Studies of functional variability in the compound eyes of flies reveal superior temporal resolution of photoreceptors from the frontal areas that mediate binocular vision, and in males mate recognition and pursuit. However, the mechanisms underlying differences in performance are not known. Here, we investigated properties of hover fly Volucella pellucens photoreceptors from two regions of the retina, the frontal-dorsal “love spot” and the lateral one. Morphologically, the microvilli of the frontal-dorsal photoreceptors were relatively few in number per rhabdomere cross-section, short and narrow. In electrophysiological experiments involving stimulation with prolonged white-noise and natural time intensity series, frontal-dorsal photoreceptors demonstrated comparatively high corner frequencies and information rates. Investigation of possible mechanisms responsible for their superior performance revealed significant differences in the properties of quantum bumps, and, unexpectedly, relatively high absolute sensitivity of the frontal-dorsal photoreceptors. Analysis of light adaptation indicated that photoreceptors from two regions adapt similarly but because frontal-dorsal photoreceptors were depolarized much stronger by the same stimuli than the lateral photoreceptors, they reached a deeper state of adaptation associated with higher corner frequencies of light response. Recordings from the photoreceptor axons were characterized by spike-like events that can significantly expand the frequency response range. Seamless integration of spikes into the graded voltage responses was enabled by light adaptation mechanisms that accelerate kinetics and decrease duration of depolarizing light response transients.

As essential specifications of correlation domain for signal quality evaluation, distortions of the S-curve, including bias and slope distortions of the zero-crossing point, are usually selected as indicators of optimisation in the process of designing the channels of receivers or navigation satellites. Focusing on this issue, we present a detailed analysis of slope distortion in the presence of group delay and amplitude distortions. After validating the theoretical results, we present further discussions about the impacts of different group delay terms on slope distortions. The results indicate that both the odd-order and the even-order terms have impacts on the slope distortion, and higher odd-order terms have less slope distortion compared with the lower odd-order terms. These results are useful for evaluating the slope distortion from the group delay and guiding improvement in design of the channel.

A spoofing attack on a global navigation satellite system (GNSS) receiver is a threat to a significant community of GNSS users due to the high stakes involved. This paper investigates the use of slope based metrics for the detection of spoofing. The formulation of slope based metrics involves monitoring correlators along with tracking correlators in the receiver's channel, which are slaved to the prompt tracking correlator. In this study, using some candidate metrics, detectors have been formed through the analysis of simulated spoofing attacks. A theoretical variance of each metric has also been calculated as a reference for the threshold. A threshold is estimated using the measured variance from the clean signals, for specific false alarm rate. By using the measured threshold, detectors are formed based on slope metrics. These detectors have been tested using TEXBAT data. The results show that the differential slope metrics have good performance. The results have also been compared with some other techniques of spoofing detection.

Subacute ruminal acidosis (SARA) is usually characterized by abnormal and intermittent drops in rumen pH. Nevertheless, high individual animal variability in rumen pH and the difference in measurement methods for pH data acquisition decrease the sensitivity and accuracy of pH indicators for detecting SARA in ruminants. The aim of this study was to refine rumen pH indicators in long-term SARA based on individual dairy cow reticulo-rumen pH kinetics. Animal performances and rumen parameters were studied weekly in order to validate SARA syndrome and rumen pH was continuously measured using reticulo-rumen sensors. In total, 11 primiparous dairy cows were consecutively fed two different diets for 12 successive weeks: a control diet as low-starch diet (LSD; 13% starch for 4 weeks in period 1), an acidotic diet as high-starch diet (HSD; 32% starch for 4 weeks in period 2), and again the LSD diet (3 weeks in period 3). There was a 1-week dietary transition between LSD and HSD. Commonly used absolute SARA pH indicators such as daily average, area under the curve (AUC) and time spent below pH<5.8 and pH<6 were processed from absolute (raw) daily kinetics. Then signal processing was applied to raw pH values in order to calculate relative pH indicators by filtering and normalizing data to remove inter-individual variability, sensor drift and sensor noise. Normalized AUC, times spent below NpH<−0.3 and NpH<−0.5, NpH range and NpH standard deviation were calculated. Those relative pH indicators were compared with commonly used pH indicators to assess their ability to detect SARA. This syndrome induced by HSD was confirmed by consistent expected changes in milk quality, dry matter intake and acetate : propionate ratio in the rumen, whereas the ruminal concentration of lipopolysaccharide was increased. Commonly used pH SARA indicators were not able to discriminate SARA syndrome due to high animal variability and sensor drift and noise, whereas relative pH indicators developed in this study appeared more relevant for SARA detection as assessed by receiver operating characteristic tests. This work shows that absolute pH kinetics should be corrected for drift, noise and animal variability to produce relative pH indicators that are more robust for SARA detection. These relative pH indicators could be more relevant for identifying affected animals in a herd and also for comparing SARA risk among studies.

