Updating analysis of key performance indicators of 4G LTE network with the prediction of missing values of critical network parameters based on experimental data from a dense urban environment.

In practice, field measurements often show missing data due to several dynamic factors. However, the complete data about a given environment is key to characterizing the radio features of the terrain for a high quality of service. In order to address this problem, field data were collected from a dense urban environment, and the missing parameters were predicted using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) algorithm. The field measurement was taken around Victoria Island and Ikoyi in Lagos, Nigeria. The test equipment comprises a Global Positioning System (GPS) and a Fourth Generation (4G) Long Term Evolution (LTE) modem equipped with a 2×2 MIMO antenna, employing 64 Quadrature Amplitude Modulation (QAM). The Modem was installed on a personal computer and assembled inside a test vehicle driven at a near-constant speed of 30 km/h to minimize possible Doppler effects. Specifically, the test equipment records 67 LTE parameters at 1 s intervals, including the time and coordinates of the mobile station. Thirty-two parameters were logged at 42,498 instances corresponding to 11 h, 48 min and 18 s of data logging on the mobile terminal. Sixteen important 4G LTE parameters were extracted and analyzed. The statistical errors were calculated when the missing values were exempted from the analyses and when the missing values were incorporated using the PCHIP algorithm. In particular, this update paper estimated the missing values of critical network parameters using the PCHIP algorithm, which was not covered in the original article. Also, the error statistics between the data (histograms) and the corresponding probability density function curves for the measured data with missing values and the data filled with the missing values using the PCHIP algorithm are derived. Additionally, the accuracy of the PCHIP algorithm was analysed using standard statistical error analysis. More network parameters have been tested in the update article than in the original article, presenting only basic statistics and fewer network parameters. Overall, results indicate that only the parameters which measure the throughput values follow the half-normal distribution while others follow the normal distribution.

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects' data has been created. In this paper, gesture accuracies under different sampling frequencies and channel's number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.

A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX).

Motor Unit Number Index (MUNIX) is a technique that provides a susceptive biomarker for monitoring innervation conditions in patients with neurodegenerative diseases. A satisfactory repeatability is essential for the interpretation of MUNIX results. This study aims to examine the effect of channel number and location on the repeatability of MUNIX. In this study, 128 channels of high-density surface electromyography (EMG) signals were recorded from the biceps brachii muscles of eight healthy participants, at 10, 20, 30, 40, 50, 60, 70, 80, and 100% of maximal voluntary contraction. The repeatability was defined by the coefficient of variation (CV) of MUNIX estimated from three experiment trials. Single-channel MUNIX (sMUNIX) was calculated on a channel-specific basis and a multi-channel MUNIX (mMUNIX) approach as the weighted average of multiple sMUNIX results. Results have shown (1) significantly improved repeatability with the proposed mMUNIX approach; (2) a higher variability of sMUNIX when the recording channel is positioned away from the innervation zone. Our results have demonstrated that (1) increasing the number of EMG channels and (2) placing recording channels close to the innervation zone (IZ) are effective methods to improve the repeatability of MUNIX. This study investigated two potential causes of MUNIX variations and provided novel perspectives to improve the repeatability, using high-density surface EMG. The mMUNIX technique proposed can serve as a promising tool for reliable neurodegeneration evaluation.

How do the reference montage and electrodes setup affect the measured scalp EEG potentials?

OBJECTIVE: Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. APPROACH: First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five monopolar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (Linked Mastoids (LM), Average Reference (AR) and Reference Electrode Standardization Technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. MAIN RESULTS: Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. SIGNIFICANCE: These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.