Neural Coding of an Auditory Pitch Illusion

Pitch is an important perceptual dimension in audition, supporting auditory object segregation, melody recognition and lexical distinction. Huggins’ pitch, for example, is a phenomenon evoked by two sources of broadband noise presented binaurally with an inter-aural phase shift over a narrow frequency band. Huggins’ pitch and other dichotic pitches have been studied extensively using perceptual experiments. Several models have been proposed to explain and predict the perception of pitch; however, no studies have tried to record in vivo neuron responses to Huggins’ pitch (HP) nor have tried to explain how the HP is coded by neurons. The existence of pitches arising from the detection of binaural temporal cues may suggests that at least some of the “pitch neurons” involved must be linked to binaural unmasking: a phenomenon whereby binaural processing enhances the perceptual signal-to-noise ratio in noisy environments. To evaluate the neural coding of HP, in vivo recordings of chinchilla auditory nerve fibers (ANFs) and medial superior olivary (MSO) axons were made. Monoaurally and binaurally spike trains were gathered from ANFs and MSO axons respectively. Computational simulation using cross-correlation was used to predict the output of HP using ANFs as input and then it was compared to the recorded output (MSO). A decrease in the firing rate near the MSO neuron center frequency was found in the computation model and in the MSO neurons output. Therefore, by recording from single ANFs and single MSO fibers, we provide evidence for a de-correlation based neural coding of an auditory illusion: Huggins’ pitch

Electrochemo-magneto abrasive flow machine setup fabrication and experimental investigation of the process alongwith mathematical modeling and optimization

In abrasive flow machining, there are two sets of piston-cylinder arrangements, i.e. machine and media. the machine ram pushes the media piston two and fro so that media filled inside it flows past the inner wall of workpiece and the material is removed. The extrusion pressure is the main mechanism of material removal. Various authors have made the process more effective in terms of material removal and surface roughness by providing rotational and magnetic force

Tribological characterization of eco-designed aluminium hybrid metal matrix composites

47-57In present experimental investigation, wear characteristics of Al 7075-T6/Eggshell/SiC/Al2O3 hybrid composites (Al 7075-T6 as base metal with eggshell particles wt. % 0.5, 1and 1.5, average particle size ~ 60 µm, SiC particles wt. % 1, 1.5 and 2, average particle size ~ 65 µm and Al2O3 particles wt. % 1.5, 2 and 2.5, average particle size ~90 µm) synthesized through electromagnetic stir casting route have been studied at various specimen temperatures under dry and lubricated test conditions. Wear investigations have been conducted on pin-on-disk rotary tribometer at a constant load of 20 N for a sliding speed of 2m/s and sliding distance of 2 km. Tribological attributes of synthesized composites have been evaluated as the function of reinforcements content and mechanical stirring time, as per the design of experiment according to Taguchi L9 orthogonal array. Experimental study has shown that at 30 ºC pin temperature under dry wear condition, among the developed hybrid composites, specimen S8 demonstrated a maximum relative decrease of 60% in wear loss while with lubrication the wear loss has been relatively decreased by 89% as compared to the base metal (specimen S0). At elevated pin temperature of 70 ºC under dry wear condition, the hybrid composite specimen S8 exhibited maximum relative reduction of 82% in wear loss whereas under lubricated condition the wear loss has been relatively reduced by 82% in comparison of their unreinforced counterpart (specimen S0). With exceedingly augmented tribological attributes, the current study strongly rationalizes high temperature wear resistant applications of synthesized aluminium hybrid composites with a total reinforcement weight percentage of 4.5% only (specimen S8).