An X-ray pulsar/starlight Doppler deeply-integrated navigation method is proposed in this paper. A starlight Doppler measurement-aided phase propagation model, which can remove the orbital effect within the recorded photon Time Of Arrivals (TOAs), is derived, and guarantees that the pulse phase can be extracted from the converted photon TOAs using computationally efficient methods. Some simulations are performed by a hardware-in-loop system to verify the performance of the integrated pulse phase estimation method as well as of the integrated navigation method. The integrated pulse phase estimation method could achieve an estimation performance similar to the existing method for orbiting vehicles at the cost of much less computational complexity, is capable of handling the signals of millisecond pulsars, and is applicable to various vehicles. In addition, the proposed integrated navigation method could provide reliable positioning results for various vehicles.

The increasing demand for high-data rate communications in the connected world imposes various challenges in analog and radio frequency (RF) circuits. Although continued scaling in advanced processes offers faster devices, it is accompanied by increasing complexity in circuit design and layout strategy, resulting in diminishing benefits for analog/RF circuits. In order to enable new breakthroughs in speed, cost, and power efficiency, simplifying analog/RF circuits with the assistance of signal processing is becoming a clear trend. This paper provides an overview of this trend by reviewing the signal processing algorithms commonly deployed in wireless communications, data converters, and wired data links. The discussion covers design considerations, as well as algorithms used to compensate for circuit imperfections, so as to demonstrate the cross-discipline interactions between signal processing and analog/RF circuit design.

A pulse phase estimation of an X-ray pulsar with the aid of vehicle orbital dynamics is proposed. The original continue-time X-ray pulsar signal model is modified to be a term of vehicle position and velocity varying with time, and a modified definition of pulse time of arrival is given. The modified signal model is further linearized around the predicted position and velocity of the vehicle to the second order. The initial phase and the coefficients of the extended signal model can be estimated by maximum likelihood estimator. Some simulations are performed to verify the method and show the method has robustness to the initial error within initial state of the vehicle and is capable of handling the phase-estimation problem for pulsars with low fluxes.

The study of arm muscles for independent operations leading to prosthetic design was carried out. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for different arm motions and then repeated factorial analysis of variance (ANOVA) technique was implemented to analyze effectiveness of signal. The electronic design consisted of analog and digital signal processing and controlling circuit and mechanical assembly consisted of wrist, palm and the fingers to grip the object in addition to a screw arrangement connected to a low power DC motor and gear assembly to open or close the hand. The wrist is mechanically rotated to orient the hand in a direction suitable to pick up/hold the object. The entire set up is placed in a casing which provides a cosmetic appeal to the artificial hand and the connected arm. The design criteria include electronic control, reliability, light weight, variable grip force with ease of attachment for simple operations like opening, grasping and lifting objects of different weight with grip force slightly more than enough just like that of a natural hand.

This work deals with the application of the fuzzy logic to automate diagnosis of bearing defects in rotating machines based on vibration signals. The classification tool used is a fuzzy inference system (FIS) of Mamdani type. The vector form of input contains parameters extracted from the signals collected from the test bench studied. The output vector contains the classes for the different operating modes of the experimental study. The results show that; pretreatment data (filtering, decimation,...), the choice of parameters of fuzzy inference system (input variables and output, types and parameters of membership functions associated with different input and output variables of the system, the generation of fuzzy inference rules,...) are of major importance for the performance of fuzzy inference system used as a tool for fault diagnosis of rotating machinery.

The E. & F. White Conference held in Sydney in December 1999 brought together expertise on a range of interference mitigation techniques from CSIRO, Australian and international industry and universities. Key goals were to enhance the understanding of techniques and their inter-relationship, to increase awareness of advanced technologies such as software radios and photonics, and to foster a cooperative approach to the development of interference mitigation techniques. The foremost application in mind was the square kilometre array (SKA) and the need to find ways to develop a hierarchical scheme for removing unwanted signals from astronomical data. This paper gives an overview of the topics discussed at the conference and summarises some of the key ideas and results that were presented.

Angle of arrival (AOA), time of arrival (TOA), and frequency of arrival (FOA) can be measured for a signal from multiple platforms. By combining such measurements it is possible to obtain high-accuracy emitter position estimates. This requires a data link with low latency and sufficient data-rate, and synchronization of the platforms in space, time, and search pattern. Typically several of the platforms will have to make their measurements in the radar sidelobes that require very high receiver sensitivity. This paper focuses on discussing how the sensitivity can be improved using antenna gain or signal processing.

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.