (EM) algorithm. The BI-GDFE provides the soft information of the transmitted signals to the EM-based algorithm in the form a combination of hard decision and a coefficient so-called the input-decision correlation (IDC). The resultant receiver becomes a doubly iterative scheme. To evaluate the performance of the proposed estimation algorithm, the Cramer-Rao lower bound (CRLB) is also derived. Computer simulations show that the bit error rate (BER) performance of the proposed receiver for joint channel estimation and signal detection can reach the performance of the BI-GDFE with perfect CSI.

In this paper, the problem of training sequence design in a bi-directional relay network is investigated. The two nodes Txl and Tx2 communicate with each other via a relay (RL). The RL receives the signals that are transmitted simultaneously from both nodes and then broadcasts the superposition back to the two nodes. By doing so, bi-directional relaying reduces half the channel resources required by the conventional relaying. In frequency-selective fading channels, single carrier cyclic prefic (SCCP) is efficient in combatting intersymbol interference (ISI). Hence, in this paper, SCCP is deployed in the transmission from the two nodes and the RL as well. We are interested in the channel estimation problem in this scenario. We propose a design of training sequences from two nodes to minimize the mean square error (MSE) of the channel estimation according to zero forcing (ZF) criterion. We do not separate the links between each node to RL but corporate them into composite channel. Simulation results show that our design performance suffers only 1 dB loss in terms of bit-error-rate (BER) as compared to the case with perfect channel information case.

In this paper, we consider the problem of joint channel and frequency offset estimation, data detection in a flat-fading multi-input multi-output (MIMO) system. We assume that each pair of transmit and receive antennas has a different frequency offset value. We present a computationally efficient iterative algorithm based on the space-alternating generalized expectation-maximization (SAGE) and the expectation conditional maximization (ECM) algorithms.

The joint channel estimation and data detection problem for multi-input multi-output single carrier cyclic-prefix (MIMO-SCCP) systems is considered in this paper. The detection process is completed by Block-Iterative generalized decision feedback equalizer (BI-GDFE) method. The BI-GDFE method provides the soft information of the transmitted signals to the expectation- maximization (EM)-based channel estimation. The Cramer-Rao lower bound (CRLB) is also derived to evaluate the quality of estimated parameters. Simulations show that the proposed iterative structure has the bir-error-rate (BER) performance close to the that of the BI-GDFE method with perfect channel state information (CSI).

In this paper, we consider the problem of joint channel and frequency offset estimation in a flat-fading multi- input multi-output (MIMO) system. We assume that each pair of transmit and receive antennas has a different frequency offset. We present two computationally efficient iterative algorithms based on expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) algorithms. The mean-square-error (MSE) performance of the interested parameters for the proposed algorithms is compared with the Cramer-Rao Bound (CRB). Simulation results show that the proposed iterative algorithms achieve the CRB and overcome the drawbacks of existing algorithms.

In this letter, we consider the problem of joint channel estimation and data detection for single-input multi-output (SIMO) systems under fast-fading environment with unknown spatially correlated noise. We present a computationally efficient iterative receiver based on the expectation conditional maximization (ECM) algorithm. Bit error rate (BER) performance of the proposed iterative receiver is simulated and compared with that of the maximum likelihood (ML) receiver with perfect channel state information (CSI). Simulation results show that the proposed iterative receiver achieves nearly the performance of ML receiver with perfect CSI and requires only a few iterations.

Single carrier cyclic-prefix (SCCP) transmission is the scheme that can support the increasing demand of nowadays high data rate applications. The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancellation scheme which has near-ML performance yet with very low complexity. In order to deploy this scheme, the channel state information (CSI) must be available at the receiver. In this paper, the problem of channel estimation using the expectation maximization (EM) algorithm is investigated. The BI-GDFE provides the soft information of the transmitted signal for the EM-based algorithm. The resulted receiver becomes a doubly iterative scheme. To evaluate the performance of the proposed estimation algorithm, the Cramer-Rao lower bound (CRLB) is also derived. Computer simulations show that the bit error rate (BER) performance of the proposed receiver can reach the performance of BI-GDFE with perfect CSI.

The single-input multi-output (SIMO) channel model is widely used in data transmission and diversity receptions. In this paper, we present three computationally efficient iterative receivers for joint channel estimation and data detection in SIMO systems. These receivers result from an application of the expectation-maximization (EM) and the space-alternating generalized expectation-maximization (SAGE) algorithms. Bit error rate (BER) performance of the Maximum Likelihood (ML) receivers with perfect channel state information (CSI) is plotted and compared with the BER performance of the proposed iterative receivers. Proposed receivers exhibit near ML performance with a few iterations.

In this paper, an iterative interference cancellation (IIC) scheme is proposed for space-time block coded (STBC) multirate multiuser systems. The proposed IIC scheme is based on minimum mean square estimation (MMSE) interference cancellation and maximum likelihood detection. In the proposed scheme, the high rate user data is decoded first and the decoded high rate user data is used to decode the low rate user data. Subsequently, low rate user data is used to re-decode the high rate user data. The bit error rate performance of the proposed IIC is better than the conventional non-iterative MMSE scheme. In addition, the proposed scheme performs well not only in multirate multiuser systems but also in equal rate multiuser systems. As expected, the performance margin diminishes with number of iterations.