Optimization using genetic algorithm of tribological behaviour of WC tool material

In this investigation we have used a heuristic approach to optimize the process parameters in terms of tool wear rate. We have used the L8 orthogonal array design of experiments with three input parameters set at two levels. We have carried out the experimentation on two different processes viz. dry sliding and dry turning processes. An attempt has been made to achieve and validate the results obtained from these processes to check the repeatability of values in the same experimental environment. The tool material chosen for tool insert is Tungsten Carbide which is used in the manufacturing industries. We have optimised the results obtained on tribometer under the dry sliding process through a modern optimization technique i.e. genetic algorithm. The response surface methodology model (L8 orthogonal array) formed the basis for the development of genetic algorithm model through which we have defined the conditions. We have used the conditions of minimum tool wear for turning process, minimum coefficient of friction and minimum surface roughness for sliding process on a pin-on-disc test rig. It has been inferred that the sliding and turning processes under the conditions of no lubrication yielded analogous results. We have verified the same results practically by performing confirmation experiments on lathe machine for turning operation under the same experimental conditions

Optimization using genetic algorithm of tribological behaviour of WC tool material

889-896In this investigation we have used a heuristic approach to optimize the process parameters in terms of tool wear rate. We have used the L8 orthogonal array design of experiments with three input parameters set at two levels. We have carried out the experimentation on two different processes viz. dry sliding and dry turning processes. An attempt has been made to achieve and validate the results obtained from these processes to check the repeatability of values in the same experimental environment. The tool material chosen for tool insert is Tungsten Carbide which is used in the manufacturing industries. We have optimised the results obtained on tribometer under the dry sliding process through a modern optimization technique i.e. genetic algorithm. The response surface methodology model (L8 orthogonal array) formed the basis for the development of genetic algorithm model through which we have defined the conditions. We have used the conditions of minimum tool wear for turning process, minimum coefficient of friction and minimum surface roughness for sliding process on a pin-on-disc test rig. It has been inferred that the sliding and turning processes under the conditions of no lubrication yielded analogous results. We have verified the same results practically by performing confirmation experiments on lathe machine for turning operation under the same experimental conditions

Performance enhancements in powder-mixed near-dry electric discharge machining

Powder-mixed near-dry electric discharge machining (PMND-EDM) process have been attracting attention in the manufacturing community because of its capability to produce the end products with better surface quality characteristics and improved machining rate. Present work is an effort to understand the influence of process variables like tool diameter, flow rate of dielectric mist, concentration of metallic powder and mist pressure of dielectric medium. Taguchi method orthogonal array L9 was used for the conduction of experiments. Analysis of variance (ANOVA) was employed to find out the significance of process parameters. The results reveal that the optimum process parameters for the experimental condition for maximum MRR (0.11 mg s-1)was at tool diameter at A1 (2mm), flow rate of mist at B3 (15 ml min-1), concentration of metallic powder at C3 (25 gm l-1) and mist pressure of dielectric at D3 (0.6 MPa). It has been also concluded that metallic powder was responsible for surface modification to achieve higher micro-hardness value up to 530.10 Vickers hardness number along with refined surface characteristics

Machinability Characterization of Ecodesigned Hybrid Aluminium Composites

Machinability is expressed as the ease with which a material can undergo machining operations with gratifying surface finish and persistent material removal rate. In general, alumiAnium composites are observed to be difficult to machine due to infusion of hard reinforcement particles into metal matrix. In present study, machinability attributes of Al7075-T6/ Eggshell/SiC/Al2O3composites (Al7075-T6 as matrix material infused with three reinforcement materials: eggshell particles with average particle size ~ 60 µm; wt. % 0.5, 1and 1.5, Silicon Carbide particles with average particle size ~ 65 µm; wt. % 1, 1.5 and 2, and Aluminium Oxide particles with averageparticle size ~90 µm; wt. % 1.5, 2 and 2.5,) synthesized through electromagnetic stir casting route, have been investigated with fixed machining parameters (Cutting speed : 6 m/min, Depth of cut: 1mm, Feed rate: 0.3 mm/second and Test duration: 30 seconds). With enhanced mechanical attributes, the machinability of synthesized aluminium composites was realized to remain uninfluenced in terms of proportionate material removal rate (material removal rate of specimen S8: 0.0040g/sec and of specimen S0: 0.0043g/sec) and comparable surface roughness (average surface roughness of specimen S8: 1.02 μm and of specimen S0: 1.15μm). Disposal of eggshells has been listed worldwide as one of the worst environmental problems, hence eggshell powder has been used as one of the reinforcement in order to synthesize ecodesigned hybrid aluminium composites

Machinability Characterization of Ecodesigned Hybrid Aluminium Composites

252-257Machinability is expressed as the ease with which a material can undergo machining operations with gratifying surface finish and persistent material removal rate. In general, alumiAnium composites are observed to be difficult to machine due to infusion of hard reinforcement particles into metal matrix. In present study, machinability attributes of Al7075-T6/ Eggshell/SiC/Al2O3composites (Al7075-T6 as matrix material infused with three reinforcement materials: eggshell particles with average particle size ~ 60 µm; wt. % 0.5, 1and 1.5, Silicon Carbide particles with average particle size ~ 65 µm; wt. % 1, 1.5 and 2, and Aluminium Oxide particles with averageparticle size ~90 µm; wt. % 1.5, 2 and 2.5,) synthesized through electromagnetic stir casting route, have been investigated with fixed machining parameters (Cutting speed : 6 m/min, Depth of cut: 1mm, Feed rate: 0.3 mm/second and Test duration: 30 seconds). With enhanced mechanical attributes, the machinability of synthesized aluminium composites was realized to remain uninfluenced in terms of proportionate material removal rate (material removal rate of specimen S8: 0.0040g/sec and of specimen S0: 0.0043g/sec) and comparable surface roughness (average surface roughness of specimen S8: 1.02 μm and of specimen S0: 1.15μm). Disposal of eggshells has been listed worldwide as one of the worst environmental problems, hence eggshell powder has been used as one of the reinforcement in order to synthesize ecodesigned hybrid aluminium composites

Characterization of finished surface through thermal additive centrifugal abrasive flow machining for better surface integrity

29-44Abrasive Flow Machining (AFM) process has been a useful technique for deburring and polishing of the surface and edges through the abrasive laden media. The surface material has been removed in form of micro chips due to abrasion action of sharp cutting edges abrasive particles. A large amount of force and energy has been lost due to frictional forces between the surface and abrasive particles in AFM process. A new hybrid form of AFM process named as thermal additive centrifugal abrasive flow machining (TACAFM) has been discussed in the present investigation, which utilized the spark energy to melt the surface material. A lesser amount of force has been required by the abrasive particles to remove the molten material from the surface and also minimized the energy loss. In the present investigation central composite design response surface methodology has been used to plan and conduct the experiments using Design Expert® 11 software. Experiments have been performed to analyze the effect of input process variables such as current intensity, duty cycle, abrasive concentration, rotational speed of the electrode and extrusion pressure on scatter of surface roughness, micro hardness and % improvement in Ra of the workpiece. Also the finished surface of the brass work piece has been characterized for the microstructure study using SEM and XRD analysis. From the experimental results it has been found that duty cycle has the most significant effect towards Scatter of surface roughness with a contribution of 17.5 % while current has been contributed largest as 85.17 % towards micro hardness. Also it has been observed that current has contributed largest as 21.88% against the % improvement in Ra. The optimum scatter of surface roughness, micro hardness and % improvement in Ra has been observed as 0.15 μm, 345.95 HV and 39.52 % respectively

The Closed Loop Optimization of Deep Brain Stimulation Programming

Deep brain stimulation (DBS) is a procedure used to treat movement disorders such as Parkinson's disease. The current procedure for programming the parameters for DBS is time consuming and prone to error. The DBS programming procedure can be significantly improved using a closed-loop optimization approach. Due to recent advances in quantitative assessment metrics, the capability to translate a closed-loop optimization procedure for DBS programming from simulation to clinic has become more possible. Previous literature has presented closed-loop approaches that utilize evolutionary algorithms. It is very difficult to implement an evolutionary algorithm in the clinic because they typically require a large number of parameter evaluations. A parameter evaluation is testing how well a certain set of DBS parameters work. It is difficult to do a large number of parameter evaluations due to time constraints and patient fatigue. A response surface based closed-loop optimization approach for DBS programming is presented that has higher potential to be translated to the clinic because it requires much less parameter evaluations.Undergraduat